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Priority and interference aware multipath routing based communications for extreme surveillance systems
Journal Pre-proof Priority and interference aware multipath routing based communications for extreme surveillance systems T. Murugeswari, S. Rathi
PII: DOI: Reference:
S0140-3664(19)31180-6 https://doi.org/10.1016/j.comcom.2019.11.050 COMCOM 6052
To appear in:
Computer Communications
Received date : 14 September 2019 Revised date : 26 November 2019 Accepted date : 28 November 2019 Please cite this article as: T. Murugeswari and S. Rathi, Priority and interference aware multipath routing based communications for extreme surveillance systems, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.11.050. 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. © 2019 Published by Elsevier B.V.
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PRIORITY AND INTERFERENCE AWARE MULTIPATH ROUTING BASED COMMUNICATIONS FOR EXTREME SURVEILLANCE
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SYSTEMS
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T. Murugeswari*1 and Dr.S.Rathi2
Assistant Professor/ Department of Electrical and Electronics Engineering, Hindusthan College
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Engineering
and
Technology,
Coimbatore
[email protected]
Tamilnadu,
India.
E-mail:
Associate Professor, Department of Computer Science Engineering, Government College of
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2
641032,
Technology, Coimbatore, 641013, Tamilnadu, India. E-mail: [email protected] ABSTRACT
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Increased natural disaster in various urban and rural areas requires immediate attention to avoid the major causes. In real world, immediate recovery to the disaster areas is ensured by adapting the unmanned aerial vehicles that enable people to reach the disaster areas immediately. Here specific task in the disaster area can be completed efficiently by working with multi UAV instead of single UAV. Here data communication between the UAV needs to be very reliable to ensure the proper disaster management outcome. It is more complex to provide the required services to the users when there is situation arise to switch between the heterogeneous networks. The QoS-Oriented Distributed routing protocol (QOD) is used in the existing methods to give solution to this problem. The data is transferred between hybrid networks with required QoS. In the existing work, routing is done by considering the QoS consideration thus the efficient and
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reliable distributed routing is guaranteed. However the existing work lacks from the following issues: It avoids the data transfer through the path in which data transmission is going already to avoid the interference problems which might reduce the throughput rate. Priority of the data transferred from multiple sources are not considered in the previous work and also existing work focused on reducing delay alone as QoS factor. Priority and Interference aware Multipath Routing Protocol (PIMRP) is introduced to rectify this issue in the proposed method. In this 1
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method, interference aware and priority routing is ensured by introducing the following research methods. Here, Multipath interference based routing method is used allow transmission of data from multiple path that resides within interference range with guaranteed interference avoidance and increased throughput. Here route path nodes are selected with multiple objectives such as
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delay, bandwidth, and energy consumption. To provide more preference to the prioritized data transmission nodes with more resource availability is provided to the prioritized data packets which are tiny segmented. The performance of the proposed method is evaluated using NS2
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simulation tool. The simulation results confirm the efficiency of the proposed method. Keywords: Multipath routing, Interference aware routing, throughput, QoS parameters, Hybrid wireless network.
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1. INTRODUCTION
Disaster Management can be defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters [1].
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Unmanned aerial vehicles (UAVs) can be effectively used in disaster management as per the suggestion of the National Disaster Management Authority (NDMA) [2]."UAVs can provide high-resolution, real-time images of even the inaccessible locations. These images can then be used to produce accurate hazard maps so that prevention and mitigation measures for reducing disaster risks are planned accordingly," said R K Jain, Member, NDMA. In a post-disaster situation, UAVs can be used to map the affected areas in high resolution within a short time, which, in turn, can aid swift and efficient response, he said during a national-level brainstorming session on "Application of Unmanned Aerial Vehicles (UAVs) in Disaster Management". The event was aimed to build and improve the capacity of stakeholders to use UAVs and related technologies for better disaster preparedness and response. Underlining the importance
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of UAV technology for disaster management, Jain said that all stakeholders should strive to leverage existing technologies for quick and efficient disaster response."UAVs can be effectively used in different phases of disaster management - they can help us identify areas affected by disasters.
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Hybrid wireless networks are the one in which every mobile nodes belong to a wireless network has connectivity possible either in a direct manner through a gateway over an infrastructure based network [3]. The network described in the latter part may have an IP based network as an internet or an 3G network and can also have LAN that are 802.11 bases[4]. In
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reality, any network based technique can be considered. Here, the representation of the Intra based technology and the inter based techniques are represented [5].
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In case of one node communicating with the other one in a network that uses a same technology, this can be efficiently seen as the Intra technique based Hybrid Network [6]. As an example, the node in a mobile network for an ad hoc 802.11 network that communicates with using an Access point (AP) in a network of infrastructure [7]. In the contrast, if a mobile node happens to communicate with another type of network which uses a different technique; this can
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be considered as Inter technology hybrid wireless network [8].
The progress in case of a mobile based communications thus leads to environments that are capable of accessing the networks at any point of time and from anywhere [9]. Various
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mobile applications have been developed that focuses on the performances in the transmission rate and the network capacity [10]. The expectation is that such a system in the near future will be as capable of providing the communication techniques to make the fast reaction as a possible event even in emergency cases since these are quick and more over accurate in assessing the damages which is a vital component for the fast recovery of rescue activities [11]. One of the other concerns is that the available communication systems are mostly vulnerable in the post math of the large and natural disaster owing to the congestion in the communication [12]. A few studies on the feasibility have been already carried for the collection of damage assessment and the related information for the mitigation of damages in a large disaster [13]. The work presented in [14] describes the personnel safety over the internet. The WWW is filled with
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many technologies that are very much useful for the collection of damage assessment from one city owing to the disaster incase of the interruption in the network [15]. Anyways, these have a chance of having a potential threat due to the congestion in the communication. It can never guarantee a faster response in case of disaster [16].
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Meanwhile, the WNs are generally non trivial for the disaster communications [17]. Specifically, the Ad-Hoc types of networks have drawn most attention and various protocols for the routing have been proposed in the literature. [18]. these networks can work in an efficient manner for the disaster in the relief operations that too in the facilities of the infrastructure that
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do not get operated in a sufficient manner [19]. These networks can be more flexible when the connection tends to be vulnerable for the variety of conditions. On the one hand, A network hierarchy based centralized network can be established for the effective communication due to
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the BS (Base Station), which will affect the grade of the connection [20].
In order to get the resolution to the problems mentioned above, this research work targets at introducing a Hybrid network for the wireless communications, which integrates the Ad-Hoc with the Cellular network. It is of huge help in preserving the connectivity and the availability in
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the network using the concept of multi hopping in emergency scenarios. In the proposed work, the priority and the concept of interference in routing are thereby guaranteed by using few approaches. These include the multipath interference and are dependent on the routing method that basically tries to permits transmitting data in alternative paths and which is present in a
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range ensuring avoidance and an improved throughput. In this, the path of the route are not chosen considering different aims like the delay and the bandwidth for the energy consumption, for providing higher priority to the data, present in priority data packets and are segmented in tinier ones.
The overall organization of this works is provided as below: Section 1 gives an extensive overview on the proposed system. In section 2, the techniques available in the literature are discussed. Section 3 describes the proposed method. Section 4 throws light on experimental arrangement. Section 6 and 7 discusses the result and conclusion.
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2. RELATED WORKS
Aswale et al [21] implemented a Geographic Multipath Routing (TIGMR) protocol based on minimum inter-path Interference and Triangle link quality metric. This protocol is used to find the multiple node- disjoint paths in the networks which are based on IEEE 802.15.4. Based on unused energy, distance in the presence of minimum adjacent path interference effect and triangle link quality metric the forwarding node is selected by the cross-layer routing protocol. It 4
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does not use handshake techniques like RTS/CTS (Request-To-Send/Clear-To-Send) to avoid Hidden Node Problem (HNP) in the sink node. Radi et al [22] implemented a protocol to fulfill the QoS requirements of event-based applications which is known as Low-Interference Energy-efficient Multipath Routing protocol
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(LIEMRO). This protocol uses the quality-based load balancing algorithm to optimize the resource usage in the defined paths by controlling the amount of traffic in that path.
