- Email: [email protected]
A bidirectional congestion control transport protocol for the internet of drones
Journal Pre-proof A bidirectional congestion control transport protocol for the internet of drones Bhisham Sharma, Gautam Srivastava, Jerry Chun-Wei Lin
PII: DOI: Reference:
S0140-3664(19)31546-4 https://doi.org/10.1016/j.comcom.2020.01.072 COMCOM 6193
To appear in:
Computer Communications
Received date : 30 October 2019 Revised date : 5 January 2020 Accepted date : 29 January 2020 Please cite this article as: B. Sharma, G. Srivastava and J.C.-W. Lin, A bidirectional congestion control transport protocol for the internet of drones, Computer Communications (2020), doi: https://doi.org/10.1016/j.comcom.2020.01.072. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
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A Bidirectional Congestion Control Transport Protocol for the Internet of Drones Bhisham Sharmaa,∗, Gautam Srivastavab,c,∗, Jerry Chun-Wei Lind a Chitkara
University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India. Email: [email protected] b Department of Mathematics and Computer Science, Brandon University, Brandon, MB, CANADA c Research Centre for Interneural Computing, China Medical University, Taichung, TAIWAN d Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway. Email: [email protected]
Abstract
Wireless sensor networks (WSNs) are composed of energy constrained devices that autonomously form networks through which sensed information is trans-
ported from the region of interest to the central control station (sink), integration with unmanned aerial vehicles (UAVs) leads to enlarged monitoring area
and to enhance overall network performance. Due to application specific nature of wireless sensor networks, it is challenging to design a congestion control
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protocol that is suitable for all types of applications in the Internet of Drones (IoD). Congestion avoidance and control in wireless sensor networks mainly
aims at reducing packet drop due to congestion and maintaining fair bandwidth allocation to all network flows. In this research work, we propose a reliable and congestion based protocol, which provides both bidirectional reliability and rate adjustment based congestion control. It uses Technique for Order Prefer-
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ence by Similarity to Ideal Solution (TOPSIS) method to select optimal path for data transmission because TOPSIS selects an alternative such that it has shortest distance from the ideal one and greatest distance from negative ideal ∗ Corresponding
author Email address: [email protected],[email protected] (Gautam Srivastava)
Preprint submitted to Journal of LATEX Templates
January 30, 2020
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solution. Congestion is detected by using proportion of average packet service time over average packet inter-arrival time as congestion degree. Then, congestion is notified using implicit notification to save energy and reduce overhead.
To mitigate congestion along with maintaining fairness, an equal priority index is assigned to all data sources and when congestion occurs, rate adjustment
to optimal value based on priority value is used for congestion control. This
approach helps to diminish packet drops, maintain fairness and get better energy efficiency. Finally, we compare the performance of the proposed protocol
with that of existing protocols. Our simulation results show reduced average
delivery overhead, drop packet ratio, queue length and delay with increased average delivery ratio. Moreover, our protocol provides better energy efficiency and fairness when compared with the existing competing protocols.
Keywords: Wireless sensor networks; Transport layer protocols; Internet of Drones; sensor nodes; motes; TOPSIS; Energy Efficiency; Fairness; UAV
1. Introduction
Wireless Sensor Networks (WSNs) have constraints like limited energy and limited resources. In past few years, due to innovation in radio technologies and
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low-power circuit, WSNs have emerged and have achieved enormous popularity. A WSN is comprised of loads of self-organizing, lightweight sensor motes and being used for a wide range of monitoring applications. Each sensor mote is composed of one processing unit, a storage unit, a transceiver, limited battery power with multiple sensors on board. Due to small size and expensiveness of sensor motes, they have limited energy, limited processing power, limited memory and other resources. The mobile sink node shares the data with the sensor
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nodes establish on its fixed or dynamically calculated navigation route. This technique decreases the energy consumption but indicates a substantial problem by restricting the mobile collector to flat navigable surfaces. To overwhelmed this drawback, the mobile sink node can be carried by an unmanned aerial vehi-
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cles (UAV), commonly known by their everyday name, drones. Any protocol for
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[23, 31, 4, 24].
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WSNs should be designed by considering the limits of sensor motes cautiously
WSN-UAV are having enormous applications like military area monitoring,
smart home monitoring, air-pollution monitoring, target tracking and health20
care etc. The UAV network can undertake the part of mobile base stations when required, in order to regroup the sensor nodes located within the sensing
area, into WSN clusters. Additionally, smarter dimension systems driven by
UAVs embed additional processing at the point of procurement, taking valu-
able critical data that in the hands of the right people, can decode to improved 25
and quicker data-driven results. Wireless Sensor Network have emerged as re-
search areas with a great effect on practical application developments. WSNs are having enormous applications like military area monitoring, smart home
monitoring, air-pollution monitoring, target tracking and healthcare etc. Many WSN applications require reliable data transport for time-critical dynamic ap30
plications with no tolerance for data loss such as transfer of instructions from the sink to sensor motes in order to reprogram them to execute a task different
from the one they are performing currently or change the detection parameters
[7]. It is an important task to collect the data periodically from various sensor
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nodes for monitoring and recording the physical conditions of the environment.
The sensed data must be received and transmitted between the nodes in the network. In such cases sensor motes get in rather problematic situations, and creating connection with different sensor motes is not easy. Therefore, positive WSN deployment based on together the hardware characteristic and the network self-organization protocols which are used. Such applications have zero tolerance for data loss like if any part of instruction is dropped, then sensor
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mote will not be able to interpret the instruction correctly. Further in WSN, data flow can be upstream (sensor-to-sink) and downstream (sink-to-sensor). In upstream data flow, all sensor motes send their sensed data to base station and in downstream flow, sink may send control information or query messages
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to sensor motes. So, reliability is required in both upstream and downstream directions for efficient working of WSN. Due to limited power resources and fre3
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quent node failures, providing reliability and quality of service in wireless sensor networks can be challenging [3, 27, 35].
The Internet of Drones (IoD) is a layered network control architecture which 50
has been designed predominantly for the coordination of access of UAVs (drones) to airspace that are controlled. Moreover, IoD provided services for navigating between areas referred to as nodes in a WSN. IoD can also provide more general services for a variety of drone applications. Some examples include • package delivery
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• traffic surveillance • search and rescue
• emergency situation remote coverage
For applications, where sensor nodes send sensed data of a specific area to one or more base stations, traffic load is less in the senor network most of the 60
time. But, when an attention-grabbing event occurs, such as enemy intrusion,
the sensor motes will produce and send out a sudden massive amount of data. During these conditions, congestion occurs when a sensor mote accepts extra
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data than it can forward and redundant data has to be buffered. Thus, the restricted buffer space of a node is filled and results in loss of packets (new 65
or old). Due to this, energy and communication resources of sensor nodes are wasted and also packet loss results in reduction of the event detection reliability [25].
The problem of congestion in WSNs is quite different from traditional net-
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works. Protocols used for wired networks like TCP are not suitable for WSNs. 70
Congestion in WSNs has following negative impact on performance like throughput, which is decreased drastically and per-packet energy consumption is increased [11, 43]. Hence, congestion in WSN must be addressed effectively to prolong the network lifetime and congestion control can save energy of nodes and reduce the delay by adjusting the transmitting rates, or by using alternative
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resources [6, 26].
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Currently, many schemes and algorithms have been proposed for congestion control and avoidance, as well as for reliable data transmission in WSNs. In the
great majority of congestion control algorithms, authors use either traffic control or resource control approach. By using traffic control, they choose to control the 80
rates with which sources inject packets in the networks and by using resource
control, they choose alternative resources to route congested traffic. On the other hand, reliable data transmission approaches usually run at the transport layer. Some of them are end-to-end and employ re-transmissions using ACK or NACKs [9]. 85
Energy efficiency is an important aspect of a WSN because it is repeatedly not possible to interchange or revitalize batteries of sensor nodes. Due to this constraint, it is very critical that a protocol be energy-efficient in order
to prolong the network lifetime and allow more data to be collected. In order to diminish the effects of congestion near the base station, an energy-efficient 90
protocol that can handle multiple simultaneous events without losing reliability is needed [44].
WSNs that include UAVs can be converted into applications that provides particular advantages such as: (1) On-site data gathering and examination (2)
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UAVs can provides ground WSN clustering method by electing the cluster heads
(3) UAVs can make available mapping information. It is also used to support ground WSN deployment. This provides the flexibility of flying UAVs to any place in the area of interest. In WSNs, multi-hop forwarding scheme is used to forward data due to limited communication range of a sensor node [21]. Therefore, when congestion occurs, sensor motes that are closer to sink will be able to send more data to sink as compared to sensor motes that are far-off from
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the sink. So, this leads to unfairness towards far away data sources and reduces reliability. Consequently, fairness must be provided among all the sensor motes. The main focus in this paper is to determine the behavior of several network
parameters like traffic load, node density and their impact to congestion in
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WSNs and present design and analysis of bidirectional reliable and rate adjustment based congestion control transport protocol that is different from existing 5
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traditional protocols. Some other issues such as energy consumption and fairness problems and their relation to congestion are also examined.
The remaining of this paper is systematized as follows. Brief summary of ex110
isting transport layer protocols is defined in Section 2. In Section 3, bidirectional
reliable and congestion control transport protocol (BRCCTP) is proposed. The comparative analysis of both Asymmetric and Reliable Transport (ART) and
BRCCTP protocols has been discussed in Section 4. Lastly, in Section 5 paper is concluded.
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2. Existing Transport Layer Protocols
Different transport layer protocols have been proposed to locate numerous
problems for example packet loss recovery, reliability, congestion control, energy efficiency, flow control, and heterogeneous application support in wireless sensor
networks. A brief detail of the associated protocols is explained in this section. 120
In [13], the authors proposed the congestion and communication channel
occupation in WSN by examine the states that results in congestion of the communication network by determining the subsequent nodes configuration con-
straints: (1) data packet generation rate, (2) transmission time intervals, and
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(3) transmitter output control level. From the graphics presented by the au-
thors shows that around is a saturation level, and higher than this level the connection is no longer linear. The degree of this level is start from 20 number of data packets per second to 60 number of data packets per second and if data transmission is higher than this degree, the communication channel will not transmit data successfully, and also delay will increase. In [30, 8], the authors proposed the adaptive cuckoo search based optimal
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rate adjustment (ACSRO) for the congestion prevention and mitigation in WSN. The realization of the proposed protocol is calculated by using various performance parameters for example latency, throughput, congestion level, normalized packet loss, and normalized queue size. The position of the congestion is dy-
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namic at the sensor node, which implies that the ACSRO performs the action
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and removes congestion in the system. It uses the rate optimization approach
to control the congestion by decreasing the rate of the child nodes in the sensor network.
In [20, 28], the authors deal with congestion avoidance and interference reg140
ulation in WSN by developing the method named IACC (Interference-Aware
Congestion Control). This method provides maximum bandwidth size consump-
tion for each node through efficient rate control among the interfering nodes. IACC is based on two phases verification. In first phase, the overall bandwidth
capacity among every node and its parent node is projected and further forward145
ing projected dimensions to the base-station. In second phase, the scheduling
scheme is used to guarantee the fairness though mitigating congestion in the network.
In [2], the authors proposed dynamic rate control on source node in transport layer protocol for WSN. They established a dynamic source rate based adjust150
ment control algorithm that implements a cache-aware method where cache
management strategies are active to inform the comparative amount of packet
losses and set boundary limit for transmission window size. This rate control method accomplished 30% rise in cache consumption although sustaining a ra-
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tional portion of bandwidth distribution among child nodes which did not lower than ERCTP and did not improve than static DTC and DTSN + protocols.
In [10], the authors developed an energy efficient conditional multi-path and multi-copy routing technique. It consists of two novel data transmission schemes, viz. CM and CMrest along with consistent mathematical programming models. It also includes two legal dissimilarities in order to decrease the resolution periods for mathematical prototypes with profitable solvers. Authors
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also define a heuristic method, that can implement together for the CM and CM
rest
. Both of these solution techniques, precise model through legal dif-
ferences and the heuristic technique, can be implemented to resolve CM and CMrest depending on the balance among quality and solution time in a specific
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application.
In [12], The degree of congestion of the child node is calculated by determin7
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ing buffer occupancy ratio and further sending this data to the current node.
Rate of transmission is adjusted by the current node to mitigate the difficulty
of congestion and increasing the throughput of network with multi-classification 170
gained through Support Vector Machines (SVMs). Simulations results of author
express that the finest tool for data classification is SVM. Moreover, results validate that the proposed scheme requires less energy consumption and increased throughput.
In [19], the authors proposed Reliable, Efficient, Fair and Interference-Aware 175
Congestion Control (REFIACC) protocol for WSNs. This protocol avoids the
interference and guarantees an increased fairness of bandwidth consumption by planning the communications among sensor nodes. Linear programming is
currently used to achieve optimal consumption proficiency of the determined
existing bandwidth. It has been implemented in TOSSIM simulator and related 180
with two existing works. In REFIACC congestion control is established on physical dimensions of radio links size.
In [29, 41], the authors proposed a Hierarchical Energy Efficient Reliable
Transport Protocol (HEERTP) for the data communication in WSNs. It is
transport layer protocol based on cluster-based selection that reduces energy consumption by decreasing repetitive data transportation over wireless sensor
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networks. It minimizes the broadcast of detected repetitive data with the direction of the base station. It makes an order of groups comprising of motes for data collection inside the network. One node in the network is selected as the cluster head (CH). It is accountable for gathering the data from the sensor 190
nodes. It forwards the gathered data to the CHs and then to the base station.
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The CH is nominated on the foundation of remaining energy of the sensor motes and coordinate position. In [22, 1, 34], the authors defined congestion control method called packet
priority intimation (PPI). It includes PPI bit in every packet to highlight its
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status. The key benefit of this method is to send highest priority packets with least latency. The congestion is mitigated and the data traffic is maintained in an effective way through low and high priority array. This technique is useful 8
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not only in single health care, but also in numerous wireless sensor network applications. It is extended and improved from First Come First Serve (FCFS) 200
scheme and can increase the efficiency in relations to latency, packet drop rate, energy efficiency, and throughput.
In [36], the authors proposed protocol for multicast communication and retransmission based reliable error control to make it explicitly for data centric networks. It functions in three phases: first phase is communication setup in 205
which the multicast subtrees are formed and the suitable bloom filters are dis-
persed. Second phase is primary content distribution in which the producer
directs the full information and assembles collective feedback. Third phase is retrieval where single or multiple cycles of originating re-transmissions and gathering novel combined response, till entire subscribers have finished the transfer 210
positively.
In [39], the authors proposed an effective redundancy coding-based data communication method for wireless sensor networks (RECODAN), which assures re-
liable data communication in hop-by-hop manner over hybrid re-transmission. It makes use of selective Reed-Solomon coding to decrease the procedure of en215
coding in an intermediary node. To increase data reliability, it makes use of an
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adaptive codeword re-transmission. It also uses the Ornstein-Uhlenbeck (OU)
method to determine the packet receiving rate and reduce data redundancy in code delivery as well as increasing the network lifetime. Simulation experiments were conducted in MATLAB and outcomes demonstrate that RECODAN can 220
considerably increase the data communication reliability and reduce the delay
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and energy consumption.