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Li et al [23] presented a Geographic Energy-Aware non-interfering Multipath (GEAM) routing approach which partitions entire network topology into several districts. At the same time, data is forwarded through these districts with no interference with one another to carry out transmissions with no interference. The energy status of the nodes is used to adapt each district’s changing topology.
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load. Fixed paths are not created initially. So it can produce better performance in the quick
Jabbar et al [24] identified the nodes to use as MPRs to broadcast the information of topology by using Mobility Aware Multi-Point Relay (EMA-MPR) selection method. It is an
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extension of EXata network simulator. Various simulation situations and parameters of the mobility are used to evaluate the performance of the method and results show it ability. Sutagundar et al [25] used mobile and static agent’s sets to implement an event triggered multipath routing which can be used in WSNs. The location of sink and each are assumed to be known by each node. The performance evaluation parameters like energy consumption, overhead, latency and packet delivery ratio are used to evaluate the performance of the proposed routing.
Hou et al [26] defined the coding-sensitive routing protocol. Path bandwidth estimate is produced in a better manner. It is capable of identifying paths with much better throughput.
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Elaborate NS2 simulations shows this approach performs better than the available schemes. Chao et al [27] presented an Interference aware cross layer routing protocol (IA-CLR) for MANETs. It is an interference aware routing protocol and it is based on MAC layer of IEEE 802.11. IA-CLR shows the link’s interference strength by defining receiver and transmitter’s
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potential. Network and MAC layers are combined to form a cross layer which introduces a novel routing metric to choose a route with minimum bottleneck. Peng et al [28] proposed a Coding and Interference Aware Routing (CIAR) protocol to detect the probable coding possibilities. They are used to define the paths by considering cost of
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the path’s interference. It can be quite helpful in rendering the balance between enabling more coding gains and tackling interference.
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Li et al [29] improved the potential of QoS support in hybrid networks by using QoSOriented Distributed routing protocol (QOD). This protocol converts packet routing problem into resource scheduling issue by using the hops with less number of transmission and hybrid network’s transmission characteristics. The experimental results show the better performance in
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terms of scalability, mobility-tolerance, overhead and transmission delay.
Li et al [32] proposed a multi objective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and Tabu Search (TS) was designed to solve the model. An experiment optimizing rescue
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routing plans under a real urban storm event, was carried out to validate the proposed model. Manaseer et al [33] performed deeper analysis on some of the main mobility models used in testing new protocols and a new mobility model is proposed to incorporate some neglected factors concerned with disaster recovery situations.
Jahir et al [34] investigated network architecture and routing models for disaster area networks. The main objective and goal of DAN is to ensure reliable, energy efficient communication which is susceptible to mobility and topology changes in the disaster area. The purpose is to improve delay, reduce overhead, minimize energy used, sustain movement and
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increase bandwidth for multimedia applications.
3. PRIORITY AND INTERFERENCE AWARE MULTIPATH ROUTING In this research methodology, priority and interference aware routing is ensured by introducing the following research methods. Those are Multipath interference based routing method is introduced. This protocol allows transmission of data from multiple path that resides 6
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within interference range with guaranteed interference avoidance and increased throughput. Here route path nodes are selected with multiple objectives including as delay, bandwidth, and energy consumption. To provide more preference to the prioritized data transmission nodes with more resource availability is provided to the prioritized data packets which are tiny segmented. Non
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Interference in this work defines that no data will be forwarded in the same data link where there is already on-going data transmission. Optimal route paths are selected to achieve this by considering on-going data transmission. And interference is prevented by introducing the priority
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calculation where emergency data will be allowed to transfer first by pausing the on-going data transmission. 3.1. NETWORK MODEL
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Wireless network are molded by the use of graph G= (V, E) where the V represents node set and E represents the link set. All edge links which comes between nodes that are currently inside the range of transmission and this work we have assumes that this is not changeable for all the nodes. The result of the graph is named as a Unit Disk Graph (UDG). Neighbor set given by
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vi by N (vi). The path between Source Node (S) and the destination node D is represented as (S = v0, v1, v2. . .vn = D) where vi∈ V and vi∈ N(vi−1) with the length n. The path in which first choice of transmission process was carried between source and destination are termed as the primary path. All nodes are in the hybrid WNs and has a specific identifier and its geo positions are well known. All the nodes are arranged in a 2D Euclidean space where G is a graph of geometry. Further assumption is also made that the complete nodes will transmit their positions to their nearby nodes by the use of HELLO messages in a regular time interval. These are also termed as
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Beacon Messages. The network model is shown in the following figure 1.
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Figure 1. Network model of hybrid network
3.2. ROUTE DISCOVERY
Primary step for discovery of route has two steps. They are, 3.2.1. Route Request:
The S has the number of packets to get it delivered to D and it does not have the route to achieve the destiny, S then happen to send a request (RREQ). Each of the requests will be unique and
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these are identified by using the identifiers. The count of the sequence in RREQ as well as with the address in the node that originate along the RREQ. The RREQ’s header is then shown in the figure 2.
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RREQ
rreq_seq_numrreq_srcrreq_destlength
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Figure 2.RREQ header
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The more detailed RREQ header format is shown in the following figure 3
Figure 3. RREQ header format
The field that belongs to the path has the list which is intermediate type of nodes between the Source node S and the Destination node D. The list which is presented will be empty as and when the S sends the request and there after the path field has the list of all nodes that are intermediate and which is present inside the s and d. Again, the path of the field is then updated and then these are prorogated. All the nodes that are relay type will have their own address proof and are in the nodes present in-between. The list hence allow the loops that are used for the routing and also helps for building a path that returns in sending the reply for the request RRERQ1. Always the PIMRP are given training in the source and these are also tend to get used
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by the node which is termed as the source for specifying the whole in the data for all the packets. These are also called as Encapsulated Path. Collection of maximum information regarding the path that includes the info of the nodes and that of the neighbor by preserving the reliability, the nodes are then found in the nodes that lies in an intermediate position and does not allow for the reply in the RREQs which are seemed 9
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to be redundant and are hence to avoid the issues in the broadcast. As a node that seems to be intermediate and when getting the first copy of the RREQ which is a duplicate, the nodes are ensured to have the address of its own are not being any part of the list intermediate for avoiding the loops in prior to the RREQ cache that are being saved.
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At the same time, as and when the RREQs are dealt with, it facilitate search node in learning about its neighborhood. Hello packets are avoided to reduce overhead of routing. Every
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node adds the neighboring node which has transmitted the packets of it’s in its list of the neighbors. With a particular end objective to avoid out of date passages, an evacuation timeout instrument is added. After timeout, another packetis supplied from identical source.After received RREQ final goal node, RREP is transmitted.
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3.2.2. Route Reply:
Intermediate nodes are not allowed for replying the RREQ packet. Hence, only the destination D are made used for sending the RREP (which is the reply of route) packet as shown in figure 4. rrep_numrreq_numrrep_srcrrep_destlength path neighbors
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RREP
Figure 4. RREP header
The headers of the RREP are found to be similar to that of the RREQ but two fields are added additionally. Sequence number and list of path neighboring nodes are replied by the route. Destination replies are given to all the available RREQs by the process of sending messages of RREP type that has an empty list. In-fact, the near ones of the source (Origin of RREQ) and the destination (Origin of RREP) will not be taken for consideration in the process of computation. As depicted in the figure 4, all the nodes that are intermediate and nodes receives the RREP have an assurance that no routing of loop is there in the path. The node in the intermediate form then
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gives the update message by the additional address of its own and its list of nearby nodes to the field, if there are no such loops,. The RREP thus updated are the n forwarded nodes between to itsneighbors from which it received the request route. Cache of RREQ has this. Propagation of RREP will happen until it reaches the node.
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The overall processing flow of the research work is added in the following figure 5.
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Run routing protocol
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Predict the multiple route paths to the destination
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Compute bandwidth, power and delay for all nodes involved in multi route paths
Optimal route path selection using cat swarm algorithm
Calculate priority of nodes using fuzzy logic
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Assign bandwidth based on priority of nodes
Perform data transmission
Figure 5. Overall processing flow
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4. COMPUTING MULTI-PATH ROUTES The computation of Zone-disjoint routes can be done by S by using RREP’s cache. For all the ‘p’ no of paths into cache destination d of RREP, PIMRP algorithm is used to compute the multi-math routes and selects only non mutual or the non-interfering path. The set of the path are
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built from the p and they don’t have common intermediate or inter node. The multi-path route is formed by combing single nodes. The pairs without common nodes with a) the paths 2) nearby
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nodes union in all the paths.