3. Bidirectional Reliable and Congestion Control Transport Protocol In this section, we first evaluate the functioning of an existing protocol Asym-
metric and Reliable Transport (ART) and from the literature studied it has been
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found that only ART provides bidirectional reliability that is required for the application wherever a user node wants to re-task a cluster of sensor motes in
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its locality by inserting program images into target nodes and where the loss of packets is not tolerated. Now we define the drawbacks of current protocol and afterwards we deliver the resolution to those drawbacks in our recommended 230
Bidirectional Reliable and Congestion Control Transport Protocol (BRCCTP) protocol for WSN.
3.1. Analysis of a Prevailing Transport Protocol
An ART mechanism for WSN is a transport protocol that delivers bidirec-
tional reliability for reliable event and query communication in wireless senor 235
networks. ART implement an event-driven data delivery model to transmit data
from sensors to the sink. Sensors transfer data only if they sense an event. If an event is sensed in the time of an update interval, a sensor reports the event
to the sink by transmitting consecutive messages. Authors uses the parameter
event-reporting frequency to modify how often a sensor node transfer event re240
ports when phenomenon is in its sensing range. On the other hand, the sink
makes use of a continuous data delivery model, by transporting periodic queries to the sensors. Likewise, ART uses query-reporting frequency, as a simulation parameter to sustain traffic load in downstream direction. It handles with only
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a subgroup of the huge number of sensor nodes accordingly letting a decrease
in complexity in cases of loss message recovery. To acclimatize to the essential characteristics of upstream (sensors-to-sink) and downstream (sink-to-sensors) traffic, authors proposed an asymmetric method, making use of ACK and NACK methods. Reliability necessities for both sink-to-sensor and sensor-to-sink data transfer that is addressed by existing protocol [14, 15, 42, 5]. It achieves these properties using energy aware classification of sensors and using NACK and
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ACK based mechanisms. In ART authors proposed an energy-aware sensor classification algorithm to create a network topology that is consists of sensors in providing required level of bidirectional reliability. Firstly, authors proposed an energy-aware node classification algorithm, which is a weighted-greedy algo-
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rithm to find a set of sensors, named as essential nodes. For this requirement, weight function is well-defined to signify the weight of a sensing area of a sensor 10
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on the basis of its remaining energy. The proposed algorithm takes into account the residual battery power on the sensors to facilitate sensors running short on battery has a lesser chance of being essential. It provides a substantial flexibility 260
for controlling the existing energy in the network between all the sensors, therefore providing a larger network lifetime. The reliability of ART is constructed upon the classification of sensors as essential (E) nodes and non-essential (N) nodes. So as to choose the set of E-nodes, authors maintained a coverage set,
represented by C, to which E-nodes belong. Then it uses a greedy approach to 265
discover an approximating coverage set running in polynomial time. Though, ART mechanism is diverse in two aspects. Primary, it does not require a con-
nected set, subsequently N-nodes can still be used to forward the data packets. Secondary, it selects the coverage set of sensors to increase the benefit in terms of coverage, specifically, the major uncovered sensing area is covered with the 270
smallest number of sensors. Consequently, ART mechanism is used to cover the whole area with least number of sensors having more remaining energy. ART mechanism also defined an energy-aware greedy algorithm to locate a nearby optimal coverage set. In every phase, it chooses one node from the unselected
sensors which covers the largest area with maximum remaining energy level.
For this requirement, weight function is used to characterize the weight of a
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sensing area of a sensor based on its remaining energy. In addition, congestion control mechanism is incorporated to improve network performance by saving and balancing energy that leads to increased network lifetime. It is asymmetric in the way it deals with event (upstream) reliability and query (downstream) 280
reliability. Reliability is established in the sense that queries from the base sta-
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tion are acknowledged by a lesser number of sensor nodes that can cover the whole sensing area and critical event data is received by base station. It classifies nodes into essential nodes (E-nodes) and non-essential nodes to
ensure reliability. Essential nodes are selected in a way that they cover the
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entire sensing region. So, selection of essential nodes ensures that event is identified by as a minimum one essential sensor node and query of base station affects entire sensible terrain. A periodic weighted greedy mechanism is used 11
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at sink node to select essential nodes. This mechanism selects nodes based on their residual energy and their sensing region from uncovered area. Each time 290
essential nodes are updated, nodes with higher energy levels are selected so that energy consumption is fair among all sensors. Set of essential nodes is updated
locally and globally in the network. When an essential node fails, then new
essential nodes are selected locally and global update of all essential nodes is done after predetermined update intervals. 295
ART is mainly comprised of three different key roles: reliable query transfer, reliable event transfer, and distributed congestion control. Reliability of event
transfer is directed from E-nodes to sink node provided by using ACK mechanism. Whenever a new event occurs, E-node sends the event alarm message
having Event Notification (EN) bit set. When sink receives this alarm message 300
with EN bit set, it sends the ACK for that message. If E-nodes do not receive
ACK within certain time interval, then E-nodes re-transmit the event alarm message.
Functionality of query reliability is well-defined to be accomplished when each query of the sink is acknowledged by those sensors that cover the complete 305
sensible terrain inside the region of deployment, which is essential and adequate
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for sink-to-sensor route reliability. Reliability of query transfer that is from sinkto-sensors is done by means of negative acknowledgments (NACK). Queries are
sent in sequence number by the sink, so any query loss can be detected by essential nodes using sequence numbers. Essential nodes send NACK message 310
if any out of order query is received. Though, lost query messages can be distinguished when E-Nodes obtain a new query message. This might affect in
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two problems. Initial, damage of the last query message cannot be perceived. Reflect the last message qk with sequence number k is lost. E-Node might not deal with the lost message since there is no successive query. Next, the
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query transmission frequency might be very low such that lost queries cannot be recovered before timeout mechanism. To detect the loss of final query message and other losses before timeout when query frequency is very low, a Poll/Final (P/F) bit is used. When query is last query or next query will be sent after 12
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a long time, P/F bit is set. Essential nodes send ACK for query with P/F
bit set to indicate successful reception of query. If ACK is not received, Sink re-transmits the query until ACK is not received.
In ART authors uses Congestion Timeout (CTO) for congestion detection,
that is dynamically determined on the basis of Round Trip Time (RTT) like adaptive retransmission timeout in TCP. Suppose that all sensors have an ini325
tial RTT that is the interval among the time after the message is sent and the time when the ACK of the message is acknowledged at the sender. Then,
RTT is calculated dynamically on the basis of latest RTT by means of time
stamp field. Sensors emphasize the time information in their messages sent back through ACKs by the sink. Therefore, E-nodes can control the RTT by 330
relating the time stamp acknowledged by ACK. Congestion control in ART is
done in a distributed manner by using E-nodes. Congestion detection is done on the basis of reception of ACK of event message. After classifying sensors as
essential and non-essential sensors, the primary purpose of asymmetric acknowledgement (ACK) message and negative acknowledgement (NACK) message is 335
to provide end-to-end reliable communications and signaling between E-nodes
and the sink node. For event reliability, ART uses lightly-loaded ACK mecha-
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nism among the E-Nodes and the sink node. Though, using an ACK mechanism
that needs acknowledgement for every message may result ineffective usage of battery power, which is measured to be a very scarce resource in WSNs. In 340
ART, congestion control is managed by the E-nodes in a distributed way. It is positioned on monitoring the ACK packets of event reports. If ACK message is not received by essential node within timer, E-node sends Congestion Alarm
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(CA) message to neighbouring N-nodes, N-nodes that receive CA message will stop sending their data to sink node. Then Event message is re-transmitted, if
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ACK is still not received before timeout period then CA message is again sent by increasing hop-count value. Each node that receives CA message stops sending data and decreases hop-count. Congestion Alarm message is forwarded till hop-count becomes 0. When ACK is acknowledged, E-node sends Congestion Safe (CS) message having same hop-count value that is of last congestion alarm 13
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message directed by essential node. All nodes that receive CS message resume
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their normal operation. Thus, regulation of excessive traffic for congestion control is done by reducing the number of sensors that send their information to sink node [37].
3.2. Drawbacks of Existing Protocol 355
We have formerly explained the working of an existing protocol in preceding part. In this part, we discuss major drawbacks of competing existing protocol, which are enhanced by proposed protocol.
3.2.1. NACK based Query Reliability Problem
NACK centered methods suffer from all-packet-lost and last-segment-lost 360
difficulties since the reception node does not have any method to identify the occurrence of some missing segments except some segment that reach the node
which has a sequence number greater than estimated [33]. Subsequently ART
protocol is founded on NACKs; there can be difficulties of all-packet-lost and
last-segment-lost. ART protocol does not address the query reliability problem 365
because reliability is ensured by using NACK message for missing sequenced
query. To indicate last query, Poll/Final bit is used and an ACK is expected
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for the last query. From the start of transmitting query from the sink node to the essential node, working condition like sensor node is either switched off or
temporarily shut down and inputting process is not verified. If an E-node is 370
temporarily switched off while receiving queries from sink, it will not be able to send ACK for query with P/F bit set and after specified time interval, sink node will gain send query until the ACK message is not received. This affects
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in more energy consumption of sensor nodes and increases delay in the network. 3.2.2. Congestion Mitigation Problem
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ART protocol does not address the congestion mitigation problem efficiently
because congestion control method used by ART uses Congestion Alarm and Congestion Safe signaling messages to handle congestion. Reception of ACK message of event reporting message is used as congestion detection. If ACK 14
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message is not received for an event reporting message within a timer greater than round trip time, then E-node sends Congestion Alarm (CA) message to
neighbor non-essential nodes. Consequent to reception of this Congestion Alarm message, those immediate nodes pause sending messages. After certain fixed
time interval, if the congestion is still no more alleviated, then essential node will resend the congestion alarm message with increased hop-count. This con385
gestion alarm message is forwarded till hop count becomes 0. As soon as the ACK message is acknowledged, it sends a Congestion Safe (CS) message to the
neighbor non-essential nodes with latest hop-count value and those nodes start
sending messages. The present method of congestion control does not use rate
control system. The usage of CA and CS messages reduces the average data 390
transfer ratio in case of heavy traffic and for more number of sensor motes. The
present congestion control method similarly presents delay in the network as no non-essential node be able to deliver messages that have received CA messages. Similarly, the usage of external messages to inform congestion dissipate network
energy and extra memory is required to store data values. Therefore, we require 395
a congestion control method that does not use external control messages rather than rate control mechanism to optimize values.
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3.2.3. Bandwidth Allocation Problem
Existing competing protocol does not address the issues of fairness among sensor nodes in terms of bandwidth allocation. In ART, packets of each source 400
node are routed independently and can follow various available routes to the sink. Hence due to multi-hop forwarding scheme, nodes nearer to sink will
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achieve more delivery ratio when compared to nodes that are far away from sink. Thus, unfairness towards the senor nodes that are distant from sink in ART protocol must be prevented so that all source nodes that share the same
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downstream traffic jam would have equal network bandwidth allocation.
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Table 1: End-to-End Delay and Drop Packet Ratio of existing protocol.
No. of sensor nodes
End-to-End Delay (secs.)
Drop Packet Ratio
50
6.62
0.23
7.85
0.26
8.39
0.31
11.13
0.39
13.52
0.49
100 150 200 250
3.2.4. End-to-End delay and Drop Packet Problem
ART is simulated on mobile sensors having a maximum speed of 20 m/s. ART works good under lightly loaded traffic; having event reporting frequency 1 secs. and query reporting frequency 10 secs. For this, drop packets is below 410
20% and end-to-end delay is 1.5-6.5 secs. In the following situations, drop packet
ratio, latency and energy consumption of ART are increased in the network like when we increase the number of sensor nodes in the network and when packet
load increases with the increase in number of events and queries per second. End-to-End delay and drop packet ratios in four scenarios having heavily loaded 415
traffic with event reporting frequency 0.1 secs. and query reporting frequency
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2 secs. are shown in the Table 1.
ART does not address this issue and needs an improvement in end-to-end delay, drop packet ratio and energy consumption for increased number of nodes. ART has not any accurate rate control method. Therefore, it depends on the 420
nodes to manage their own data transmission rates.
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3.3. Proposed Protocol
In the proposed solution, we use optimal path selection for transmission,
priority based fair bandwidth allocation and rate adjustment-based congestion control to increase network throughput [32]. The proposed method selects the
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optimal paths to transmit the sensed data and guarantees reliable data transmission between sensor nodes and the sink node. In this paper, we use a optimal
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path selection method to remove threats to the path to deliver both reliable
data transmission and over-all energy conservation. In the proposed method, the routing path is created by considering the energy required for data transmis430
sion, loss rate and delay. The optimal path in the sense is the path with lowest energy and hop. The emphasis is to decrease the path breaking and to surge the network lifetime. First, essential nodes are selected based on their sensing region and remaining energy. Selected essential nodes work as coverage set of
entire sensing field. Then we use Technique for Order Preference by Similarity 435
to Ideal Solution (TOPSIS) [40] approach to select optimal transmitting path
from the various alternative paths between any essential node and sink node.
Evaluation of various alternative paths is performed on the basis of metrics like remaining energy, and loss rate and delay. Then the optimal path is selected.
TOPSIS technique is a standard method to multiple criteria decision making 440
(MCDM) and has been broadly used in the existing methods. In real-world operations, we need to choose one output, which will fulfill the diverse objectives
to some range. Such a resolution is termed as finest compact solution. TOPSIS technique has the capability to recognize the finest alternate from a restricted
set of choices rapidly. It built upon the idea that the selected alternate must have the minimum distance from the optimistic perfect resolution and the ex-
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445
treme from the adverse utmost solution. TOPSIS can integrate relative weights of standard significance. To check the working condition of a node in order to increase query reliability, an initial control message is sent by sink to all nodes before sending queries. Then to control congestion, we use priority based rate 450
adjustment approach that also ensures fairness in terms of bandwidth allocation.
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3.3.1. Optimal Path Selection using TOPSIS Approach After selecting essential nodes, each essential node uses TOPSIS approach
to select optimal path to sink when data is to be transmitted. Let Pe,d represent the routes from essential node e to sink node d, where
455
k k Pe,d is the kth route. In case node j is on route Pe,d , then represent it as j ∈
k Pe,d . Infer Psk0 sn is formed by s0 → s1 → s2 → . . . → sn . Then the evaluation
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of route Psk0 sn is done on the basis of following parameters:
1. Delivery success rate: Let P LRj,d be the Packet Loss Rate on single-hop
connection between node j and its nearby sink node d. Next the Delivery Success Rate DSRks0 sn of Psk0 sn is calculated as given in Eq. (1): DSRks0 sn = 1 −
n−1 Y
m=0
(1 − P LRsm sm+1 )
(1)
2. Delay: Let Dj,d be the Delay of single-hop connection among node j and
its nearby sink node d. Later delay Dsk0 sn of Psk0 sn can be calculated as the aggregate of delay among all the nearby nodes on it as given in Eq. (2):
Dsk0 sn
=
n−1 X
Dsm sm+1
(2)
m=0
3. Hop count: Hop Count (HC ) has also influence while selecting optimal route. Value of hop count is given in Eq. (3):
HC ks0 sn = N f or Psk0 sn : s0 → s1 → s2 → . . . → sn
(3)
4. Energy: Transmitting possibility and residual lifetime of wireless sensor networks directly depends on residual energy of nodes on route P ks0 sn . If
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any node on route Psk0 sn dies out as a result of drained energy, then route
Psk0 sn will be unsuccessful to finish its communication. Let RE sm be the Residual Energy of node sm . Then, residual energy RE ks0 sn of route Psk0 sn depends on the minimal residual power of any node on it. Therefore, RE ks0 sn can be calculated as given in Eq. (4):
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RE ks0 sn = min{RE sm |m = 0, 1 . . . , n, s ∈ Psk0 sn }
Let TPsk s
0 n
(4)
is the calculated outcome of route Psk0 sn while TOPSIS is imple-
mented on entire above metrics. Then TPsk s is as shown in Eq. (5): 0 n
TPsk s = F (DSRks0 sn , Dsk0 sn , HC ks0 sn , RE ks0 sn ) 0 n
(5)
F represents the evaluating function of TOPSIS having four parameters delivery success rate, delay, hop-count and residual energy and gives the evaluated value 18
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TPsk s . Among all the alternatives, the one with maximum value of TPsk s is
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0 n
0 n
the considered the optimal one.
3.3.2. TOPSIS evaluation mechanism
Following steps are followed for TOPSIS evaluation: i. A decision matrix M = [mi,j ]P N s 465
tributes, i.e.
DSRks0 sn ,
0 sn ×4
k 1/Ds0 sn ,
is constructed to optimize four at-
1/HC ks0 sn , RE ks0 sn . Every Row of M
correlates to a single alternate route Psk0 sn where P N s0 sn is the number of the alternate routes among node s0 and node sn .
ii. Then, normalize the matrix M and obtain a new matrix M 0 = [m0 i,j ]P N s Matrix can be normalized as given in Eq. (6): sX m0 i,j = mi,j / m2i,j
0 sn
×4 ,
(6)
j
iii. Next, assign a weight to each attribute according to the weight age of im-
portance of each attribute in decision making and multiply each column
of matrix M 0 with its assigned weight wj to build the weighted controlled selection matrix. Then weighted controlled selection matrix R is given in Eq. (7):
(7)
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R = [ri,j ]P N s0 sn ×4
Where ri,j = wj × m0 i,j
470
iv. Determine the ideal solution R+ = rj+ 1×4 , rj+ = maxri,j , i = 1, 2, . . . P N s0 sN and negative ideal solution R− = rj− 1×4 , rj− = minri,j , i = 1, 2, . . . P N s0 sN .
Where R+ specify the best desirable alternate and R− the minimum desir-
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able alternate.
v. Determine the difference between every alternate route from optimistic best resolution and undesirable perfect solution. The difference of any alternate route from positive best resolution is calculated as given in Eq. (8): v u 4 uX 2 + ri,j − rj+ di = t j=1
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(8)
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The difference of any alternate path from undesirable perfect resolution is calculated as given in Eq. (9):
v u 4 uX 2 t d− = ri,j − rj− i
(9)
j=1
vi. Determine the relative nearness of each alternate path Psk0 sn relating to ideal solution as shown in Eq. (10):
TPsk s = 0 n
d+ i + d− i + di
(10)
vii. Rank the order of alternative paths according to value of TPsk s and a path 0 n
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Psk0 sn with highest value of TPsk s is selected as optimal path. 0 n
Two packets are used for selecting optimal path, i.e. PREQ and PREP.
Let an essential node e broadcasts a PREQ packet to find a path to sink
node d when a packet is to be transmitted and there is no existing path between e and d. PREQ packet contains fields for TPe,d of current path P ke,d , minimum k 480
energy, delay, and loss rate record.
When a node receives PREQ packet, it will receive link information from PREQ and update reverse routing table if:
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a) There is no existing reverse path.
b) Sequence no. of PREQ packet is greater than previous PREQ packet. 485
c) TPe,d is greater than stored in reverse routing table. k Moreover, it broadcasts the updated PREQ packet to find out the remaining path to destination.
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When PREQ packet reaches sink node d, it replies with PREP packet. When
an intermediate node receives PREP packet, it calculates TPe,d based on ink
490
formation passed in PREP packet, selects the optimum route and then sends updated PREP packet in reverse direction. Essential node e receives all PREP packets and selects the optimal path
based on TPe,d k value.
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After selecting an optimal path, data is transmitted along that path. Further, path selection is done periodically to remove drained nodes and corrupt routes, which need to be informed in timely manner. Fresh paths might direct
communications to improved routes in direction to extend the life-cycle and increase the throughput.
3.3.3. Reliable Event Transfer 500
After selection of optimal path, Each E-node sends the event data on selected optimal path, which increases the average delivery rate and network lifetime.
Algorithm 1 Reliable Event Packet Data Transfer from Sensor Node to Sink Input: Specified a network of sensor as G. Any Essential-Node is directing an Event-Alarm message pEA i ; set timeout period tout ;
1. Essential-Node: If pi = pEA i , fixed the Event-Notification bit, and check if optimal path is selected. If no optimal path selection is done before,
initiate the path selection based on TOPSIS approach. Send message to the sink on optimal path, begin clock and store pEA till an ACK is granted. i Else, forward message to sink node and remove it from memory.
2. Sink-Node: Deliver a ACK on reverse optimal path if it gets a pEA i .
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3. Essential-Node: If no ACK is granted within timer for pEA , then transi mit again pEA and reset clock. i
3.3.4. Solution to NACK based query reliability problem In our proposed protocol query reliability is increased by using optimal path
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for query transfer. Queries are sent on selected optimal paths between essential
505
nodes and sink. If no optimal path exists, then sink initiates the path selection to corresponding essential node and selects the optimal path. Next, sink sends the initial query control message to check the working condition of nodes and waits for ACK from essential nodes. If ACK is not received from an essential node, then sink retransmits control message after time-out period. After receiving
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Algorithm 2 Reliable Query Transfer Input: Specified a network of sensor as G; Sink-Node consists of number of queries Q = [q1 , . . . qi , qi+1 , . . . ]
1. Sink-Node: Check if optimal path exists. If not, select one based on TOPSIS approach.
2. Send initial query control message to all the Essential-nodes and wait for an ACK from each Essential-node.
3. If no ACK is acknowledged from an Essential-nodes, Sink will again send query control message after time-out period.
Once all the ACKs are received from the Essential-nodes, start regular process of sending the queries in increasing order: 1, 2, . . . i.
4. Essential-Node: Get the information for qi . Verify the order of sequence number for failure finding.
5. Essential-Node: If a difference is found in the sequence numbers, deliver a NACK to recover the missing communication.
6. Sink-Node: Resend qi−1 if a NACK is acknowledged.
7. Essential-Node: While the queries are acknowledged in order, verify if P/F bit is fixed, and then deliver an ACK to the sink node.
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8. Sink-Node: Resend the communication with P/F bit fixed till the ACK is acknowledged.
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ACKs from all essential nodes, sink will send queries. Our proposed protocol is enhanced when compared to traditional protocol since our proposed protocol
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uses ACK/NACK based scheme for achieving query reliability instead of NACK based scheme used by existing traditional protocol. 3.3.5. Solution to congestion alarm and congestion safe based congestion miti-
515
gation problem
In our proposed solution after transmission of data on optimal path, and in
order to reduce congestion, we measure congestion by means of average packet
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inter-arrival time and average packet service time. Here, packet inter-arrival time is the average of interval period between the arrival of two packets either
from source traffic or from routed traffic and the average packet service inter-
val is the average time between a packet arrival at MAC layer and successful transmission of final bit of packet. Many times, in wireless sensor networks packets are routed around the congested area and scatter the excessive number
of packets among the multiple paths. It is vastly likely that the congestion is occurred by burst traffic. For example, the sensor nodes will produce transient
bursts of traffic, when the unexpected events arise. When burst happens close
to node v, the queue length of this node might be low, nevertheless it is clear
that node v is not a suitable destination for the source nodes to send the packet,
as a lot of packets will arrive v due to the burst nearby it and the queue length field cannot differentiate this problem. To solve this problem, we describe the
congestion degree field. Hence, Congestion Degree (cd) is calculated as the per-
centage of average packet service time over average packet inter-arrival time in a quantified interval period for every sensor mote k as shown in Eq. (11): cd(k) = tservice (k)/tinter
arrival (k)
(11)
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The calculated value of congestion degree specifies the level of congestion of the
network. Once the congestion is sensed, it is reported using implicit signaling. On reception of congestion notification signal, the transmission rate is controlled so as to decrease the congestion. Congestion degree can be calculated on each node packet-by-packet basis. It represents congestion time of every sensor node. Moreover, it effectively points out at both node-level and link-level congestion.
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To update the congestion degree, packet inter-arrival time and packet service time are calculated by exponential weighted moving average (EWMA) algorithm [16]. Here, tinter
arrival (k)
is updated periodically whenever N new packets
arrives as shown in Eq. (12): tinter
arrival (k)
= (1 − αia ) ∗ tinter
23
arrival (k)
+ αia ∗ TN /N
(12)
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where 0 < αia < 1 is a constant, TN is the time over which N packets arrive
and tservice (k) is updated whenever a packet is forwarded as shown in Eq. (13): tservice (k) = (1 − αs ) ∗ tservice (k) + αs ∗ t0service (k)
(13)
where 0 < αs < 1 is a constant, t0service (k) is the service time of packet communicated.
When congestion degree cd(k) is greater than 1, service time will be larger
than packet inter-arrival time, and then buffer overflow occurs. Otherwise, 520
when service time is less than packet inter-arrival time, congestion degree will
be less than 1, congestion fades away. We use implicit congestion notification to reduce additional control packets. Each sensor mote piggybacks its congestion degree in packet header of outgoing packets. Due to broadcast nature of wireless
channel, this information is heard by all the child motes. Finally, the scheduling 525
rate is increased or decreased on the basis of congestion degree. Our proposed solution is better than existing solution because our scheme uses rate adjustment
based congestion control mechanism that has higher delivery ratio and reduced delay and energy consumption instead of congestion alarm and congestion safe
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messages used by existing protocol.
3.3.6. Solution to bandwidth allocation problem In order to ensure fair bandwidth allocation, all essential nodes are assigned an equal priority index 2 and all non-essential nodes are assigned with priority index 1, so that essential nodes are having higher priority than non-essential nodes. Thus, all essential nodes with equal priority index have equivalent bandwidth [38]. A global priority index of each node is calculated as the total of
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global priority of entire child nodes and priority of itself. If there are no child nodes, global priority is equal to priority of node itself. Global Priority (GP ) of any sensor node k can be computed as shown in Eq.
(14):
GP (k) =
X
GP (child node(k)) + priority(k)
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(14)
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Each packet includes its global priority in packet header. When congestion occurs, scheduling rate of nodes is reduced based on global priority index to 540
mitigate congestion, which leads to fairness. Our protocol provides better solu-
tion for bandwidth allocation problem by assigning the equal priority to essential and non-essential nodes, which ensures fairness. 3.3.7. Steps of Proposed Method
1. Select Essential nodes based on residual energy and sensing region and 545
assign priority equal to priority index to each essential node.
2. Select optimal path between essential nodes and sink node using TOPSIS approach.
3. Transmit queries and event on selected optimal path.
4. Piggyback congestion degree and global priority index in each forwarded 550
packet header.
5. If congestion degree is greater than previous congestion degree, this means
that congestion is increased, and then child node reduce the scheduling rate based on global priority of parent node and itself as shown in Eq. (15):
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Rsch(i) = Rsch(i) ∗ GP (i)/GP (pi)
(15)
6. If congestion degree is less than 1 and lesser than previous congestion degree, then scheduling rate is increased to utilize bandwidth as shown in Eq. (16):
Rsch(i) = Rsch(i)/cd(pi).
(16)
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Update coverage set periodically.
4. Performance Analysis In this section, we have explained the performance investigation of recom-
mended BRCCTP protocol and the traditional protocol. We have implemented
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packet oriented simulation. The current method works by altering the technique
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a prevailing method works at the sensor nodes and sink node. We have com-
bined a few functionalities equally at the sink node and sensor nodes to enhance the algorithm.
4.1. Performance Metrics 560
Comparison has been made with recommended BRCCTP protocol with a prevailing protocol on the foundation of seven metrics. These metrics are described as follows.
4.1.1. Average delivery ratio
It describes the capability of the protocol to effectively allocate the data
packets to last destination inside assured interval. It is the proportion of sum of messages a destination node received to the number of messages a source node injects into the network. We have computed the average of the delivery ratio
of number of simulations to acquire the average delivery ratio. Mathematically, average delivery ratio is given by: Average Delivery Ratio =
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4.1.2. End-to-End Delay
Number of packets received by destination node Number of packets sent by source node
It represents the time reserved by a packet to reach the destination node from source node. Specifically, the time change among the time when the packet arrives at the transport unit of the receiver and the time when the packet was transmitted by the transport unit of the sender. 4.1.3. Average Delivery Overhead
Average delivery overhead is measured as the sum of messages sent each
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data message acknowledged by a destination node. This parameter inspects the transmission cost to accomplish consistent transfer of packets over the network. We average the delivery overhead of some simulation results to find the average delivery overhead.
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4.1.4. Drop Packet Ratio
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Drop Packet Ratio is determined as the ratio of the number of packets drop
to the number of packets generated by data sources. A protocol with less drop packet ratio is more reliable. Mathematically drop packet ratio is given by: Drop Packet Ratio = 4.1.5. Queue Length
Number of Drop Packets Number of sent packets
Queue Length determines the size of queue at each node. Given method
depends on the current node’s message queue holding. When the qualified number of queue up packets exceeds a definite pre-mentioned level (e.g. 75%), 580
the node is measured to be congested, and will continue to alert its peers of that
situation. Therefore, to control congestion, every node identifies congestion by observing its queue occupancy. The congested node then basically piggybacks a congestion degree together with its data, therefore winning benefit of the
transmission environment of the wireless medium to transmit its congestion 585
degree to its neighbors. 4.1.6. Energy Efficiency
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It is described as the ratio of the summation of residual energy of nodes to the summation of preliminary energy of nodes. Mathematically, Energy Efficiency =
Sum of residual energy of nodes Sum of initial energy of nodes
A network with high energy efficiency would have high network lifetime as compared to the one with less energy efficiency. So, protocol must achieve high
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energy efficiency.
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4.1.7. Fairness Index
To measure fairness, we use popular fairness Index that is Jain’s Fairness
Index [18] which is calculated as Pn 2 ( i=1 ri ) Fairness Index (φ) = Pn 2 n ( i=1 ri ) 27
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Where n is the number of sensors in the network and ri is the average rate
of packets delivered from the ith sensor. Fairness index lies between 0 and 1.