Once if all are set for the non-interference and when the multipath is calculated, the best of the set is chosen for the replacement of the route which are recorded on the table that are active in routing. The other sets are then computed in the passive way of routing for the later use
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which generally happens when path failure occurs. Simple storing technique based on the size of path is multi-path routing. Various disjoint strategies, it thinks about all the course answers (RREPs).
This will expand the opportunity of acquiring non-meddling multi-way. During the
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beginning of data move, non-meddling strategies are computedand finish delay are not started to build. For advancement, once hub configuration licenses it, parallelization of standard of nonmeddling strategies happens (RREP reserve sections territory unit correspondingly free). To put it plainly, the standard of disjoint techniques used by it doesn't rely on the essential way found, anyway all the got RREPs. Cat swarm optimization algorithm is used to select the route node. The performance metrics like available bandwidth, delay and energy consumption, are used for evaluation and they are given by,
4.1. Available Bandwidth (BW):
The available bandwidth between source node and destination node are given by the Available
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bandwidth (BW) in multicast tree. It is measured in bits, kilobits, or megabits per second and it shows the rate of data transfer in the network. Throughput is measured instead of speed. It also corresponds to the operators licensed frequency spectrum in wireless networks. Highest transmission rate of a transmission link is given by Effective bandwidth. It is measured using
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bandwidth test. Bandwidth test computes the time taken by the link to transfer a file from source to its destination. 4.2. Available Power (P): In multicast tree, the available power of a node is given by,
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P = PTotal – Econsumed(1) Where, Total energy of a node is given byPTotal . Total energy is fixed and predetermined
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for all nodes in the network 4.3.Available Delay (D):
The minimum delay incurred between source and destination path is termed as delay (D). The time taken by a packet to reach its destination from source is indicated by one-way from this.
where
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dend-end= N[ dtrans+dprop+dproc+dqueue]
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delay (OWD). It is also termed as End-to-end delay and Round-Trip Time (RTT) is different
dend-end represents end-to-end delay
dtrans represents transmission delay dprop represents propagation delay dprorepresentsprocessing delay
dqueue represents Queuing delay
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N represnts number of links (Number of routers - 1)
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5. OPITMAL ROUTE NODE SELECTION USING MODIFIEDCAT SWARM ALGORITHM In this section optimal route path selection from the selected multi paths is done. The processes of selecting the multipath routes are explained in the previous section. Multiple route
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paths to the destination is predicted by using route protocol from which optimal route path with satisfied constraints are chosen finally for the successful data transmission. In this work modified
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cat swarm optimization algorithm is utilized for optimal route path selection. The process of cat swarm optimization algorithm is given in this section.
In the field of swarm intelligence, Modified Cat Swarm Optimization is a new optimization algorithm [30]. Two modes are used to conduct felines in CSO: They are, 'Looking for' and
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'Following'. It is made up of introductory populace and made out of particles in arrangement space to look. Winged animals like ants and honey bees are used to make advancement in Particle swarm.In CSO,felines are used tackle issues and feline have its own position. It is made out of D measurements, speeds measurement, wellness esteem, and a banner. Best position of
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one of the felines is arranged at last. Until it reaches the end of the iterations, the CSO keeps the
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best solution. The processing overview of cat swarm optimization is shown figure 6.
Figure 6.Modified Cat swarm optimization working procedure
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CSO has two modes to solve the following problems: 5.1.Seeking Mode: In order to model the characteristics of the cats in the time they rest seeking mode is
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utilized. This particular mode time is given to think and take a decision to move to a next option. It has four important parameters and they are counts of dimension to change (CDC), seeking of memory pool (SMP), self-position consideration (SPC) and the seeking range of the selected
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dimension (SRD). Process of Seeking is elucidated below.
Step1: J number of copies for current position in which k represents cat, where the value of j=SMP. If SPC is found to be true, j= (SMP-1) and current position is retained.
value to replace old values. Step3: Fitness value is calculated ( SF) .
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Step2: For all the copies, in accordance with the CDC , the random plus or minus SRD of current
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Step4: FS of all the nodes are equal, calculation of the probability of each candidate point as per (1) is done. Else , all the selective probability is tuned to 1. Step5: The pickup point of the random points of the candidates are found and replacement of the position of cat k is done
FS
|
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Where 0
FSi=Current node Fitness value
FSb= Node with best fitness value
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FSmax=Maximum fitness value FSmin=Minimum fitness value
To find minimum solution, ,FSb= FSmax, otherwise FSb = FSmin
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(2)
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5.2.Tracing Mode: This is the second type among the modes of the algorithm. Here, targets and the foods are traced by the cats. Tracing process is described as, Step1: Velocities of all the dimensions as per (2) are updated.
range, the limit is given by, ,
𝑉
,
𝑟𝑐
𝑋
Step 3: Position catk is updated as per (3) Xk,d= Xk,d+ Vk,d
,
𝑋
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(3)
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Step2: Verify whether the velocities are found inside the range. New velocity is found to exceed
(4)
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Xbest,d is the position of the cat and it has the best fitness. Xk,dthe position of cat k is and c1 is the acceleration co-efficient for the velocity that exists for the movement of cat for moving in solution space. Its value will lies near 2.05 [31]. r1 is the random value generated uniformly
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for given range [0,1].
For the combination of both of the nodes, The MR ( mixer-ratio) is defined that will indicate the rate at which a mixture is made of the node which is seeking and the one which is tracing. This particular parameter makes a decision as how much cats move in seeking mode. For instance, consider the size of population is 50 and the MR is found to be 0.7. So there must be 50×0.7=35 number of cats moving into seeking mode. the rest of the cats will move into tracing mode [11].
The summary of the MCSO algorithm has been made below. Initially, the N cats (nodes) are created. They are initialized with a flag, position (location) and velocity. As per fitness function which comprises of Energy, Bandwidth and Delay), fitness value of each cat is
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evaluated and best value is kept in memory (Xbest). In the following step, as per the cat’s flag, the apply cat that of the seeking mode or of the tracing mode process. After the completion of related processes, the re-picking of the count of cats and to set them in a seeking mode or in the tracing mode in accordance with the MR value. At the end, the checking of termination condition and the program is done else go to (*)
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This type of algorithm is used to select optimum nodes by most significant routing. This proposed method ensures optimum selection of route paths by which the communication can be made in all the nodes that represent the environment.
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6. PRIORITY BASED TRANSMISSION In case of schemes that have more priority, the customers who hold a highest priority is selected for a service irrespective of the time of arrival of others on the network, The WSNs
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normally perform inside requirements that are more contradictive of involving in the maintenance of these message delay and reliability in data transmissions.At the same time, maximization of the life of battery in ach single sensor. For example in the medical field, if there are any data to be transmitted immediately where there is no enough data resources to transmit.
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In this case our research work can find immediate solution based on priority level of data to be transmitted. Likewise this research work can adapt into various emergency relation applications. Here, the main goal that is to be considered for the research is to propose an algorithm using fuzzy logic by which is optimized and that which allows the queue in a rate of transmission. This
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profound method gives performance related with the values of the fuzzy type of priority in a server which cannot be relied upon including the two type of fuzzy variables. The variables here designed are aimed in giving the priority for the nodes that do not drop the packets and whose energy consumption is more.
In this work, priority is calculated based on traffic length and queue size. If the traffic length is higher than the buffer size then those nodes will be assigned with higher priority for avoiding higher traffic thus the data loss can be prevented. This will prevent the network performance degradation with higher throughput and packet delivery ratio. Likewise nodes are classified into 3 types of clusters namely the one with low, medium and high. That which has low priority does not have any data that are critical and have to be sent without any delay. Hence,
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we can make increasingly the value of the BE for giving a chance to the rest of the nodes for the access of channels. In the other hand, the nodes that are classified as a high in priority have to transmit the data first. This can be achieved by the decrementation of the BE value. The nodes that are considered to be medium, can select one random value of the BE that are given as per the standard. The logical scheme is given as in figure 7.