If average percentage of packets transported from each node is same, fairness index would be 1. 595
4.2. Simulation Setup
Proposed BRCCTP protocol and the traditional protocol are tested using
NS2 simulator [17]. For Simulations, sensors are placed randomly in a square area. Each sensor is having a fixed sensing range r. Two sensor nodes can
communicate with each other if they are in communication range of each other. 600
A sensing field of 300 × 300 m2 is required for simulations. We simulated the network with sensor nodes 50, 100, 150, 200 and 250 so that we are able to eval-
uate the performance of our protocol from very sparse to large dense networks. Used figure of sensors must be adequate to bind the sensing area for specified
metrics. Wireless sensor nodes are taking communication range of 90m and 605
sensing range of 60m are used. Initial energy of sensors is 3 Joules (J). In these experiments, an action is characterized to identify the phenomenon node in the recognizing area of a sensor.
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A data delivery model is based on event-driven and is used to transmit the
data between the sensor nodes and sink node. When an event is detected, 610
sensors send data. If an event occurs during the update interval, then sensor nodes report event to sink node by transmitting successive messages. Event reporting frequency metric is used to modify how often a sensor node directs event reports to sink after phenomenon occurs in its sensing area. Sink is in the
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center of the sensing field. For routing, AODV protocol is used and for MAC, 615
CSMA/CA protocol is used. Table 2 lists the parameters for the simulation. Let us consider scenario of 50 sensor nodes and a sink node 0 as shown in
Fig. 1.
Out of these 50 nodes, essential nodes are selected based on their residual
energy and sensing region. Nodes with grey color filled indicate the essential
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nodes and dotted circles are used to indicate the sensing region boundary of each 28
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Table 2: Parameters for Simulation.
Sensing field area
300 × 300 m2
No. of sensor motes
50, 100, 150, 200, 250
Time of Simulation
150 secs.
Packet length
100 bytes
Interface queue length
50
Sensor node radio range
90 m
Sensor node sensing range
60 m
Idle power
13 mW
24 mW
Receive power
13 mW
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Transmit power
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Figure 1: TOPSIS based 50-node Network Topology.
essential node and remaining nodes with no color filled indicate the non-essential nodes. Union of sensing region of essential nodes cover the entire sensing area. Now each essential node selects an optimal path between itself and sink node by implementing TOPSIS method out of various alternative paths based on
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the following attributes: hop count, delay, delivery success rate, and residual
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Table 3: Comparison of Average Delivery Ratio for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
BRCCTP
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs. fe=1 secs. fq=2 secs.
fq=2 secs.
fe=0.5 secs. fe=0.5 secs. fe=1 secs. fe=1 secs.
50
0.768
0.796
0.797
0.828
0.878
0.883
100
0.741
0.761
0.733
0.791
0.846
0.864
150
0.686
0.722
0.714
0.761
0.879
0.871
200
0.604
0.681
0.692
0.746
0.835
0.862
250
0.507
0.579
0.646
0.696
0.802
0.838
energy. Selected path from each essential node is indicated with red arrows and any node that is on optimal path from essential node to sink node is colored with red boundary.
4.3. Simulation Outcomes 630
Simulation has been carried out for ART protocol and the proposed BRCCTP protocol using NS-2 simulator. The network is visualized using NAM
(Network Animator). To do pursuance calculation of ART and BRCCTP pro-
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tocol, seven metrics are considered; these are average delivery ratio, end-to-end delay, average delivery overhead, drop packet ratio, queue length, energy effi635
ciency and fairness index.
4.3.1. Average delivery ratio
Table 3 shows result of ART and proposed BRCCTP in the aspect of average
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delivery ratio for various network size and traffic loads. We have simulated three different traffic loads by variable event-reporting frequency (fe ) and query
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reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs., moderate: fe = 0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Every
test is executed ten times and the outcomes presented are an average of these execution.
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Figure 2: Comparing Average Delivery Ratio for Diverse Traffic Loads.
Figure 3: Comparing End-to-End Delay for Diverse Traffic Loads.
As we observe in Fig. 2, our procedure aside from resolving the difficulties
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with ART accomplishes considerable improvement over the prevailing protocol. When the size of network increases, the total number of packets acknowledged
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Table 4: Comparison of End-to-End Delay for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs.
BRCCTP
fe=1 secs.
fq=2 secs.
fq=2 secs.
fq=5 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
6.621
5.901
2.483
2.082
1.594
1.432
100
7.854
6.892
4.124
3.447
1.985
1.761
150
8.392
7.287
7.891
6.218
2.692
2.234
200
11.131
9.766
10.014
8.183
4.236
3.653
250
13.522
12.092
12.431
10.786
6.298
5.202
at the receiving node diverges. Higher amount of data packets is acknowledged
by receiving node just in case of proposed BRCCTP protocol. The reason of this is that our BRCCTP protocol uses rate adjustment to optimum rate for 650
congestion control. The prevailing protocol Congestion Alarm and Congestion Safe based congestion control method decreases the communication ratio to zero
after a node’s buffer touches the threshold; because of this not any packets can be sent to that node. That decreases delivery ratio for ART protocol. Proposed
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protocol accomplishes greater average delivery ratio. 4.3.2. End-to-End Delay
End-to-end delay indicates how much interval of time is used by packet to reach the destination. The comparison results of end-to-end delay for existing and projected protocol for three diverse traffic loads are shown in Fig. 3. Table 4 shows end-to-end delay for existing and proposed protocol for three different traffic loads.
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It is inferred from Fig. 3 that the BRCCTP protocol has less end-to-end de-
lay than the existing protocol. This improvement in end-to-end delay is because delay of path is used as a selection parameter in TOPSIS approach while choosing optimal path and the one with minimum delay and hop-count is selected.
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While in ART protocol, packets are independently routed and can follow any
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available path. Consequently, delay in packets transmitted on optimal path is
less as compared to existing protocol. Delay of proposed protocol is also reduced
due to congestion control using rate adjustment to optimal value. Body-area networks can gather information about a person’s health, fitness, and energy 670
expenditure. In health care applications the confidentiality and legitimacy of user data has prime importance. Particularly due to the addition of sensor net-
works, with IoT, the user authentication becomes more challenging. End-to-end
delay of proposed protocol is reduced even when node density is equal to 250 and having high traffic load when compared to ART protocol. 675
4.3.3. Average Delivery Overhead
Here, we find transmission rate for reliability in the two systems in numerous
channel environments with a 3-hop network. Transmission rate is determined as the average amount of communication for each data packet. It is known as
average delivery overhead. Our proposed BRCCTP protocol also has reduced 680
communication overhead as compare to ART protocol because our protocol requires less number of per data packet transmission as compared to existing
protocol. Table 5 displays the result of ART and BRCCTP protocols in relation to average delivery overhead for diverse network size and traffic loads. We have
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simulated three different traffic loads by variable event-reporting frequency (fe ) and query reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs.,
moderate: fe = 0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Every test is executed ten times and the outcomes presented are an average of these execution. As we observe in Fig. 4, the rate for BRCCTP is constantly
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lesser than ART protocol in relation to average delivery overhead for different 690
network size and traffic loads. Hence, the proposed protocol chooses the optimal path for data transfer using TOPSIS approach based on various parameters including delivery success rate of the path that provide reliable delivery of data packets to the destination with less number of packet drops.
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Table 5: Comparison of Average Delivery Overhead for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs.
BRCCTP
fe=1 secs.
fq=2 secs.
fq=2 secs.
fq=5 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
4.2
1.8
3.6
1.5
2.9
1
100
4.9
2.1
3.9
1.7
3
1
150
5.4
2.7
4.3
2
3.3
1.3
200
6.1
3.2
4.8
2.4
3.6
1.5
250
6.8
3.5
5.1
2.6
3.9
1.8
4.3.4. Drop Packet Ratio 695
Drop packet ratio measures the number of packets that never received by the destination. The comparison of drop packet ratio for existing and proposed
protocol for three different traffic loads is given in Fig. 5. We have simulated three different traffic loads by variable event-reporting frequency (fe ) and query
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reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs., moderate: fe =
Figure 4: Comparing Average Delivery Overhead for Different Traffic Loads.
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Figure 5: Comparing Drop Packet Ratio for Different Traffic Loads.
Table 6: Comparison of Drop Packet Ratio for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs. fq=2 secs.
fq=5 secs.
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fq=2 secs.
fe=1 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
0.232
0.204
0.203
0.172
0.122
0.117
100
0.259
0.239
0.267
0.209
0.154
0.136
150
0.314
0.278
0.286
0.239
0.121
0.129
200
0.396
0.319
0.308
0.254
0.165
0.138
250
0.493
0.421
0.354
0.304
0.198
0.162
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BRCCTP
0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Table 6 shows the drop packet rate for prevailing and proposed protocol for three different traffic loads.
It is inferred from Fig. 5 that the BRCCTP protocol has lower drop packet
ratio as compared to the existing protocol. This is because the proposed pro-
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tocol selects the optimal path for data transfer using TOPSIS approach based 35
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on various parameters including delivery success rate of the path. Further, it uses rate adjustment to optimum value for congestion control. So, combination
of both of these techniques reduces drop packet ratio for proposed protocol. The existing protocol uses congestion alarm and congestion safe messages based 710
congestion control mechanism to reduce traffic of non-essential nodes that does not control congestion effectively and increases drop packet ratio for existing
protocol. Drop packet ratio of proposed protocol is reduced effectively under
high traffic load with event-reporting frequency (fe ) equal to 0.1 secs. and query reporting frequency (fq ) equal to 2 secs. 715
4.3.5. Queue Length
As we observe from Fig. 6 and Fig. 7, our proposed BRCCTP protocol queue occupancy is less as compared to ART protocol. As we go away from sink node
buffer occupancy also decreases after applying the proposed algorithm. We con-
trol the queue occupancy at every node by adjusting the reporting rates among 720
sensor nodes then in turn we avoid queue overflows at those nodes. This also
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results in decrease of drop packet ratio. Hence, we overcome the congestion
Figure 6: Queue Length of ART protocol.
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Figure 7: Queue Length of BRCCTP protocol.
in sensor network. Our proposed BRCCTP protocol also has less computa-
tion overhead as compared to ART protocol because our proposed solution uses
priority based fair bandwidth allocation and rate adjustment based congestion 725
control instead of congestion alarm and congestion safe based congestion mit-
igation used by ART protocol. The queue availability can also be used as the
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reverse of queue occupancy. It characterizes the amount of packets a node might
still accept before it starts to decline arriving data. The proposed protocol emphasizes on proficient organization of queue to deliver reliability and decrease 730
packet loss. It is also inferred from Fig. 6 that most of the time in ART protocol queued packets surpasses a certain predefined threshold (75%) that causes packet loss and large delay in network. In a network, if the queue length is not
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properly maintained, the source node will continue transporting packets at the preliminary level that results in packet loss. Thus, in our proposed protocol we
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reduce transport ratio of packet transmitters formerly surpassing a determined threshold so as to avoid the packets from being discarded. In a perceptive application identical to health monitoring, loss of packet is the most significant problem that has to be reduced.
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Figure 8: Comparing Fairness Index.
4.3.6. Fairness Index 740
Fairness index measures the fairness in terms of reporting rate of all sensor nodes. The evaluation of fairness index for prevailing and proposed protocol by
varying traffic load in relation of packets per second (PPS) is depicted in Fig. 8. Table 7 shows fairness index for existing and proposed protocol at different
traffic loads. It is inferred from Fig. 8 that the BRCCTP protocol has higher fairness index than ART protocol because in our proposed protocol, an equal
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priority index is assigned to all data source nodes and a global priority index is calculated at each node. Rate adjustment to control congestion is done on the basis of global priority index of nodes that provides fair bandwidth allocation to all data sources. As traffic load that is number of Packets Per Second (PPS) 750
increases, fairness index decreases due to congestion. Nevertheless, BRCCTP
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protocol has better fairness index even under high traffic load condition than existing ART protocol. 4.3.7. Energy Efficiency Energy efficiency of network represents the ratio of remaining energy of net-
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work to the initial energy of the network. A network having high energy effi-
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Table 7: Comparison of Fairness Index.
Traffic Load (PPS)
ART
BRCCTP
0.25
0.919
0.924
0.5
0.672
0.698
1
0.437
0.542
2
0.296
0.451
3
0.248
0.409
4
0.225
0.413
ciency would have better network lifetime. The evaluation of energy efficiency
for prevailing and proposed protocol for different number of nodes is given in Fig. 9. Table 8 shows energy efficiency for existing and proposed protocol at different node densities. 760
It is concluded from Fig. 9 that the proposed protocol has higher residual
energy of network than the prevailing protocol. This is the reason that our pro-
posed protocol always chooses the path with higher remaining energy for data
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transfer using TOPSIS approach and hence maintains an energy balance in the
Figure 9: Energy Efficiency for Different Node Densities.
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Table 8: Comparison of Energy Efficiency for diverse node densities.
No. of Nodes
ART
BRCCTP
50
0.329
0.352
100
0.291
0.319
150
0.198
0.242
200
0.153
0.194
250
0.097
0.115
network and the congestion mitigation method in proposed protocol uses ratio 765
regulation strategy that manages congestion efficiently as a result of which less packets are damaged in proposed protocol and therefore, less retransmissions
and lower energy loss. Packet drops are higher in ART protocol and packets
follow in any available path, so energy consumption is more in ART protocol, which reduces the residual energy of network. Resource management is of princi770
pal importance for QoS provisioning as the equivalent resource budgets essential to be guaranteed in order to accomplish certain QoS levels. This is mainly ac-
curate for WSNs where communication, computing and energy resources are
fundamentally restricted. Resource management in WSNs is very challenging,
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as of the ever-increasing complexity of WSNs. Energy efficiency of proposed
protocol is increased even when node density is equal to 250 and having high traffic load when compared to ART protocol.
5. Conclusion
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To conclude, we propose a new Wireless Sensor Networks (WSNs) protocol called: Bidirectional Reliable and Congestion Control Transport Protocol (BR-
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CCTP).We analyzed its performance under different scenarios and compared with the competing ART protocol.Simulation results have shown the better performance of our proposed scheme when compared to ART scheme. The BRCCTP makes use of TOPSIS approach to select optimal path and it uses ratio regulation to optimize value based on priority so as to alleviate the effect of 40
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congestion. The latter can be distributed with in an optimum method, but not
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at the price of decreased throughput and delivery ratio. Priority based rate
adjustment provides fairness among all sensor nodes. It has been shown that BRCCTP protocol performance is superior when compared to ART protocol,
especially form the point view of the average delivery ratio, end-to-end delay, 790
average delivery overhead, drop packet ratio, queue length, energy efficiency and fairness index metrics. The BRCCTP protocol is successful in decreasing drop
packet ratio, end-to-end delay, average delivery overhead and queue length and in having better average delivery ratio, energy efficiency and fairness. References 795
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Conflict of Interest and Authorship Conformation Form
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Please check the following as appropriate: o
All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.
o
This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.
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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript
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The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:
Author’s name
Affiliation
Bhisham Sharma Gautam Srivastava Jerry Lin
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Brandon University
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Bhisham – Conceptualization, Data curation, Formal analysis; Gautam - Funding acquisition, Investigation, Methodology, Project administration, Roles/Writing – original draft; Jerry - Writing – review & editing.
PII: DOI: Reference:
S0140-3664(19)31546-4 https://doi.org/10.1016/j.comcom.2020.01.072 COMCOM 6193
To appear in:
Computer Communications
Received date : 30 October 2019 Revised date : 5 January 2020 Accepted date : 29 January 2020 Please cite this article as: B. Sharma, G. Srivastava and J.C.-W. Lin, A bidirectional congestion control transport protocol for the internet of drones, Computer Communications (2020), doi: https://doi.org/10.1016/j.comcom.2020.01.072. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
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A Bidirectional Congestion Control Transport Protocol for the Internet of Drones Bhisham Sharmaa,∗, Gautam Srivastavab,c,∗, Jerry Chun-Wei Lind a Chitkara
University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India. Email: [email protected] b Department of Mathematics and Computer Science, Brandon University, Brandon, MB, CANADA c Research Centre for Interneural Computing, China Medical University, Taichung, TAIWAN d Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen 5063, Norway. Email: [email protected]
Abstract
Wireless sensor networks (WSNs) are composed of energy constrained devices that autonomously form networks through which sensed information is trans-
ported from the region of interest to the central control station (sink), integration with unmanned aerial vehicles (UAVs) leads to enlarged monitoring area
and to enhance overall network performance. Due to application specific nature of wireless sensor networks, it is challenging to design a congestion control
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protocol that is suitable for all types of applications in the Internet of Drones (IoD). Congestion avoidance and control in wireless sensor networks mainly
aims at reducing packet drop due to congestion and maintaining fair bandwidth allocation to all network flows. In this research work, we propose a reliable and congestion based protocol, which provides both bidirectional reliability and rate adjustment based congestion control. It uses Technique for Order Prefer-
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ence by Similarity to Ideal Solution (TOPSIS) method to select optimal path for data transmission because TOPSIS selects an alternative such that it has shortest distance from the ideal one and greatest distance from negative ideal ∗ Corresponding
author Email address: [email protected],[email protected] (Gautam Srivastava)
Preprint submitted to Journal of LATEX Templates
January 30, 2020
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solution. Congestion is detected by using proportion of average packet service time over average packet inter-arrival time as congestion degree. Then, congestion is notified using implicit notification to save energy and reduce overhead.
To mitigate congestion along with maintaining fairness, an equal priority index is assigned to all data sources and when congestion occurs, rate adjustment
to optimal value based on priority value is used for congestion control. This
approach helps to diminish packet drops, maintain fairness and get better energy efficiency. Finally, we compare the performance of the proposed protocol
with that of existing protocols. Our simulation results show reduced average
delivery overhead, drop packet ratio, queue length and delay with increased average delivery ratio. Moreover, our protocol provides better energy efficiency and fairness when compared with the existing competing protocols.
Keywords: Wireless sensor networks; Transport layer protocols; Internet of Drones; sensor nodes; motes; TOPSIS; Energy Efficiency; Fairness; UAV
1. Introduction
Wireless Sensor Networks (WSNs) have constraints like limited energy and limited resources. In past few years, due to innovation in radio technologies and
5
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low-power circuit, WSNs have emerged and have achieved enormous popularity. A WSN is comprised of loads of self-organizing, lightweight sensor motes and being used for a wide range of monitoring applications. Each sensor mote is composed of one processing unit, a storage unit, a transceiver, limited battery power with multiple sensors on board. Due to small size and expensiveness of sensor motes, they have limited energy, limited processing power, limited memory and other resources. The mobile sink node shares the data with the sensor
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nodes establish on its fixed or dynamically calculated navigation route. This technique decreases the energy consumption but indicates a substantial problem by restricting the mobile collector to flat navigable surfaces. To overwhelmed this drawback, the mobile sink node can be carried by an unmanned aerial vehi-
15
cles (UAV), commonly known by their everyday name, drones. Any protocol for
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[23, 31, 4, 24].
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WSNs should be designed by considering the limits of sensor motes cautiously
WSN-UAV are having enormous applications like military area monitoring,
smart home monitoring, air-pollution monitoring, target tracking and health20
care etc. The UAV network can undertake the part of mobile base stations when required, in order to regroup the sensor nodes located within the sensing
area, into WSN clusters. Additionally, smarter dimension systems driven by
UAVs embed additional processing at the point of procurement, taking valu-
able critical data that in the hands of the right people, can decode to improved 25
and quicker data-driven results. Wireless Sensor Network have emerged as re-
search areas with a great effect on practical application developments. WSNs are having enormous applications like military area monitoring, smart home
monitoring, air-pollution monitoring, target tracking and healthcare etc. Many WSN applications require reliable data transport for time-critical dynamic ap30
plications with no tolerance for data loss such as transfer of instructions from the sink to sensor motes in order to reprogram them to execute a task different
from the one they are performing currently or change the detection parameters
[7]. It is an important task to collect the data periodically from various sensor
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nodes for monitoring and recording the physical conditions of the environment.
The sensed data must be received and transmitted between the nodes in the network. In such cases sensor motes get in rather problematic situations, and creating connection with different sensor motes is not easy. Therefore, positive WSN deployment based on together the hardware characteristic and the network self-organization protocols which are used. Such applications have zero tolerance for data loss like if any part of instruction is dropped, then sensor
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mote will not be able to interpret the instruction correctly. Further in WSN, data flow can be upstream (sensor-to-sink) and downstream (sink-to-sensor). In upstream data flow, all sensor motes send their sensed data to base station and in downstream flow, sink may send control information or query messages
45
to sensor motes. So, reliability is required in both upstream and downstream directions for efficient working of WSN. Due to limited power resources and fre3
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quent node failures, providing reliability and quality of service in wireless sensor networks can be challenging [3, 27, 35].
The Internet of Drones (IoD) is a layered network control architecture which 50
has been designed predominantly for the coordination of access of UAVs (drones) to airspace that are controlled. Moreover, IoD provided services for navigating between areas referred to as nodes in a WSN. IoD can also provide more general services for a variety of drone applications. Some examples include • package delivery
55
• traffic surveillance • search and rescue
• emergency situation remote coverage
For applications, where sensor nodes send sensed data of a specific area to one or more base stations, traffic load is less in the senor network most of the 60
time. But, when an attention-grabbing event occurs, such as enemy intrusion,
the sensor motes will produce and send out a sudden massive amount of data. During these conditions, congestion occurs when a sensor mote accepts extra
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data than it can forward and redundant data has to be buffered. Thus, the restricted buffer space of a node is filled and results in loss of packets (new 65
or old). Due to this, energy and communication resources of sensor nodes are wasted and also packet loss results in reduction of the event detection reliability [25].
The problem of congestion in WSNs is quite different from traditional net-
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works. Protocols used for wired networks like TCP are not suitable for WSNs. 70
Congestion in WSNs has following negative impact on performance like throughput, which is decreased drastically and per-packet energy consumption is increased [11, 43]. Hence, congestion in WSN must be addressed effectively to prolong the network lifetime and congestion control can save energy of nodes and reduce the delay by adjusting the transmitting rates, or by using alternative
75
resources [6, 26].
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Currently, many schemes and algorithms have been proposed for congestion control and avoidance, as well as for reliable data transmission in WSNs. In the
great majority of congestion control algorithms, authors use either traffic control or resource control approach. By using traffic control, they choose to control the 80
rates with which sources inject packets in the networks and by using resource
control, they choose alternative resources to route congested traffic. On the other hand, reliable data transmission approaches usually run at the transport layer. Some of them are end-to-end and employ re-transmissions using ACK or NACKs [9]. 85
Energy efficiency is an important aspect of a WSN because it is repeatedly not possible to interchange or revitalize batteries of sensor nodes. Due to this constraint, it is very critical that a protocol be energy-efficient in order
to prolong the network lifetime and allow more data to be collected. In order to diminish the effects of congestion near the base station, an energy-efficient 90
protocol that can handle multiple simultaneous events without losing reliability is needed [44].
WSNs that include UAVs can be converted into applications that provides particular advantages such as: (1) On-site data gathering and examination (2)
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UAVs can provides ground WSN clustering method by electing the cluster heads
(3) UAVs can make available mapping information. It is also used to support ground WSN deployment. This provides the flexibility of flying UAVs to any place in the area of interest. In WSNs, multi-hop forwarding scheme is used to forward data due to limited communication range of a sensor node [21]. Therefore, when congestion occurs, sensor motes that are closer to sink will be able to send more data to sink as compared to sensor motes that are far-off from
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the sink. So, this leads to unfairness towards far away data sources and reduces reliability. Consequently, fairness must be provided among all the sensor motes. The main focus in this paper is to determine the behavior of several network
parameters like traffic load, node density and their impact to congestion in
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WSNs and present design and analysis of bidirectional reliable and rate adjustment based congestion control transport protocol that is different from existing 5
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traditional protocols. Some other issues such as energy consumption and fairness problems and their relation to congestion are also examined.
The remaining of this paper is systematized as follows. Brief summary of ex110
isting transport layer protocols is defined in Section 2. In Section 3, bidirectional
reliable and congestion control transport protocol (BRCCTP) is proposed. The comparative analysis of both Asymmetric and Reliable Transport (ART) and
BRCCTP protocols has been discussed in Section 4. Lastly, in Section 5 paper is concluded.
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2. Existing Transport Layer Protocols
Different transport layer protocols have been proposed to locate numerous
problems for example packet loss recovery, reliability, congestion control, energy efficiency, flow control, and heterogeneous application support in wireless sensor
networks. A brief detail of the associated protocols is explained in this section. 120
In [13], the authors proposed the congestion and communication channel
occupation in WSN by examine the states that results in congestion of the communication network by determining the subsequent nodes configuration con-
straints: (1) data packet generation rate, (2) transmission time intervals, and
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(3) transmitter output control level. From the graphics presented by the au-
thors shows that around is a saturation level, and higher than this level the connection is no longer linear. The degree of this level is start from 20 number of data packets per second to 60 number of data packets per second and if data transmission is higher than this degree, the communication channel will not transmit data successfully, and also delay will increase. In [30, 8], the authors proposed the adaptive cuckoo search based optimal
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rate adjustment (ACSRO) for the congestion prevention and mitigation in WSN. The realization of the proposed protocol is calculated by using various performance parameters for example latency, throughput, congestion level, normalized packet loss, and normalized queue size. The position of the congestion is dy-
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namic at the sensor node, which implies that the ACSRO performs the action
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and removes congestion in the system. It uses the rate optimization approach
to control the congestion by decreasing the rate of the child nodes in the sensor network.
In [20, 28], the authors deal with congestion avoidance and interference reg140
ulation in WSN by developing the method named IACC (Interference-Aware
Congestion Control). This method provides maximum bandwidth size consump-
tion for each node through efficient rate control among the interfering nodes. IACC is based on two phases verification. In first phase, the overall bandwidth
capacity among every node and its parent node is projected and further forward145
ing projected dimensions to the base-station. In second phase, the scheduling
scheme is used to guarantee the fairness though mitigating congestion in the network.
In [2], the authors proposed dynamic rate control on source node in transport layer protocol for WSN. They established a dynamic source rate based adjust150
ment control algorithm that implements a cache-aware method where cache
management strategies are active to inform the comparative amount of packet
losses and set boundary limit for transmission window size. This rate control method accomplished 30% rise in cache consumption although sustaining a ra-
155
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tional portion of bandwidth distribution among child nodes which did not lower than ERCTP and did not improve than static DTC and DTSN + protocols.
In [10], the authors developed an energy efficient conditional multi-path and multi-copy routing technique. It consists of two novel data transmission schemes, viz. CM and CMrest along with consistent mathematical programming models. It also includes two legal dissimilarities in order to decrease the resolution periods for mathematical prototypes with profitable solvers. Authors
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160
also define a heuristic method, that can implement together for the CM and CM
rest
. Both of these solution techniques, precise model through legal dif-
ferences and the heuristic technique, can be implemented to resolve CM and CMrest depending on the balance among quality and solution time in a specific
165
application.
In [12], The degree of congestion of the child node is calculated by determin7
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ing buffer occupancy ratio and further sending this data to the current node.
Rate of transmission is adjusted by the current node to mitigate the difficulty
of congestion and increasing the throughput of network with multi-classification 170
gained through Support Vector Machines (SVMs). Simulations results of author
express that the finest tool for data classification is SVM. Moreover, results validate that the proposed scheme requires less energy consumption and increased throughput.
In [19], the authors proposed Reliable, Efficient, Fair and Interference-Aware 175
Congestion Control (REFIACC) protocol for WSNs. This protocol avoids the
interference and guarantees an increased fairness of bandwidth consumption by planning the communications among sensor nodes. Linear programming is
currently used to achieve optimal consumption proficiency of the determined
existing bandwidth. It has been implemented in TOSSIM simulator and related 180
with two existing works. In REFIACC congestion control is established on physical dimensions of radio links size.
In [29, 41], the authors proposed a Hierarchical Energy Efficient Reliable
Transport Protocol (HEERTP) for the data communication in WSNs. It is
transport layer protocol based on cluster-based selection that reduces energy consumption by decreasing repetitive data transportation over wireless sensor
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185
networks. It minimizes the broadcast of detected repetitive data with the direction of the base station. It makes an order of groups comprising of motes for data collection inside the network. One node in the network is selected as the cluster head (CH). It is accountable for gathering the data from the sensor 190
nodes. It forwards the gathered data to the CHs and then to the base station.
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The CH is nominated on the foundation of remaining energy of the sensor motes and coordinate position. In [22, 1, 34], the authors defined congestion control method called packet
priority intimation (PPI). It includes PPI bit in every packet to highlight its
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status. The key benefit of this method is to send highest priority packets with least latency. The congestion is mitigated and the data traffic is maintained in an effective way through low and high priority array. This technique is useful 8
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not only in single health care, but also in numerous wireless sensor network applications. It is extended and improved from First Come First Serve (FCFS) 200
scheme and can increase the efficiency in relations to latency, packet drop rate, energy efficiency, and throughput.
In [36], the authors proposed protocol for multicast communication and retransmission based reliable error control to make it explicitly for data centric networks. It functions in three phases: first phase is communication setup in 205
which the multicast subtrees are formed and the suitable bloom filters are dis-
persed. Second phase is primary content distribution in which the producer
directs the full information and assembles collective feedback. Third phase is retrieval where single or multiple cycles of originating re-transmissions and gathering novel combined response, till entire subscribers have finished the transfer 210
positively.
In [39], the authors proposed an effective redundancy coding-based data communication method for wireless sensor networks (RECODAN), which assures re-
liable data communication in hop-by-hop manner over hybrid re-transmission. It makes use of selective Reed-Solomon coding to decrease the procedure of en215
coding in an intermediary node. To increase data reliability, it makes use of an
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adaptive codeword re-transmission. It also uses the Ornstein-Uhlenbeck (OU)
method to determine the packet receiving rate and reduce data redundancy in code delivery as well as increasing the network lifetime. Simulation experiments were conducted in MATLAB and outcomes demonstrate that RECODAN can 220
considerably increase the data communication reliability and reduce the delay
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and energy consumption.