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Start
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Node ready to send
Queue length assessment
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Traffic rate assessment
Phase selector
Medium priority
High priority
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Low priority
Figure 7.The fuzzy logic scheme.
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The structure of logical system is shown in Figure 8. The inputs rate of traffic and length of the queue are fuzzed inside the variables that are linguistic. It has certain level of uncertainty within given range. These features makes the fuzzy type of algorithms are one of the best adaptive in a dynamic scenario.
Queue length
Traffic rate
Fuzzy rules
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Membership function
Fuzzification Input
Inference Output
Defuzzification Set priority
Figure 8. The fuzzy logic system
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To improve QoS and potential of the energy, dynamic queue management is combined with mixes traffic rate activity in planned fuzzy algorithm. This will also reduce the packet losses. Queue length of each node is managed in such way that packet losses are minimized. The communication at channel interval is ensured by dynamic planning approach. The variables will
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have a random range which is defined by the membership function. Inputs are Triangular functions and output is singleton functions. They are three levels in the amendment. They are
Queue-Length = Qnϵ{Empty, Medium, Full} (1) Traffic-Rate = Tnϵ{Low, Medium, High}. (2)
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low, high and middle. 2 sets in the fuzzy rule are mapped as follows,
Totally 9 combination of pairs can be arranged. The output fuzzy variable for each
Priority= Pnϵ{Low, Medium, High}. (3)
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combination should be established. This is given by,
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Decision table is used to consider the entire appropriate rule. Decision table has nine rules and they are constructed with the help of the Max-Min way. In the phase of the normalization, all the measured value of the system is notified for providing a value that belongs to a common speech. For the normalization of the linguistic value, the proceeding is made in the following manner. If the variation field of the entry variable “y” is [a, b], it can be converted to [−1, 1] using the following linear relation. V = 2y − b − a / b – a
(4)
In initial stage of fuzzy type of rules, algorithm is able to calculate the variables of linguistics that are considered in the work. They are rate of trafficand mean length of the queue. After the completion of normalization, algorithm is applied on all nodes in a network after
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defining the membership function (µk (Qn), µk (Tn)). 7. RESUTLS AND DISCUSSION NS2 simulation environment is used to evaluate and compare the performance of the proposed method. Performance of the proposed method is improved by using various new 19
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algorithms. The comparison evaluation is done between QoS-Oriented Distributed routing protocol (QOD) and Priority and Interference aware Multipath Routing Protocol (PIMRP). Table 1 shows the setting value of the simulation. Based on the application this values can be optimized and varied.
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Table 1: Setting Values for Experiments Value of Experiment
Explanation
E i simulation t Time of
0-100 ms
Duration of simulation
Area of Dimension
(1090*1000) M
No. of mobile nodes
550
Placement of the node
Random waypoint
Speed of mobility
(0-154) Kmph(Kilometer per
X,Y Dimension of direction No. of mobile nodes in a N/W Direction change randomly
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hour)
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Parameter of the
Nodes mobility
5000
hoc N/W Model of mobility
Randomly
Direction of mobility
DSR, Link state routing protocol.
Finding the path
IEEE802.11g
Wired and Wireless routing
Wired and Wireless Channel
t l types Channel
10
No. of base stations
Nodes in infrastructure
1000
No. of nodes in infrastructure N/W
N/W Initial energy of nodes
200 joules
Routing Protocol MAC protocol Channel type
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Base stations
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No. of nodes in each Ad
No. of nodes in a network
In the following sub sections, performance metrics are discussed in detailed based on simulation outcome by utilizing the graphical format. 20
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7.1. Packet Delivery Ratio: It is a ratio of successfully received packet to totally sent packet. The numerical values obtained are given in the following table 2.
Packet delivery ratio QoD 0.62 0.68 0.69 0.72 0.72
200 400 600 800 1000
1 0.9
0.6 0.5 0.4 0.3 0.2 0.1 0
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Packet Delivery Ratio
0.8 0.7
PIMRP 0.73 0.76 0.86 0.92 0.95
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.
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Number of nodes
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Table 2. Packet delivery ratio comparison values
200
400
600 Number of nodes
QoD PIMRP
800
1000
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Figure 9. Number of nodes Vs Packet Delivery Ratio
Figure 9 clearly shows that the proposed method has improved packet delivery ratio. Number of nodes is represented in the x axis and packet delivery ratio is depicted in y axis. In this work, multi path routing is performed which leads to increased packet delivery ratio with
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increased number of nodes. The simulation results confirm that the proposed method significant increased average packet delivery ratio performance than the existing QOD. 7.2. Throughput: Throughput is the rate at which information is sent through the network. The
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numerical values obtained are given in the following table 3. Table 3. Throughput comparison values
QoD 115 187 293 375 450
700 600
300 200 100 0
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Throughput kb/s
500 400
PIMRP 169 239 375 501 590
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200 400 600 800 1000
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Throughput in Kbps
Number of nodes
200
400
600 Number of nodes
QoD PIMRP
800
1000
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Figure 10. Number of nodes vs Throughput comparison
In figure 10, the comparison of throughput parameter is shown. In this comparison figure, number of nodes is represented in the x axis and throughput value is depicted in y axis. The throughput of the proposed work is increased considerably by utilizing the multiple roué paths
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for the data transmission and priority assignment where the urgent packets will be forwarded with more bandwidth allocation. 7.3. Energy consumption: The energy consumed by a network to complete a successful data transmission is called as Energy consumption. The numerical values obtained are given in the
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following table 4.
Number of nodes
Energy consumption(mJ) QoD 200 365 625 850 1000
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200 400 600 800 1000
800 600 400 200 0
PIMRP
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1000
200
PIMRP 30 45 330 450 730
400
600 Number of Nodes
800
1000
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Energy consumption in milli joules
QoD 1200
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Table 4. Energy comparison values
Figure 11: Number of nodes Vs Energy Consumption
Figure 11shows energy consumption of proposed system (PIMRP) is less. Number of nodes is represented in x axis and energy consumption is represented in the y axis. Priority based 23
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data transmission is done in this research work which enables network to assign the higher bandwidth to the nodes where it is required. Thus the unwanted bandwidth allocation is avoided which significantly leads to reduced energy consumption in the proposed research work. 7.4.End to End Delay: Time taken by packet reach its destination form source in a network is
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given by End-to-end delay. The numerical values obtained are given in the following table 5.
200 400 600 800 1000 QoD
500 400 300 200 100 0
PIMRP
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End to End delay in milli seconds
600
End to End delay (ms) QoD PIMRP 140 97 225 115 365 250 410 270 490 325
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Number of nodes
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Table 5. End to End delay comparison values
400
600 Number of Nodes
800
1000
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Figure 12. Number of nodes vs End to End delay
Figure 12 shows the comparison evaluation of the proposed and existing research techniques in terms of end to end delay. This comparison evaluation leads to prove that the proposed PIMRP gains lesser end to end delay than the existing QOD. End to End delay of the 24
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proposed research work is reduced considerably by assigning more priority to the data packets to be sent urgently and adapting the multipath data transmission. 8. CONCLUSION
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Disaster management using UAV becomes the most popular research issues in the real world. This research work implemented a method to ensure the successful packet delivery rate with the concern of prioritization in emergency areas. The overall research work ensures the
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reliable data communication between the UAV vehicles in the reliable manner. This research work also ensures the faster data transmission by focusing on the multi path data transmission factors. This is achieved by introducing the PIMRP which works by considering the interference of route paths in order to avoid the data transmission failure. This work attempts to select the
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most optimal route path by considering interference measure and different QoS attribute to make sure successful packet delivery rate. And emergency services are supported by considering the priority factors where most prioritized data packets will be transmitted by pausing the ongoing data transmission to avoid interference issues. The performance assessment of this work also
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concluded that the proposed technique can guaranteed optimal performance than existing work. Proposed PIMRP shows better performance than QOS where 8.42% increased packet delivery ratio, 19.016% increased throughput, 19.518% reduced energy consumption and 18.663% reduced end to end delay.
In future, security issues can be focused which might reduce the network performance by provide false information. Recent techniques can be utilized to improve the network performance in the future.