3. Bidirectional Reliable and Congestion Control Transport Protocol In this section, we first evaluate the functioning of an existing protocol Asym-
metric and Reliable Transport (ART) and from the literature studied it has been
225
found that only ART provides bidirectional reliability that is required for the application wherever a user node wants to re-task a cluster of sensor motes in
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its locality by inserting program images into target nodes and where the loss of packets is not tolerated. Now we define the drawbacks of current protocol and afterwards we deliver the resolution to those drawbacks in our recommended 230
Bidirectional Reliable and Congestion Control Transport Protocol (BRCCTP) protocol for WSN.
3.1. Analysis of a Prevailing Transport Protocol
An ART mechanism for WSN is a transport protocol that delivers bidirec-
tional reliability for reliable event and query communication in wireless senor 235
networks. ART implement an event-driven data delivery model to transmit data
from sensors to the sink. Sensors transfer data only if they sense an event. If an event is sensed in the time of an update interval, a sensor reports the event
to the sink by transmitting consecutive messages. Authors uses the parameter
event-reporting frequency to modify how often a sensor node transfer event re240
ports when phenomenon is in its sensing range. On the other hand, the sink
makes use of a continuous data delivery model, by transporting periodic queries to the sensors. Likewise, ART uses query-reporting frequency, as a simulation parameter to sustain traffic load in downstream direction. It handles with only
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a subgroup of the huge number of sensor nodes accordingly letting a decrease
in complexity in cases of loss message recovery. To acclimatize to the essential characteristics of upstream (sensors-to-sink) and downstream (sink-to-sensors) traffic, authors proposed an asymmetric method, making use of ACK and NACK methods. Reliability necessities for both sink-to-sensor and sensor-to-sink data transfer that is addressed by existing protocol [14, 15, 42, 5]. It achieves these properties using energy aware classification of sensors and using NACK and
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250
ACK based mechanisms. In ART authors proposed an energy-aware sensor classification algorithm to create a network topology that is consists of sensors in providing required level of bidirectional reliability. Firstly, authors proposed an energy-aware node classification algorithm, which is a weighted-greedy algo-
255
rithm to find a set of sensors, named as essential nodes. For this requirement, weight function is well-defined to signify the weight of a sensing area of a sensor 10
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on the basis of its remaining energy. The proposed algorithm takes into account the residual battery power on the sensors to facilitate sensors running short on battery has a lesser chance of being essential. It provides a substantial flexibility 260
for controlling the existing energy in the network between all the sensors, therefore providing a larger network lifetime. The reliability of ART is constructed upon the classification of sensors as essential (E) nodes and non-essential (N) nodes. So as to choose the set of E-nodes, authors maintained a coverage set,
represented by C, to which E-nodes belong. Then it uses a greedy approach to 265
discover an approximating coverage set running in polynomial time. Though, ART mechanism is diverse in two aspects. Primary, it does not require a con-
nected set, subsequently N-nodes can still be used to forward the data packets. Secondary, it selects the coverage set of sensors to increase the benefit in terms of coverage, specifically, the major uncovered sensing area is covered with the 270
smallest number of sensors. Consequently, ART mechanism is used to cover the whole area with least number of sensors having more remaining energy. ART mechanism also defined an energy-aware greedy algorithm to locate a nearby optimal coverage set. In every phase, it chooses one node from the unselected
sensors which covers the largest area with maximum remaining energy level.
For this requirement, weight function is used to characterize the weight of a
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275
sensing area of a sensor based on its remaining energy. In addition, congestion control mechanism is incorporated to improve network performance by saving and balancing energy that leads to increased network lifetime. It is asymmetric in the way it deals with event (upstream) reliability and query (downstream) 280
reliability. Reliability is established in the sense that queries from the base sta-
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tion are acknowledged by a lesser number of sensor nodes that can cover the whole sensing area and critical event data is received by base station. It classifies nodes into essential nodes (E-nodes) and non-essential nodes to
ensure reliability. Essential nodes are selected in a way that they cover the
285
entire sensing region. So, selection of essential nodes ensures that event is identified by as a minimum one essential sensor node and query of base station affects entire sensible terrain. A periodic weighted greedy mechanism is used 11
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at sink node to select essential nodes. This mechanism selects nodes based on their residual energy and their sensing region from uncovered area. Each time 290
essential nodes are updated, nodes with higher energy levels are selected so that energy consumption is fair among all sensors. Set of essential nodes is updated
locally and globally in the network. When an essential node fails, then new
essential nodes are selected locally and global update of all essential nodes is done after predetermined update intervals. 295
ART is mainly comprised of three different key roles: reliable query transfer, reliable event transfer, and distributed congestion control. Reliability of event
transfer is directed from E-nodes to sink node provided by using ACK mechanism. Whenever a new event occurs, E-node sends the event alarm message
having Event Notification (EN) bit set. When sink receives this alarm message 300
with EN bit set, it sends the ACK for that message. If E-nodes do not receive
ACK within certain time interval, then E-nodes re-transmit the event alarm message.
Functionality of query reliability is well-defined to be accomplished when each query of the sink is acknowledged by those sensors that cover the complete 305
sensible terrain inside the region of deployment, which is essential and adequate
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for sink-to-sensor route reliability. Reliability of query transfer that is from sinkto-sensors is done by means of negative acknowledgments (NACK). Queries are
sent in sequence number by the sink, so any query loss can be detected by essential nodes using sequence numbers. Essential nodes send NACK message 310
if any out of order query is received. Though, lost query messages can be distinguished when E-Nodes obtain a new query message. This might affect in
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two problems. Initial, damage of the last query message cannot be perceived. Reflect the last message qk with sequence number k is lost. E-Node might not deal with the lost message since there is no successive query. Next, the
315
query transmission frequency might be very low such that lost queries cannot be recovered before timeout mechanism. To detect the loss of final query message and other losses before timeout when query frequency is very low, a Poll/Final (P/F) bit is used. When query is last query or next query will be sent after 12
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a long time, P/F bit is set. Essential nodes send ACK for query with P/F
bit set to indicate successful reception of query. If ACK is not received, Sink re-transmits the query until ACK is not received.
In ART authors uses Congestion Timeout (CTO) for congestion detection,
that is dynamically determined on the basis of Round Trip Time (RTT) like adaptive retransmission timeout in TCP. Suppose that all sensors have an ini325
tial RTT that is the interval among the time after the message is sent and the time when the ACK of the message is acknowledged at the sender. Then,
RTT is calculated dynamically on the basis of latest RTT by means of time
stamp field. Sensors emphasize the time information in their messages sent back through ACKs by the sink. Therefore, E-nodes can control the RTT by 330
relating the time stamp acknowledged by ACK. Congestion control in ART is
done in a distributed manner by using E-nodes. Congestion detection is done on the basis of reception of ACK of event message. After classifying sensors as
essential and non-essential sensors, the primary purpose of asymmetric acknowledgement (ACK) message and negative acknowledgement (NACK) message is 335
to provide end-to-end reliable communications and signaling between E-nodes
and the sink node. For event reliability, ART uses lightly-loaded ACK mecha-
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nism among the E-Nodes and the sink node. Though, using an ACK mechanism
that needs acknowledgement for every message may result ineffective usage of battery power, which is measured to be a very scarce resource in WSNs. In 340
ART, congestion control is managed by the E-nodes in a distributed way. It is positioned on monitoring the ACK packets of event reports. If ACK message is not received by essential node within timer, E-node sends Congestion Alarm
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(CA) message to neighbouring N-nodes, N-nodes that receive CA message will stop sending their data to sink node. Then Event message is re-transmitted, if
345
ACK is still not received before timeout period then CA message is again sent by increasing hop-count value. Each node that receives CA message stops sending data and decreases hop-count. Congestion Alarm message is forwarded till hop-count becomes 0. When ACK is acknowledged, E-node sends Congestion Safe (CS) message having same hop-count value that is of last congestion alarm 13
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message directed by essential node. All nodes that receive CS message resume
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350
their normal operation. Thus, regulation of excessive traffic for congestion control is done by reducing the number of sensors that send their information to sink node [37].
3.2. Drawbacks of Existing Protocol 355
We have formerly explained the working of an existing protocol in preceding part. In this part, we discuss major drawbacks of competing existing protocol, which are enhanced by proposed protocol.
3.2.1. NACK based Query Reliability Problem
NACK centered methods suffer from all-packet-lost and last-segment-lost 360
difficulties since the reception node does not have any method to identify the occurrence of some missing segments except some segment that reach the node
which has a sequence number greater than estimated [33]. Subsequently ART
protocol is founded on NACKs; there can be difficulties of all-packet-lost and
last-segment-lost. ART protocol does not address the query reliability problem 365
because reliability is ensured by using NACK message for missing sequenced
query. To indicate last query, Poll/Final bit is used and an ACK is expected
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for the last query. From the start of transmitting query from the sink node to the essential node, working condition like sensor node is either switched off or
temporarily shut down and inputting process is not verified. If an E-node is 370
temporarily switched off while receiving queries from sink, it will not be able to send ACK for query with P/F bit set and after specified time interval, sink node will gain send query until the ACK message is not received. This affects
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in more energy consumption of sensor nodes and increases delay in the network. 3.2.2. Congestion Mitigation Problem
375
ART protocol does not address the congestion mitigation problem efficiently
because congestion control method used by ART uses Congestion Alarm and Congestion Safe signaling messages to handle congestion. Reception of ACK message of event reporting message is used as congestion detection. If ACK 14
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message is not received for an event reporting message within a timer greater than round trip time, then E-node sends Congestion Alarm (CA) message to
neighbor non-essential nodes. Consequent to reception of this Congestion Alarm message, those immediate nodes pause sending messages. After certain fixed
time interval, if the congestion is still no more alleviated, then essential node will resend the congestion alarm message with increased hop-count. This con385
gestion alarm message is forwarded till hop count becomes 0. As soon as the ACK message is acknowledged, it sends a Congestion Safe (CS) message to the
neighbor non-essential nodes with latest hop-count value and those nodes start
sending messages. The present method of congestion control does not use rate
control system. The usage of CA and CS messages reduces the average data 390
transfer ratio in case of heavy traffic and for more number of sensor motes. The
present congestion control method similarly presents delay in the network as no non-essential node be able to deliver messages that have received CA messages. Similarly, the usage of external messages to inform congestion dissipate network
energy and extra memory is required to store data values. Therefore, we require 395
a congestion control method that does not use external control messages rather than rate control mechanism to optimize values.
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3.2.3. Bandwidth Allocation Problem
Existing competing protocol does not address the issues of fairness among sensor nodes in terms of bandwidth allocation. In ART, packets of each source 400
node are routed independently and can follow various available routes to the sink. Hence due to multi-hop forwarding scheme, nodes nearer to sink will
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achieve more delivery ratio when compared to nodes that are far away from sink. Thus, unfairness towards the senor nodes that are distant from sink in ART protocol must be prevented so that all source nodes that share the same
405
downstream traffic jam would have equal network bandwidth allocation.
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Table 1: End-to-End Delay and Drop Packet Ratio of existing protocol.
No. of sensor nodes
End-to-End Delay (secs.)
Drop Packet Ratio
50
6.62
0.23
7.85
0.26
8.39
0.31
11.13
0.39
13.52
0.49
100 150 200 250
3.2.4. End-to-End delay and Drop Packet Problem
ART is simulated on mobile sensors having a maximum speed of 20 m/s. ART works good under lightly loaded traffic; having event reporting frequency 1 secs. and query reporting frequency 10 secs. For this, drop packets is below 410
20% and end-to-end delay is 1.5-6.5 secs. In the following situations, drop packet
ratio, latency and energy consumption of ART are increased in the network like when we increase the number of sensor nodes in the network and when packet
load increases with the increase in number of events and queries per second. End-to-End delay and drop packet ratios in four scenarios having heavily loaded 415
traffic with event reporting frequency 0.1 secs. and query reporting frequency
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2 secs. are shown in the Table 1.
ART does not address this issue and needs an improvement in end-to-end delay, drop packet ratio and energy consumption for increased number of nodes. ART has not any accurate rate control method. Therefore, it depends on the 420
nodes to manage their own data transmission rates.
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3.3. Proposed Protocol
In the proposed solution, we use optimal path selection for transmission,
priority based fair bandwidth allocation and rate adjustment-based congestion control to increase network throughput [32]. The proposed method selects the
425
optimal paths to transmit the sensed data and guarantees reliable data transmission between sensor nodes and the sink node. In this paper, we use a optimal
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path selection method to remove threats to the path to deliver both reliable
data transmission and over-all energy conservation. In the proposed method, the routing path is created by considering the energy required for data transmis430
sion, loss rate and delay. The optimal path in the sense is the path with lowest energy and hop. The emphasis is to decrease the path breaking and to surge the network lifetime. First, essential nodes are selected based on their sensing region and remaining energy. Selected essential nodes work as coverage set of
entire sensing field. Then we use Technique for Order Preference by Similarity 435
to Ideal Solution (TOPSIS) [40] approach to select optimal transmitting path
from the various alternative paths between any essential node and sink node.
Evaluation of various alternative paths is performed on the basis of metrics like remaining energy, and loss rate and delay. Then the optimal path is selected.
TOPSIS technique is a standard method to multiple criteria decision making 440
(MCDM) and has been broadly used in the existing methods. In real-world operations, we need to choose one output, which will fulfill the diverse objectives
to some range. Such a resolution is termed as finest compact solution. TOPSIS technique has the capability to recognize the finest alternate from a restricted
set of choices rapidly. It built upon the idea that the selected alternate must have the minimum distance from the optimistic perfect resolution and the ex-
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445
treme from the adverse utmost solution. TOPSIS can integrate relative weights of standard significance. To check the working condition of a node in order to increase query reliability, an initial control message is sent by sink to all nodes before sending queries. Then to control congestion, we use priority based rate 450
adjustment approach that also ensures fairness in terms of bandwidth allocation.
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3.3.1. Optimal Path Selection using TOPSIS Approach After selecting essential nodes, each essential node uses TOPSIS approach
to select optimal path to sink when data is to be transmitted. Let Pe,d represent the routes from essential node e to sink node d, where
455
k k Pe,d is the kth route. In case node j is on route Pe,d , then represent it as j ∈
k Pe,d . Infer Psk0 sn is formed by s0 → s1 → s2 → . . . → sn . Then the evaluation
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of route Psk0 sn is done on the basis of following parameters:
1. Delivery success rate: Let P LRj,d be the Packet Loss Rate on single-hop
connection between node j and its nearby sink node d. Next the Delivery Success Rate DSRks0 sn of Psk0 sn is calculated as given in Eq. (1): DSRks0 sn = 1 −
n−1 Y
m=0
(1 − P LRsm sm+1 )
(1)
2. Delay: Let Dj,d be the Delay of single-hop connection among node j and
its nearby sink node d. Later delay Dsk0 sn of Psk0 sn can be calculated as the aggregate of delay among all the nearby nodes on it as given in Eq. (2):
Dsk0 sn
=
n−1 X
Dsm sm+1
(2)
m=0
3. Hop count: Hop Count (HC ) has also influence while selecting optimal route. Value of hop count is given in Eq. (3):
HC ks0 sn = N f or Psk0 sn : s0 → s1 → s2 → . . . → sn
(3)
4. Energy: Transmitting possibility and residual lifetime of wireless sensor networks directly depends on residual energy of nodes on route P ks0 sn . If
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any node on route Psk0 sn dies out as a result of drained energy, then route
Psk0 sn will be unsuccessful to finish its communication. Let RE sm be the Residual Energy of node sm . Then, residual energy RE ks0 sn of route Psk0 sn depends on the minimal residual power of any node on it. Therefore, RE ks0 sn can be calculated as given in Eq. (4):
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RE ks0 sn = min{RE sm |m = 0, 1 . . . , n, s ∈ Psk0 sn }
Let TPsk s
0 n
(4)
is the calculated outcome of route Psk0 sn while TOPSIS is imple-
mented on entire above metrics. Then TPsk s is as shown in Eq. (5): 0 n
TPsk s = F (DSRks0 sn , Dsk0 sn , HC ks0 sn , RE ks0 sn ) 0 n
(5)
F represents the evaluating function of TOPSIS having four parameters delivery success rate, delay, hop-count and residual energy and gives the evaluated value 18
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TPsk s . Among all the alternatives, the one with maximum value of TPsk s is
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460
0 n
0 n
the considered the optimal one.
3.3.2. TOPSIS evaluation mechanism
Following steps are followed for TOPSIS evaluation: i. A decision matrix M = [mi,j ]P N s 465
tributes, i.e.
DSRks0 sn ,
0 sn ×4
k 1/Ds0 sn ,
is constructed to optimize four at-
1/HC ks0 sn , RE ks0 sn . Every Row of M
correlates to a single alternate route Psk0 sn where P N s0 sn is the number of the alternate routes among node s0 and node sn .
ii. Then, normalize the matrix M and obtain a new matrix M 0 = [m0 i,j ]P N s Matrix can be normalized as given in Eq. (6): sX m0 i,j = mi,j / m2i,j
0 sn
×4 ,
(6)
j
iii. Next, assign a weight to each attribute according to the weight age of im-
portance of each attribute in decision making and multiply each column
of matrix M 0 with its assigned weight wj to build the weighted controlled selection matrix. Then weighted controlled selection matrix R is given in Eq. (7):
(7)
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R = [ri,j ]P N s0 sn ×4
Where ri,j = wj × m0 i,j
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iv. Determine the ideal solution R+ = rj+ 1×4 , rj+ = maxri,j , i = 1, 2, . . . P N s0 sN and negative ideal solution R− = rj− 1×4 , rj− = minri,j , i = 1, 2, . . . P N s0 sN .
Where R+ specify the best desirable alternate and R− the minimum desir-
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able alternate.
v. Determine the difference between every alternate route from optimistic best resolution and undesirable perfect solution. The difference of any alternate route from positive best resolution is calculated as given in Eq. (8): v u 4 uX 2 + ri,j − rj+ di = t j=1
19
(8)
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The difference of any alternate path from undesirable perfect resolution is calculated as given in Eq. (9):
v u 4 uX 2 t d− = ri,j − rj− i
(9)
j=1
vi. Determine the relative nearness of each alternate path Psk0 sn relating to ideal solution as shown in Eq. (10):
TPsk s = 0 n
d+ i + d− i + di
(10)
vii. Rank the order of alternative paths according to value of TPsk s and a path 0 n
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Psk0 sn with highest value of TPsk s is selected as optimal path. 0 n
Two packets are used for selecting optimal path, i.e. PREQ and PREP.
Let an essential node e broadcasts a PREQ packet to find a path to sink
node d when a packet is to be transmitted and there is no existing path between e and d. PREQ packet contains fields for TPe,d of current path P ke,d , minimum k 480
energy, delay, and loss rate record.
When a node receives PREQ packet, it will receive link information from PREQ and update reverse routing table if:
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a) There is no existing reverse path.
b) Sequence no. of PREQ packet is greater than previous PREQ packet. 485
c) TPe,d is greater than stored in reverse routing table. k Moreover, it broadcasts the updated PREQ packet to find out the remaining path to destination.
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When PREQ packet reaches sink node d, it replies with PREP packet. When
an intermediate node receives PREP packet, it calculates TPe,d based on ink
490
formation passed in PREP packet, selects the optimum route and then sends updated PREP packet in reverse direction. Essential node e receives all PREP packets and selects the optimal path
based on TPe,d k value.
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After selecting an optimal path, data is transmitted along that path. Further, path selection is done periodically to remove drained nodes and corrupt routes, which need to be informed in timely manner. Fresh paths might direct
communications to improved routes in direction to extend the life-cycle and increase the throughput.
3.3.3. Reliable Event Transfer 500
After selection of optimal path, Each E-node sends the event data on selected optimal path, which increases the average delivery rate and network lifetime.
Algorithm 1 Reliable Event Packet Data Transfer from Sensor Node to Sink Input: Specified a network of sensor as G. Any Essential-Node is directing an Event-Alarm message pEA i ; set timeout period tout ;
1. Essential-Node: If pi = pEA i , fixed the Event-Notification bit, and check if optimal path is selected. If no optimal path selection is done before,
initiate the path selection based on TOPSIS approach. Send message to the sink on optimal path, begin clock and store pEA till an ACK is granted. i Else, forward message to sink node and remove it from memory.
2. Sink-Node: Deliver a ACK on reverse optimal path if it gets a pEA i .
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3. Essential-Node: If no ACK is granted within timer for pEA , then transi mit again pEA and reset clock. i
3.3.4. Solution to NACK based query reliability problem In our proposed protocol query reliability is increased by using optimal path
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for query transfer. Queries are sent on selected optimal paths between essential
505
nodes and sink. If no optimal path exists, then sink initiates the path selection to corresponding essential node and selects the optimal path. Next, sink sends the initial query control message to check the working condition of nodes and waits for ACK from essential nodes. If ACK is not received from an essential node, then sink retransmits control message after time-out period. After receiving
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Algorithm 2 Reliable Query Transfer Input: Specified a network of sensor as G; Sink-Node consists of number of queries Q = [q1 , . . . qi , qi+1 , . . . ]
1. Sink-Node: Check if optimal path exists. If not, select one based on TOPSIS approach.
2. Send initial query control message to all the Essential-nodes and wait for an ACK from each Essential-node.
3. If no ACK is acknowledged from an Essential-nodes, Sink will again send query control message after time-out period.
Once all the ACKs are received from the Essential-nodes, start regular process of sending the queries in increasing order: 1, 2, . . . i.
4. Essential-Node: Get the information for qi . Verify the order of sequence number for failure finding.
5. Essential-Node: If a difference is found in the sequence numbers, deliver a NACK to recover the missing communication.
6. Sink-Node: Resend qi−1 if a NACK is acknowledged.
7. Essential-Node: While the queries are acknowledged in order, verify if P/F bit is fixed, and then deliver an ACK to the sink node.
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8. Sink-Node: Resend the communication with P/F bit fixed till the ACK is acknowledged.
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ACKs from all essential nodes, sink will send queries. Our proposed protocol is enhanced when compared to traditional protocol since our proposed protocol
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uses ACK/NACK based scheme for achieving query reliability instead of NACK based scheme used by existing traditional protocol. 3.3.5. Solution to congestion alarm and congestion safe based congestion miti-
515
gation problem
In our proposed solution after transmission of data on optimal path, and in
order to reduce congestion, we measure congestion by means of average packet
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inter-arrival time and average packet service time. Here, packet inter-arrival time is the average of interval period between the arrival of two packets either
from source traffic or from routed traffic and the average packet service inter-
val is the average time between a packet arrival at MAC layer and successful transmission of final bit of packet. Many times, in wireless sensor networks packets are routed around the congested area and scatter the excessive number
of packets among the multiple paths. It is vastly likely that the congestion is occurred by burst traffic. For example, the sensor nodes will produce transient
bursts of traffic, when the unexpected events arise. When burst happens close
to node v, the queue length of this node might be low, nevertheless it is clear
that node v is not a suitable destination for the source nodes to send the packet,
as a lot of packets will arrive v due to the burst nearby it and the queue length field cannot differentiate this problem. To solve this problem, we describe the
congestion degree field. Hence, Congestion Degree (cd) is calculated as the per-
centage of average packet service time over average packet inter-arrival time in a quantified interval period for every sensor mote k as shown in Eq. (11): cd(k) = tservice (k)/tinter
arrival (k)
(11)
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The calculated value of congestion degree specifies the level of congestion of the
network. Once the congestion is sensed, it is reported using implicit signaling. On reception of congestion notification signal, the transmission rate is controlled so as to decrease the congestion. Congestion degree can be calculated on each node packet-by-packet basis. It represents congestion time of every sensor node. Moreover, it effectively points out at both node-level and link-level congestion.
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To update the congestion degree, packet inter-arrival time and packet service time are calculated by exponential weighted moving average (EWMA) algorithm [16]. Here, tinter
arrival (k)
is updated periodically whenever N new packets
arrives as shown in Eq. (12): tinter
arrival (k)
= (1 − αia ) ∗ tinter
23
arrival (k)
+ αia ∗ TN /N
(12)
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where 0 < αia < 1 is a constant, TN is the time over which N packets arrive
and tservice (k) is updated whenever a packet is forwarded as shown in Eq. (13): tservice (k) = (1 − αs ) ∗ tservice (k) + αs ∗ t0service (k)
(13)
where 0 < αs < 1 is a constant, t0service (k) is the service time of packet communicated.
When congestion degree cd(k) is greater than 1, service time will be larger
than packet inter-arrival time, and then buffer overflow occurs. Otherwise, 520
when service time is less than packet inter-arrival time, congestion degree will
be less than 1, congestion fades away. We use implicit congestion notification to reduce additional control packets. Each sensor mote piggybacks its congestion degree in packet header of outgoing packets. Due to broadcast nature of wireless
channel, this information is heard by all the child motes. Finally, the scheduling 525
rate is increased or decreased on the basis of congestion degree. Our proposed solution is better than existing solution because our scheme uses rate adjustment
based congestion control mechanism that has higher delivery ratio and reduced delay and energy consumption instead of congestion alarm and congestion safe
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messages used by existing protocol.
3.3.6. Solution to bandwidth allocation problem In order to ensure fair bandwidth allocation, all essential nodes are assigned an equal priority index 2 and all non-essential nodes are assigned with priority index 1, so that essential nodes are having higher priority than non-essential nodes. Thus, all essential nodes with equal priority index have equivalent bandwidth [38]. A global priority index of each node is calculated as the total of
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global priority of entire child nodes and priority of itself. If there are no child nodes, global priority is equal to priority of node itself. Global Priority (GP ) of any sensor node k can be computed as shown in Eq.
(14):
GP (k) =
X
GP (child node(k)) + priority(k)
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(14)
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Each packet includes its global priority in packet header. When congestion occurs, scheduling rate of nodes is reduced based on global priority index to 540
mitigate congestion, which leads to fairness. Our protocol provides better solu-
tion for bandwidth allocation problem by assigning the equal priority to essential and non-essential nodes, which ensures fairness. 3.3.7. Steps of Proposed Method
1. Select Essential nodes based on residual energy and sensing region and 545
assign priority equal to priority index to each essential node.
2. Select optimal path between essential nodes and sink node using TOPSIS approach.
3. Transmit queries and event on selected optimal path.
4. Piggyback congestion degree and global priority index in each forwarded 550
packet header.
5. If congestion degree is greater than previous congestion degree, this means
that congestion is increased, and then child node reduce the scheduling rate based on global priority of parent node and itself as shown in Eq. (15):
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Rsch(i) = Rsch(i) ∗ GP (i)/GP (pi)
(15)
6. If congestion degree is less than 1 and lesser than previous congestion degree, then scheduling rate is increased to utilize bandwidth as shown in Eq. (16):
Rsch(i) = Rsch(i)/cd(pi).
(16)
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Update coverage set periodically.
4. Performance Analysis In this section, we have explained the performance investigation of recom-
mended BRCCTP protocol and the traditional protocol. We have implemented
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packet oriented simulation. The current method works by altering the technique
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a prevailing method works at the sensor nodes and sink node. We have com-
bined a few functionalities equally at the sink node and sensor nodes to enhance the algorithm.
4.1. Performance Metrics 560
Comparison has been made with recommended BRCCTP protocol with a prevailing protocol on the foundation of seven metrics. These metrics are described as follows.
4.1.1. Average delivery ratio
It describes the capability of the protocol to effectively allocate the data
packets to last destination inside assured interval. It is the proportion of sum of messages a destination node received to the number of messages a source node injects into the network. We have computed the average of the delivery ratio
of number of simulations to acquire the average delivery ratio. Mathematically, average delivery ratio is given by: Average Delivery Ratio =
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4.1.2. End-to-End Delay
Number of packets received by destination node Number of packets sent by source node
It represents the time reserved by a packet to reach the destination node from source node. Specifically, the time change among the time when the packet arrives at the transport unit of the receiver and the time when the packet was transmitted by the transport unit of the sender. 4.1.3. Average Delivery Overhead
Average delivery overhead is measured as the sum of messages sent each
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data message acknowledged by a destination node. This parameter inspects the transmission cost to accomplish consistent transfer of packets over the network. We average the delivery overhead of some simulation results to find the average delivery overhead.
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4.1.4. Drop Packet Ratio
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Drop Packet Ratio is determined as the ratio of the number of packets drop
to the number of packets generated by data sources. A protocol with less drop packet ratio is more reliable. Mathematically drop packet ratio is given by: Drop Packet Ratio = 4.1.5. Queue Length
Number of Drop Packets Number of sent packets
Queue Length determines the size of queue at each node. Given method
depends on the current node’s message queue holding. When the qualified number of queue up packets exceeds a definite pre-mentioned level (e.g. 75%), 580
the node is measured to be congested, and will continue to alert its peers of that
situation. Therefore, to control congestion, every node identifies congestion by observing its queue occupancy. The congested node then basically piggybacks a congestion degree together with its data, therefore winning benefit of the
transmission environment of the wireless medium to transmit its congestion 585
degree to its neighbors. 4.1.6. Energy Efficiency
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It is described as the ratio of the summation of residual energy of nodes to the summation of preliminary energy of nodes. Mathematically, Energy Efficiency =
Sum of residual energy of nodes Sum of initial energy of nodes
A network with high energy efficiency would have high network lifetime as compared to the one with less energy efficiency. So, protocol must achieve high
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energy efficiency.
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4.1.7. Fairness Index
To measure fairness, we use popular fairness Index that is Jain’s Fairness
Index [18] which is calculated as Pn 2 ( i=1 ri ) Fairness Index (φ) = Pn 2 n ( i=1 ri ) 27
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Where n is the number of sensors in the network and ri is the average rate
of packets delivered from the ith sensor. Fairness index lies between 0 and 1.
If average percentage of packets transported from each node is same, fairness index would be 1. 595
4.2. Simulation Setup
Proposed BRCCTP protocol and the traditional protocol are tested using
NS2 simulator [17]. For Simulations, sensors are placed randomly in a square area. Each sensor is having a fixed sensing range r. Two sensor nodes can
communicate with each other if they are in communication range of each other. 600
A sensing field of 300 × 300 m2 is required for simulations. We simulated the network with sensor nodes 50, 100, 150, 200 and 250 so that we are able to eval-
uate the performance of our protocol from very sparse to large dense networks. Used figure of sensors must be adequate to bind the sensing area for specified
metrics. Wireless sensor nodes are taking communication range of 90m and 605
sensing range of 60m are used. Initial energy of sensors is 3 Joules (J). In these experiments, an action is characterized to identify the phenomenon node in the recognizing area of a sensor.