REFERENCES
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[1] Kirschenbaum, A. (2019). Chaos organization and disaster management.Routledge. [2] Erdelj, M., Natalizio, E., Chowdhury, K. R., &Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24-32 [3] Li, Z. and Shen, H., 2014. A QoS-oriented distributed routing protocol for hybrid wireless networks. IEEE Transactions on mobile computing, 13(3), pp.693-708. 25
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[4] Periyasamy, P. and Karthikeyan, E., 2013. Survey of current multipath routing protocols for mobile ad hoc networks. International Journal of Computer Network
and Information
Security, 5(12), p.68. [5] Cho, J., Kim, E. and Song, J., 2008. A study on enhanced multipath routing protocol in
of
hybrid wireless mesh network. In Novel Algorithms and Techniques In Telecommunications, Automation and Industrial Electronics (pp. 395-400). Springer, Dordrecht.
pro
[6] Velasquez-Villada, C. and Donoso, Y., 2013. Multipath routing network management protocol for resilient and energy efficient wireless sensor networks. Procedia Computer Science, 17, pp.387-394.
[7] Iqbal, Z., Khan, S., Mehmood, A., Lloret, J. and Alrajeh, N.A., 2016. Adaptive Cross-Layer
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Multipath Routing Protocol for Mobile Ad Hoc Networks. Journal of Sensors, 2016. [8] Lutimath, N.M. and Abhilash, D., Interference Aware Hybrid Multipath Protocol for Mobile Ad hoc Network. 2017, 25-34 .
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[9] Jayavenkatesan, R. and Mariappan, A., 2017. Energy efficient multipath routing for MANET based on hybrid ACO-FDRPSO. Int. J. Pure Appl. Math, 115(6), pp.185-191. [10] Singh, D., Sharma, B.K. and Kumar, A., 2014. A survey on challenges in multipath routing for adhocnetwrorks. International Journal of Emerging Technology and Advanced Engineering, ICADET-14, India (2250-2459), 4(1).
[11] Radi, M., Dezfouli, B., Bakar, K.A. and Lee, M., 2012. Multipath routing in wireless sensor networks: survey and research challenges. Sensors, 12(1), pp.650-685. [12] Vats, J., Khurana, S., Singh, T.P. and Sharma, S., 2012. Routing Protocols in MANETs: An Enhanced Multipath Routing Protocol (AODLB) using Load Balancing Approach. International
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Journal of Advanced Research in Computer Science, 3(2). [13] Varalakshmi, K.V., 2016. Energy-Saving
Routing Protocol for Battery Improvement in
Multipath Wireless Ad Hoc Networks.
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[14] Gomathy, C. and Shanmugavel, S., 2005, January. Implementation of modified Fuzzy Priority Scheduler for MANET and performance analysis with mixed traffic. In Proc. 11th National Conference on Communication (NCC’05). [15] Sajwan, M., Gosain, D. and Sharma, A.K., 2018. Hybrid energy-efficient multi-path
of
routing for wireless sensor networks. Computers & Electrical Engineering, 67, pp.96-113. [16] Rahat, A.A., Everson, R.M. and Fieldsend, J.E., 2016. Evolutionary multi-path routing for
pro
network lifetime and robustness in wireless sensor networks. Ad Hoc Networks, 52, pp.130-145. [17] Aswale, S. and Ghorpade, V.R., 2018. Geographic Multipath Routing based on Triangle Link Quality Metric with Minimum Inter-path Interference for Wireless Multimedia Sensor Networks. Journal of King Saud University-Computer and Information Sciences.
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[18] Rajalakshmi, S. and Maguteeswaran, R., 2015. Quality of service routing in Manet using a hybrid intelligent algorithm inspired by cuckoo search. The Scientific World Journal, 2015. [19] Adam, S.M. and Hassan, R., 2013. Delay aware reactive routing protocols for QoS in
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MANETs: A review. Journal of applied research and technology, 11(6), pp.844-850. [20] Sharma, H., 2012. QoS Oriented Reservation Based Routing Mechanism for Wireless Adhoc Networks. Journal of global research in Computer Science, 2(12), pp.22-28. [21] Aswale, S., &Ghorpade, V. R. (2018). Geographic multipath routing based on triangle link quality metric with minimum inter-path interference for wireless multimedia sensor networks. Journal of King Saud University-Computer and Information Sciences. [22] Radi, M., Dezfouli, B., Bakar, K. A., Razak, S. A., &Nematbakhsh, M. A. (2011). Interference-aware multipath routing protocol for QoS improvement in event-driven wireless
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sensor networks. Tsinghua Science and Technology, 16(5), 475-490. [23] Li, B. Y., & Chuang, P. J. (2013). Geographic energy-aware non-interfering multipath routing for multimedia transmission in wireless sensor networks. Information Sciences, 249, 2437.
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[24] Jabbar, W. A., Ismail, M., &Nordin, R. (2017). Energy and mobility conscious multipath routing scheme for route stability and load balancing in MANETs. Simulation Modelling Practice and Theory, 77, 245-271. [25] Sutagundar, A. V., &Manvi, S. S. (2013). Location aware event driven multipath routing in
of
Wireless Sensor Networks: Agent based approach. Egyptian Informatics Journal, 14(1), 55-65. [26] Hou, R., Qu, S., Lui, K. S., & Li, J. (2013). Coding-and interference-aware routing protocol
pro
in wireless networks. Computer Communications, 36(17-18), 1745-1753.
[27] Chao, G. U., & Qi, Z. H. U. (2013). Interference aware routing for mobile ad hoc networks based on node's sending and receiving capabilities. The Journal of China Universities of Posts
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and Telecommunications, 20(4), 73-79.
[28]Peng, Y., Yu, Y., Wang, X., Song, Q., Yu, Y., & Yu, C. (2013). A new coding-and interference-aware routing protocol in wireless mesh networks. Computers & Electrical
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Engineering, 39(6), 1822-1836.
[29] Li, Z., &Shen, H. (2012). A QoS-oriented distributed routing protocol for hybrid wireless networks. IEEE Transactions on mobile computing, 13(3), 693-708. [30] Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization.In Pacific Rim international conference on artificial intelligence (pp. 854-858).Springer, Berlin, Heidelberg. [31] Orouskhani, M., Orouskhani, Y., Mansouri, M., &Teshnehlab, M. (2013). A novel cat swarm optimization algorithm for unconstrained optimization problems. International Journal of Information Technology and Computer Science (IJITCS), 5(11), 32. [32] Li, Q., Tu, W., &Zhuo, L. (2018). Reliable rescue routing optimization for urban emergency 77.
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logistics under travel time uncertainty. ISPRS International Journal of Geo-Information, 7(2),
[33] Manaseer, S., &Alawneh, A. (2017). A new mobility model for ad hoc networks in disaster recovery areas. International Journal of Online Engineering (iJOE), 13(06), 113-120.
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[34] Jahir, Y., Atiquzzaman, M., Refai, H., Paranjothi, A., &LoPresti, P. G. (2019). Routing protocols and architecture for disaster area network: A survey. Ad Hoc Networks, 82, 1-14.
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AUTHORS PROFILE
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Murugeswari.T completed the AIME in Electronics and Communication Engineering, The institution of Engineers (India), Calcutta and M.E. Power Electronics & Drives in Sri Ramakrishna Engineering College, Coimbatore, (Anna University, Chennai). At present, she is pursuing Ph.D.,in the field of Networking in Anna University, Chennai. She has about 19 years of teaching experience and 6 years of experience in industrial field. She has published about 9 technical papers in various international journals. She has presented various technical papers in 8 international and 17 national conferences. She has organized a seminar, a conference and two guest lectures. She has been working as an Assistant professor in Electrical and Electronics Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India since 2005.
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Dr. S. Rathi is an Associate Professor, Department of Computer Science Engineering, Government College of Technology, Coimbatore, Tamilnadu, India. She did her Ph.D. in Mobile Computing (Anna University, Chennai). Her fields of interests include Computer Networks, Mobile Computing, Wireless Security & the Fault Tolerant system Design. She completed her ME and BE in CSE from Government College of Technology, Coimbatore. She also leads and teaches modules at both B.E, M.E. levels in Computer Science. She published 22 technical papers in the national & international journals and 24 conferences. She has organized 2 conferences for students and faculty, 7 training programmes for faculty and 12 training programmes for students.