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A data delivery model is based on event-driven and is used to transmit the
data between the sensor nodes and sink node. When an event is detected, 610
sensors send data. If an event occurs during the update interval, then sensor nodes report event to sink node by transmitting successive messages. Event reporting frequency metric is used to modify how often a sensor node directs event reports to sink after phenomenon occurs in its sensing area. Sink is in the
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center of the sensing field. For routing, AODV protocol is used and for MAC, 615
CSMA/CA protocol is used. Table 2 lists the parameters for the simulation. Let us consider scenario of 50 sensor nodes and a sink node 0 as shown in
Fig. 1.
Out of these 50 nodes, essential nodes are selected based on their residual
energy and sensing region. Nodes with grey color filled indicate the essential
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nodes and dotted circles are used to indicate the sensing region boundary of each 28
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Table 2: Parameters for Simulation.
Sensing field area
300 × 300 m2
No. of sensor motes
50, 100, 150, 200, 250
Time of Simulation
150 secs.
Packet length
100 bytes
Interface queue length
50
Sensor node radio range
90 m
Sensor node sensing range
60 m
Idle power
13 mW
24 mW
Receive power
13 mW
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Transmit power
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Figure 1: TOPSIS based 50-node Network Topology.
essential node and remaining nodes with no color filled indicate the non-essential nodes. Union of sensing region of essential nodes cover the entire sensing area. Now each essential node selects an optimal path between itself and sink node by implementing TOPSIS method out of various alternative paths based on
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the following attributes: hop count, delay, delivery success rate, and residual
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Table 3: Comparison of Average Delivery Ratio for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
BRCCTP
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs. fe=1 secs. fq=2 secs.
fq=2 secs.
fe=0.5 secs. fe=0.5 secs. fe=1 secs. fe=1 secs.
50
0.768
0.796
0.797
0.828
0.878
0.883
100
0.741
0.761
0.733
0.791
0.846
0.864
150
0.686
0.722
0.714
0.761
0.879
0.871
200
0.604
0.681
0.692
0.746
0.835
0.862
250
0.507
0.579
0.646
0.696
0.802
0.838
energy. Selected path from each essential node is indicated with red arrows and any node that is on optimal path from essential node to sink node is colored with red boundary.
4.3. Simulation Outcomes 630
Simulation has been carried out for ART protocol and the proposed BRCCTP protocol using NS-2 simulator. The network is visualized using NAM
(Network Animator). To do pursuance calculation of ART and BRCCTP pro-
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tocol, seven metrics are considered; these are average delivery ratio, end-to-end delay, average delivery overhead, drop packet ratio, queue length, energy effi635
ciency and fairness index.
4.3.1. Average delivery ratio
Table 3 shows result of ART and proposed BRCCTP in the aspect of average
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delivery ratio for various network size and traffic loads. We have simulated three different traffic loads by variable event-reporting frequency (fe ) and query
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reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs., moderate: fe = 0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Every
test is executed ten times and the outcomes presented are an average of these execution.
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Figure 2: Comparing Average Delivery Ratio for Diverse Traffic Loads.
Figure 3: Comparing End-to-End Delay for Diverse Traffic Loads.
As we observe in Fig. 2, our procedure aside from resolving the difficulties
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with ART accomplishes considerable improvement over the prevailing protocol. When the size of network increases, the total number of packets acknowledged
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Table 4: Comparison of End-to-End Delay for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs.
BRCCTP
fe=1 secs.
fq=2 secs.
fq=2 secs.
fq=5 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
6.621
5.901
2.483
2.082
1.594
1.432
100
7.854
6.892
4.124
3.447
1.985
1.761
150
8.392
7.287
7.891
6.218
2.692
2.234
200
11.131
9.766
10.014
8.183
4.236
3.653
250
13.522
12.092
12.431
10.786
6.298
5.202
at the receiving node diverges. Higher amount of data packets is acknowledged
by receiving node just in case of proposed BRCCTP protocol. The reason of this is that our BRCCTP protocol uses rate adjustment to optimum rate for 650
congestion control. The prevailing protocol Congestion Alarm and Congestion Safe based congestion control method decreases the communication ratio to zero
after a node’s buffer touches the threshold; because of this not any packets can be sent to that node. That decreases delivery ratio for ART protocol. Proposed
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protocol accomplishes greater average delivery ratio. 4.3.2. End-to-End Delay
End-to-end delay indicates how much interval of time is used by packet to reach the destination. The comparison results of end-to-end delay for existing and projected protocol for three diverse traffic loads are shown in Fig. 3. Table 4 shows end-to-end delay for existing and proposed protocol for three different traffic loads.
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It is inferred from Fig. 3 that the BRCCTP protocol has less end-to-end de-
lay than the existing protocol. This improvement in end-to-end delay is because delay of path is used as a selection parameter in TOPSIS approach while choosing optimal path and the one with minimum delay and hop-count is selected.
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While in ART protocol, packets are independently routed and can follow any
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available path. Consequently, delay in packets transmitted on optimal path is
less as compared to existing protocol. Delay of proposed protocol is also reduced
due to congestion control using rate adjustment to optimal value. Body-area networks can gather information about a person’s health, fitness, and energy 670
expenditure. In health care applications the confidentiality and legitimacy of user data has prime importance. Particularly due to the addition of sensor net-
works, with IoT, the user authentication becomes more challenging. End-to-end
delay of proposed protocol is reduced even when node density is equal to 250 and having high traffic load when compared to ART protocol. 675
4.3.3. Average Delivery Overhead
Here, we find transmission rate for reliability in the two systems in numerous
channel environments with a 3-hop network. Transmission rate is determined as the average amount of communication for each data packet. It is known as
average delivery overhead. Our proposed BRCCTP protocol also has reduced 680
communication overhead as compare to ART protocol because our protocol requires less number of per data packet transmission as compared to existing
protocol. Table 5 displays the result of ART and BRCCTP protocols in relation to average delivery overhead for diverse network size and traffic loads. We have
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simulated three different traffic loads by variable event-reporting frequency (fe ) and query reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs.,
moderate: fe = 0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Every test is executed ten times and the outcomes presented are an average of these execution. As we observe in Fig. 4, the rate for BRCCTP is constantly
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lesser than ART protocol in relation to average delivery overhead for different 690
network size and traffic loads. Hence, the proposed protocol chooses the optimal path for data transfer using TOPSIS approach based on various parameters including delivery success rate of the path that provide reliable delivery of data packets to the destination with less number of packet drops.
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Table 5: Comparison of Average Delivery Overhead for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs.
BRCCTP
fe=1 secs.
fq=2 secs.
fq=2 secs.
fq=5 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
4.2
1.8
3.6
1.5
2.9
1
100
4.9
2.1
3.9
1.7
3
1
150
5.4
2.7
4.3
2
3.3
1.3
200
6.1
3.2
4.8
2.4
3.6
1.5
250
6.8
3.5
5.1
2.6
3.9
1.8
4.3.4. Drop Packet Ratio 695
Drop packet ratio measures the number of packets that never received by the destination. The comparison of drop packet ratio for existing and proposed
protocol for three different traffic loads is given in Fig. 5. We have simulated three different traffic loads by variable event-reporting frequency (fe ) and query
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reporting frequency (fq ): heavy: fe = 0.1 secs. and fq = 2 secs., moderate: fe =
Figure 4: Comparing Average Delivery Overhead for Different Traffic Loads.
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Figure 5: Comparing Drop Packet Ratio for Different Traffic Loads.
Table 6: Comparison of Drop Packet Ratio for Diverse Traffic Loads.
No. of ART
BRCCTP
ART
BRCCTP
ART
Nodes fe=0.1 secs. fe=0.1 secs. fe=0.5 secs. fe=0.5 secs. fe=1 secs. fq=2 secs.
fq=5 secs.
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fq=2 secs.
fe=1 secs.
fq=5 secs.
fq=10 secs. fq=10 secs.
50
0.232
0.204
0.203
0.172
0.122
0.117
100
0.259
0.239
0.267
0.209
0.154
0.136
150
0.314
0.278
0.286
0.239
0.121
0.129
200
0.396
0.319
0.308
0.254
0.165
0.138
250
0.493
0.421
0.354
0.304
0.198
0.162
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BRCCTP
0.5 secs. and fq = 5 secs., light: fe = 1 secs. and fq = 10 secs. Table 6 shows the drop packet rate for prevailing and proposed protocol for three different traffic loads.
It is inferred from Fig. 5 that the BRCCTP protocol has lower drop packet
ratio as compared to the existing protocol. This is because the proposed pro-
705
tocol selects the optimal path for data transfer using TOPSIS approach based 35
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on various parameters including delivery success rate of the path. Further, it uses rate adjustment to optimum value for congestion control. So, combination
of both of these techniques reduces drop packet ratio for proposed protocol. The existing protocol uses congestion alarm and congestion safe messages based 710
congestion control mechanism to reduce traffic of non-essential nodes that does not control congestion effectively and increases drop packet ratio for existing
protocol. Drop packet ratio of proposed protocol is reduced effectively under
high traffic load with event-reporting frequency (fe ) equal to 0.1 secs. and query reporting frequency (fq ) equal to 2 secs. 715
4.3.5. Queue Length
As we observe from Fig. 6 and Fig. 7, our proposed BRCCTP protocol queue occupancy is less as compared to ART protocol. As we go away from sink node
buffer occupancy also decreases after applying the proposed algorithm. We con-
trol the queue occupancy at every node by adjusting the reporting rates among 720
sensor nodes then in turn we avoid queue overflows at those nodes. This also
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results in decrease of drop packet ratio. Hence, we overcome the congestion
Figure 6: Queue Length of ART protocol.
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Figure 7: Queue Length of BRCCTP protocol.
in sensor network. Our proposed BRCCTP protocol also has less computa-
tion overhead as compared to ART protocol because our proposed solution uses
priority based fair bandwidth allocation and rate adjustment based congestion 725
control instead of congestion alarm and congestion safe based congestion mit-
igation used by ART protocol. The queue availability can also be used as the
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reverse of queue occupancy. It characterizes the amount of packets a node might
still accept before it starts to decline arriving data. The proposed protocol emphasizes on proficient organization of queue to deliver reliability and decrease 730
packet loss. It is also inferred from Fig. 6 that most of the time in ART protocol queued packets surpasses a certain predefined threshold (75%) that causes packet loss and large delay in network. In a network, if the queue length is not
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properly maintained, the source node will continue transporting packets at the preliminary level that results in packet loss. Thus, in our proposed protocol we
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reduce transport ratio of packet transmitters formerly surpassing a determined threshold so as to avoid the packets from being discarded. In a perceptive application identical to health monitoring, loss of packet is the most significant problem that has to be reduced.
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Figure 8: Comparing Fairness Index.
4.3.6. Fairness Index 740
Fairness index measures the fairness in terms of reporting rate of all sensor nodes. The evaluation of fairness index for prevailing and proposed protocol by
varying traffic load in relation of packets per second (PPS) is depicted in Fig. 8. Table 7 shows fairness index for existing and proposed protocol at different
traffic loads. It is inferred from Fig. 8 that the BRCCTP protocol has higher fairness index than ART protocol because in our proposed protocol, an equal
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priority index is assigned to all data source nodes and a global priority index is calculated at each node. Rate adjustment to control congestion is done on the basis of global priority index of nodes that provides fair bandwidth allocation to all data sources. As traffic load that is number of Packets Per Second (PPS) 750
increases, fairness index decreases due to congestion. Nevertheless, BRCCTP
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protocol has better fairness index even under high traffic load condition than existing ART protocol. 4.3.7. Energy Efficiency Energy efficiency of network represents the ratio of remaining energy of net-
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work to the initial energy of the network. A network having high energy effi-
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Table 7: Comparison of Fairness Index.
Traffic Load (PPS)
ART
BRCCTP
0.25
0.919
0.924
0.5
0.672
0.698
1
0.437
0.542
2
0.296
0.451
3
0.248
0.409
4
0.225
0.413
ciency would have better network lifetime. The evaluation of energy efficiency
for prevailing and proposed protocol for different number of nodes is given in Fig. 9. Table 8 shows energy efficiency for existing and proposed protocol at different node densities. 760
It is concluded from Fig. 9 that the proposed protocol has higher residual
energy of network than the prevailing protocol. This is the reason that our pro-
posed protocol always chooses the path with higher remaining energy for data
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transfer using TOPSIS approach and hence maintains an energy balance in the
Figure 9: Energy Efficiency for Different Node Densities.
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Table 8: Comparison of Energy Efficiency for diverse node densities.
No. of Nodes
ART
BRCCTP
50
0.329
0.352
100
0.291
0.319
150
0.198
0.242
200
0.153
0.194
250
0.097
0.115
network and the congestion mitigation method in proposed protocol uses ratio 765
regulation strategy that manages congestion efficiently as a result of which less packets are damaged in proposed protocol and therefore, less retransmissions
and lower energy loss. Packet drops are higher in ART protocol and packets
follow in any available path, so energy consumption is more in ART protocol, which reduces the residual energy of network. Resource management is of princi770
pal importance for QoS provisioning as the equivalent resource budgets essential to be guaranteed in order to accomplish certain QoS levels. This is mainly ac-
curate for WSNs where communication, computing and energy resources are
fundamentally restricted. Resource management in WSNs is very challenging,
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as of the ever-increasing complexity of WSNs. Energy efficiency of proposed
protocol is increased even when node density is equal to 250 and having high traffic load when compared to ART protocol.
5. Conclusion
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To conclude, we propose a new Wireless Sensor Networks (WSNs) protocol called: Bidirectional Reliable and Congestion Control Transport Protocol (BR-
780
CCTP).We analyzed its performance under different scenarios and compared with the competing ART protocol.Simulation results have shown the better performance of our proposed scheme when compared to ART scheme. The BRCCTP makes use of TOPSIS approach to select optimal path and it uses ratio regulation to optimize value based on priority so as to alleviate the effect of 40
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congestion. The latter can be distributed with in an optimum method, but not
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at the price of decreased throughput and delivery ratio. Priority based rate
adjustment provides fairness among all sensor nodes. It has been shown that BRCCTP protocol performance is superior when compared to ART protocol,
especially form the point view of the average delivery ratio, end-to-end delay, 790
average delivery overhead, drop packet ratio, queue length, energy efficiency and fairness index metrics. The BRCCTP protocol is successful in decreasing drop
packet ratio, end-to-end delay, average delivery overhead and queue length and in having better average delivery ratio, energy efficiency and fairness. References 795
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Conflict of Interest and Authorship Conformation Form
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Please check the following as appropriate: o
All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.
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This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.
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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript
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The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:
Author’s name
Affiliation
Bhisham Sharma Gautam Srivastava Jerry Lin
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Brandon University
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Bhisham – Conceptualization, Data curation, Formal analysis; Gautam - Funding acquisition, Investigation, Methodology, Project administration, Roles/Writing – original draft; Jerry - Writing – review & editing.