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CRediT Author Statement
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CRediT (Contributor Roles Taxonomy) was introduced with the intention of recognizing individual author contributions, reducing authorship disputes and facilitating collaboration. The idea came about in 2015 at a Harvard workshop and it became a collaborative effort led by the Welcome Trust and Digital Science, with input from publishers, including Elsevier, represented by Cell Press. CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to the published work.
The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors. The role(s) of all authors should be listed, using the relevant above categories. Authors may have contributed in multiple roles. CRediT in no way changes the journal’s criteria to qualify for authorship.
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CRediT statements should be provided during the submission process and will appear above the acknowledgement section of the published paper as shown further below.
T. Murugeswari:
Conceptualization
Methodology
Software
Data curation
Writing- Original draft preparation
Writing- Reviewing and Editing
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Dr.S.Rathi:
Visualization
Investigation and Supervision
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PII: DOI: Reference:
S0140-3664(19)31180-6 https://doi.org/10.1016/j.comcom.2019.11.050 COMCOM 6052
To appear in:
Computer Communications
Received date : 14 September 2019 Revised date : 26 November 2019 Accepted date : 28 November 2019 Please cite this article as: T. Murugeswari and S. Rathi, Priority and interference aware multipath routing based communications for extreme surveillance systems, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.11.050. 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. © 2019 Published by Elsevier B.V.
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PRIORITY AND INTERFERENCE AWARE MULTIPATH ROUTING BASED COMMUNICATIONS FOR EXTREME SURVEILLANCE
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SYSTEMS
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T. Murugeswari*1 and Dr.S.Rathi2
Assistant Professor/ Department of Electrical and Electronics Engineering, Hindusthan College
of
Engineering
and
Technology,
Coimbatore
[email protected]
Tamilnadu,
India.
E-mail:
Associate Professor, Department of Computer Science Engineering, Government College of
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2
641032,
Technology, Coimbatore, 641013, Tamilnadu, India. E-mail: [email protected] ABSTRACT
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Increased natural disaster in various urban and rural areas requires immediate attention to avoid the major causes. In real world, immediate recovery to the disaster areas is ensured by adapting the unmanned aerial vehicles that enable people to reach the disaster areas immediately. Here specific task in the disaster area can be completed efficiently by working with multi UAV instead of single UAV. Here data communication between the UAV needs to be very reliable to ensure the proper disaster management outcome. It is more complex to provide the required services to the users when there is situation arise to switch between the heterogeneous networks. The QoS-Oriented Distributed routing protocol (QOD) is used in the existing methods to give solution to this problem. The data is transferred between hybrid networks with required QoS. In the existing work, routing is done by considering the QoS consideration thus the efficient and
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reliable distributed routing is guaranteed. However the existing work lacks from the following issues: It avoids the data transfer through the path in which data transmission is going already to avoid the interference problems which might reduce the throughput rate. Priority of the data transferred from multiple sources are not considered in the previous work and also existing work focused on reducing delay alone as QoS factor. Priority and Interference aware Multipath Routing Protocol (PIMRP) is introduced to rectify this issue in the proposed method. In this 1
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method, interference aware and priority routing is ensured by introducing the following research methods. Here, Multipath interference based routing method is used allow transmission of data from multiple path that resides within interference range with guaranteed interference avoidance and increased throughput. Here route path nodes are selected with multiple objectives such as
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delay, bandwidth, and energy consumption. To provide more preference to the prioritized data transmission nodes with more resource availability is provided to the prioritized data packets which are tiny segmented. The performance of the proposed method is evaluated using NS2
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simulation tool. The simulation results confirm the efficiency of the proposed method. Keywords: Multipath routing, Interference aware routing, throughput, QoS parameters, Hybrid wireless network.
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1. INTRODUCTION
Disaster Management can be defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters [1].
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Unmanned aerial vehicles (UAVs) can be effectively used in disaster management as per the suggestion of the National Disaster Management Authority (NDMA) [2]."UAVs can provide high-resolution, real-time images of even the inaccessible locations. These images can then be used to produce accurate hazard maps so that prevention and mitigation measures for reducing disaster risks are planned accordingly," said R K Jain, Member, NDMA. In a post-disaster situation, UAVs can be used to map the affected areas in high resolution within a short time, which, in turn, can aid swift and efficient response, he said during a national-level brainstorming session on "Application of Unmanned Aerial Vehicles (UAVs) in Disaster Management". The event was aimed to build and improve the capacity of stakeholders to use UAVs and related technologies for better disaster preparedness and response. Underlining the importance
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of UAV technology for disaster management, Jain said that all stakeholders should strive to leverage existing technologies for quick and efficient disaster response."UAVs can be effectively used in different phases of disaster management - they can help us identify areas affected by disasters.
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Hybrid wireless networks are the one in which every mobile nodes belong to a wireless network has connectivity possible either in a direct manner through a gateway over an infrastructure based network [3]. The network described in the latter part may have an IP based network as an internet or an 3G network and can also have LAN that are 802.11 bases[4]. In
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reality, any network based technique can be considered. Here, the representation of the Intra based technology and the inter based techniques are represented [5].
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In case of one node communicating with the other one in a network that uses a same technology, this can be efficiently seen as the Intra technique based Hybrid Network [6]. As an example, the node in a mobile network for an ad hoc 802.11 network that communicates with using an Access point (AP) in a network of infrastructure [7]. In the contrast, if a mobile node happens to communicate with another type of network which uses a different technique; this can
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be considered as Inter technology hybrid wireless network [8].
The progress in case of a mobile based communications thus leads to environments that are capable of accessing the networks at any point of time and from anywhere [9]. Various
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mobile applications have been developed that focuses on the performances in the transmission rate and the network capacity [10]. The expectation is that such a system in the near future will be as capable of providing the communication techniques to make the fast reaction as a possible event even in emergency cases since these are quick and more over accurate in assessing the damages which is a vital component for the fast recovery of rescue activities [11]. One of the other concerns is that the available communication systems are mostly vulnerable in the post math of the large and natural disaster owing to the congestion in the communication [12]. A few studies on the feasibility have been already carried for the collection of damage assessment and the related information for the mitigation of damages in a large disaster [13]. The work presented in [14] describes the personnel safety over the internet. The WWW is filled with
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many technologies that are very much useful for the collection of damage assessment from one city owing to the disaster incase of the interruption in the network [15]. Anyways, these have a chance of having a potential threat due to the congestion in the communication. It can never guarantee a faster response in case of disaster [16].
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Meanwhile, the WNs are generally non trivial for the disaster communications [17]. Specifically, the Ad-Hoc types of networks have drawn most attention and various protocols for the routing have been proposed in the literature. [18]. these networks can work in an efficient manner for the disaster in the relief operations that too in the facilities of the infrastructure that
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do not get operated in a sufficient manner [19]. These networks can be more flexible when the connection tends to be vulnerable for the variety of conditions. On the one hand, A network hierarchy based centralized network can be established for the effective communication due to
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the BS (Base Station), which will affect the grade of the connection [20].
In order to get the resolution to the problems mentioned above, this research work targets at introducing a Hybrid network for the wireless communications, which integrates the Ad-Hoc with the Cellular network. It is of huge help in preserving the connectivity and the availability in
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the network using the concept of multi hopping in emergency scenarios. In the proposed work, the priority and the concept of interference in routing are thereby guaranteed by using few approaches. These include the multipath interference and are dependent on the routing method that basically tries to permits transmitting data in alternative paths and which is present in a
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range ensuring avoidance and an improved throughput. In this, the path of the route are not chosen considering different aims like the delay and the bandwidth for the energy consumption, for providing higher priority to the data, present in priority data packets and are segmented in tinier ones.
The overall organization of this works is provided as below: Section 1 gives an extensive overview on the proposed system. In section 2, the techniques available in the literature are discussed. Section 3 describes the proposed method. Section 4 throws light on experimental arrangement. Section 6 and 7 discusses the result and conclusion.
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2. RELATED WORKS
Aswale et al [21] implemented a Geographic Multipath Routing (TIGMR) protocol based on minimum inter-path Interference and Triangle link quality metric. This protocol is used to find the multiple node- disjoint paths in the networks which are based on IEEE 802.15.4. Based on unused energy, distance in the presence of minimum adjacent path interference effect and triangle link quality metric the forwarding node is selected by the cross-layer routing protocol. It 4
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does not use handshake techniques like RTS/CTS (Request-To-Send/Clear-To-Send) to avoid Hidden Node Problem (HNP) in the sink node. Radi et al [22] implemented a protocol to fulfill the QoS requirements of event-based applications which is known as Low-Interference Energy-efficient Multipath Routing protocol
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(LIEMRO). This protocol uses the quality-based load balancing algorithm to optimize the resource usage in the defined paths by controlling the amount of traffic in that path.
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Li et al [23] presented a Geographic Energy-Aware non-interfering Multipath (GEAM) routing approach which partitions entire network topology into several districts. At the same time, data is forwarded through these districts with no interference with one another to carry out transmissions with no interference. The energy status of the nodes is used to adapt each district’s changing topology.
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load. Fixed paths are not created initially. So it can produce better performance in the quick
Jabbar et al [24] identified the nodes to use as MPRs to broadcast the information of topology by using Mobility Aware Multi-Point Relay (EMA-MPR) selection method. It is an
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extension of EXata network simulator. Various simulation situations and parameters of the mobility are used to evaluate the performance of the method and results show it ability. Sutagundar et al [25] used mobile and static agent’s sets to implement an event triggered multipath routing which can be used in WSNs. The location of sink and each are assumed to be known by each node. The performance evaluation parameters like energy consumption, overhead, latency and packet delivery ratio are used to evaluate the performance of the proposed routing.
Hou et al [26] defined the coding-sensitive routing protocol. Path bandwidth estimate is produced in a better manner. It is capable of identifying paths with much better throughput.
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Elaborate NS2 simulations shows this approach performs better than the available schemes. Chao et al [27] presented an Interference aware cross layer routing protocol (IA-CLR) for MANETs. It is an interference aware routing protocol and it is based on MAC layer of IEEE 802.11. IA-CLR shows the link’s interference strength by defining receiver and transmitter’s
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potential. Network and MAC layers are combined to form a cross layer which introduces a novel routing metric to choose a route with minimum bottleneck. Peng et al [28] proposed a Coding and Interference Aware Routing (CIAR) protocol to detect the probable coding possibilities. They are used to define the paths by considering cost of
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the path’s interference. It can be quite helpful in rendering the balance between enabling more coding gains and tackling interference.
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Li et al [29] improved the potential of QoS support in hybrid networks by using QoSOriented Distributed routing protocol (QOD). This protocol converts packet routing problem into resource scheduling issue by using the hops with less number of transmission and hybrid network’s transmission characteristics. The experimental results show the better performance in
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terms of scalability, mobility-tolerance, overhead and transmission delay.
Li et al [32] proposed a multi objective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and Tabu Search (TS) was designed to solve the model. An experiment optimizing rescue
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routing plans under a real urban storm event, was carried out to validate the proposed model. Manaseer et al [33] performed deeper analysis on some of the main mobility models used in testing new protocols and a new mobility model is proposed to incorporate some neglected factors concerned with disaster recovery situations.
Jahir et al [34] investigated network architecture and routing models for disaster area networks. The main objective and goal of DAN is to ensure reliable, energy efficient communication which is susceptible to mobility and topology changes in the disaster area. The purpose is to improve delay, reduce overhead, minimize energy used, sustain movement and
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increase bandwidth for multimedia applications.
3. PRIORITY AND INTERFERENCE AWARE MULTIPATH ROUTING In this research methodology, priority and interference aware routing is ensured by introducing the following research methods. Those are Multipath interference based routing method is introduced. This protocol allows transmission of data from multiple path that resides 6
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within interference range with guaranteed interference avoidance and increased throughput. Here route path nodes are selected with multiple objectives including as delay, bandwidth, and energy consumption. To provide more preference to the prioritized data transmission nodes with more resource availability is provided to the prioritized data packets which are tiny segmented. Non
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Interference in this work defines that no data will be forwarded in the same data link where there is already on-going data transmission. Optimal route paths are selected to achieve this by considering on-going data transmission. And interference is prevented by introducing the priority
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calculation where emergency data will be allowed to transfer first by pausing the on-going data transmission. 3.1. NETWORK MODEL
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Wireless network are molded by the use of graph G= (V, E) where the V represents node set and E represents the link set. All edge links which comes between nodes that are currently inside the range of transmission and this work we have assumes that this is not changeable for all the nodes. The result of the graph is named as a Unit Disk Graph (UDG). Neighbor set given by
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vi by N (vi). The path between Source Node (S) and the destination node D is represented as (S = v0, v1, v2. . .vn = D) where vi∈ V and vi∈ N(vi−1) with the length n. The path in which first choice of transmission process was carried between source and destination are termed as the primary path. All nodes are in the hybrid WNs and has a specific identifier and its geo positions are well known. All the nodes are arranged in a 2D Euclidean space where G is a graph of geometry. Further assumption is also made that the complete nodes will transmit their positions to their nearby nodes by the use of HELLO messages in a regular time interval. These are also termed as
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Beacon Messages. The network model is shown in the following figure 1.
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Figure 1. Network model of hybrid network
3.2. ROUTE DISCOVERY
Primary step for discovery of route has two steps. They are, 3.2.1. Route Request:
The S has the number of packets to get it delivered to D and it does not have the route to achieve the destiny, S then happen to send a request (RREQ). Each of the requests will be unique and
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these are identified by using the identifiers. The count of the sequence in RREQ as well as with the address in the node that originate along the RREQ. The RREQ’s header is then shown in the figure 2.
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RREQ
rreq_seq_numrreq_srcrreq_destlength
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Figure 2.RREQ header
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The more detailed RREQ header format is shown in the following figure 3
Figure 3. RREQ header format
The field that belongs to the path has the list which is intermediate type of nodes between the Source node S and the Destination node D. The list which is presented will be empty as and when the S sends the request and there after the path field has the list of all nodes that are intermediate and which is present inside the s and d. Again, the path of the field is then updated and then these are prorogated. All the nodes that are relay type will have their own address proof and are in the nodes present in-between. The list hence allow the loops that are used for the routing and also helps for building a path that returns in sending the reply for the request RRERQ1. Always the PIMRP are given training in the source and these are also tend to get used
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by the node which is termed as the source for specifying the whole in the data for all the packets. These are also called as Encapsulated Path. Collection of maximum information regarding the path that includes the info of the nodes and that of the neighbor by preserving the reliability, the nodes are then found in the nodes that lies in an intermediate position and does not allow for the reply in the RREQs which are seemed 9
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to be redundant and are hence to avoid the issues in the broadcast. As a node that seems to be intermediate and when getting the first copy of the RREQ which is a duplicate, the nodes are ensured to have the address of its own are not being any part of the list intermediate for avoiding the loops in prior to the RREQ cache that are being saved.
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At the same time, as and when the RREQs are dealt with, it facilitate search node in learning about its neighborhood. Hello packets are avoided to reduce overhead of routing. Every
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node adds the neighboring node which has transmitted the packets of it’s in its list of the neighbors. With a particular end objective to avoid out of date passages, an evacuation timeout instrument is added. After timeout, another packetis supplied from identical source.After received RREQ final goal node, RREP is transmitted.
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3.2.2. Route Reply:
Intermediate nodes are not allowed for replying the RREQ packet. Hence, only the destination D are made used for sending the RREP (which is the reply of route) packet as shown in figure 4. rrep_numrreq_numrrep_srcrrep_destlength path neighbors
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RREP
Figure 4. RREP header
The headers of the RREP are found to be similar to that of the RREQ but two fields are added additionally. Sequence number and list of path neighboring nodes are replied by the route. Destination replies are given to all the available RREQs by the process of sending messages of RREP type that has an empty list. In-fact, the near ones of the source (Origin of RREQ) and the destination (Origin of RREP) will not be taken for consideration in the process of computation. As depicted in the figure 4, all the nodes that are intermediate and nodes receives the RREP have an assurance that no routing of loop is there in the path. The node in the intermediate form then
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gives the update message by the additional address of its own and its list of nearby nodes to the field, if there are no such loops,. The RREP thus updated are the n forwarded nodes between to itsneighbors from which it received the request route. Cache of RREQ has this. Propagation of RREP will happen until it reaches the node.
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The overall processing flow of the research work is added in the following figure 5.
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Run routing protocol
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Network nodes
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Predict the multiple route paths to the destination
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Compute bandwidth, power and delay for all nodes involved in multi route paths
Optimal route path selection using cat swarm algorithm
Calculate priority of nodes using fuzzy logic
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Assign bandwidth based on priority of nodes
Perform data transmission
Figure 5. Overall processing flow
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4. COMPUTING MULTI-PATH ROUTES The computation of Zone-disjoint routes can be done by S by using RREP’s cache. For all the ‘p’ no of paths into cache destination d of RREP, PIMRP algorithm is used to compute the multi-math routes and selects only non mutual or the non-interfering path. The set of the path are
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built from the p and they don’t have common intermediate or inter node. The multi-path route is formed by combing single nodes. The pairs without common nodes with a) the paths 2) nearby
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nodes union in all the paths.
Once if all are set for the non-interference and when the multipath is calculated, the best of the set is chosen for the replacement of the route which are recorded on the table that are active in routing. The other sets are then computed in the passive way of routing for the later use
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which generally happens when path failure occurs. Simple storing technique based on the size of path is multi-path routing. Various disjoint strategies, it thinks about all the course answers (RREPs).
This will expand the opportunity of acquiring non-meddling multi-way. During the
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beginning of data move, non-meddling strategies are computedand finish delay are not started to build. For advancement, once hub configuration licenses it, parallelization of standard of nonmeddling strategies happens (RREP reserve sections territory unit correspondingly free). To put it plainly, the standard of disjoint techniques used by it doesn't rely on the essential way found, anyway all the got RREPs. Cat swarm optimization algorithm is used to select the route node. The performance metrics like available bandwidth, delay and energy consumption, are used for evaluation and they are given by,
4.1. Available Bandwidth (BW):
The available bandwidth between source node and destination node are given by the Available
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bandwidth (BW) in multicast tree. It is measured in bits, kilobits, or megabits per second and it shows the rate of data transfer in the network. Throughput is measured instead of speed. It also corresponds to the operators licensed frequency spectrum in wireless networks. Highest transmission rate of a transmission link is given by Effective bandwidth. It is measured using
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bandwidth test. Bandwidth test computes the time taken by the link to transfer a file from source to its destination. 4.2. Available Power (P): In multicast tree, the available power of a node is given by,
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P = PTotal – Econsumed(1) Where, Total energy of a node is given byPTotal . Total energy is fixed and predetermined
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for all nodes in the network 4.3.Available Delay (D):
The minimum delay incurred between source and destination path is termed as delay (D). The time taken by a packet to reach its destination from source is indicated by one-way from this.
where
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dend-end= N[ dtrans+dprop+dproc+dqueue]
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delay (OWD). It is also termed as End-to-end delay and Round-Trip Time (RTT) is different
dend-end represents end-to-end delay
dtrans represents transmission delay dprop represents propagation delay dprorepresentsprocessing delay
dqueue represents Queuing delay
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N represnts number of links (Number of routers - 1)
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5. OPITMAL ROUTE NODE SELECTION USING MODIFIEDCAT SWARM ALGORITHM In this section optimal route path selection from the selected multi paths is done. The processes of selecting the multipath routes are explained in the previous section. Multiple route
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paths to the destination is predicted by using route protocol from which optimal route path with satisfied constraints are chosen finally for the successful data transmission. In this work modified
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cat swarm optimization algorithm is utilized for optimal route path selection. The process of cat swarm optimization algorithm is given in this section.
In the field of swarm intelligence, Modified Cat Swarm Optimization is a new optimization algorithm [30]. Two modes are used to conduct felines in CSO: They are, 'Looking for' and
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'Following'. It is made up of introductory populace and made out of particles in arrangement space to look. Winged animals like ants and honey bees are used to make advancement in Particle swarm.In CSO,felines are used tackle issues and feline have its own position. It is made out of D measurements, speeds measurement, wellness esteem, and a banner. Best position of
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one of the felines is arranged at last. Until it reaches the end of the iterations, the CSO keeps the
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best solution. The processing overview of cat swarm optimization is shown figure 6.
Figure 6.Modified Cat swarm optimization working procedure
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CSO has two modes to solve the following problems: 5.1.Seeking Mode: In order to model the characteristics of the cats in the time they rest seeking mode is
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utilized. This particular mode time is given to think and take a decision to move to a next option. It has four important parameters and they are counts of dimension to change (CDC), seeking of memory pool (SMP), self-position consideration (SPC) and the seeking range of the selected
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dimension (SRD). Process of Seeking is elucidated below.
Step1: J number of copies for current position in which k represents cat, where the value of j=SMP. If SPC is found to be true, j= (SMP-1) and current position is retained.
value to replace old values. Step3: Fitness value is calculated ( SF) .
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Step2: For all the copies, in accordance with the CDC , the random plus or minus SRD of current
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Step4: FS of all the nodes are equal, calculation of the probability of each candidate point as per (1) is done. Else , all the selective probability is tuned to 1. Step5: The pickup point of the random points of the candidates are found and replacement of the position of cat k is done
FS
|
|
Where 0
FSi=Current node Fitness value
FSb= Node with best fitness value
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FSmax=Maximum fitness value FSmin=Minimum fitness value
To find minimum solution, ,FSb= FSmax, otherwise FSb = FSmin
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(2)
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5.2.Tracing Mode: This is the second type among the modes of the algorithm. Here, targets and the foods are traced by the cats. Tracing process is described as, Step1: Velocities of all the dimensions as per (2) are updated.
range, the limit is given by, ,
𝑉
,
𝑟𝑐
𝑋
Step 3: Position catk is updated as per (3) Xk,d= Xk,d+ Vk,d
,
𝑋
,
(3)
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𝑉
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Step2: Verify whether the velocities are found inside the range. New velocity is found to exceed
(4)
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Xbest,d is the position of the cat and it has the best fitness. Xk,dthe position of cat k is and c1 is the acceleration co-efficient for the velocity that exists for the movement of cat for moving in solution space. Its value will lies near 2.05 [31]. r1 is the random value generated uniformly
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for given range [0,1].
For the combination of both of the nodes, The MR ( mixer-ratio) is defined that will indicate the rate at which a mixture is made of the node which is seeking and the one which is tracing. This particular parameter makes a decision as how much cats move in seeking mode. For instance, consider the size of population is 50 and the MR is found to be 0.7. So there must be 50×0.7=35 number of cats moving into seeking mode. the rest of the cats will move into tracing mode [11].
The summary of the MCSO algorithm has been made below. Initially, the N cats (nodes) are created. They are initialized with a flag, position (location) and velocity. As per fitness function which comprises of Energy, Bandwidth and Delay), fitness value of each cat is
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evaluated and best value is kept in memory (Xbest). In the following step, as per the cat’s flag, the apply cat that of the seeking mode or of the tracing mode process. After the completion of related processes, the re-picking of the count of cats and to set them in a seeking mode or in the tracing mode in accordance with the MR value. At the end, the checking of termination condition and the program is done else go to (*)
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This type of algorithm is used to select optimum nodes by most significant routing. This proposed method ensures optimum selection of route paths by which the communication can be made in all the nodes that represent the environment.
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6. PRIORITY BASED TRANSMISSION In case of schemes that have more priority, the customers who hold a highest priority is selected for a service irrespective of the time of arrival of others on the network, The WSNs
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normally perform inside requirements that are more contradictive of involving in the maintenance of these message delay and reliability in data transmissions.At the same time, maximization of the life of battery in ach single sensor. For example in the medical field, if there are any data to be transmitted immediately where there is no enough data resources to transmit.
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In this case our research work can find immediate solution based on priority level of data to be transmitted. Likewise this research work can adapt into various emergency relation applications. Here, the main goal that is to be considered for the research is to propose an algorithm using fuzzy logic by which is optimized and that which allows the queue in a rate of transmission. This
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profound method gives performance related with the values of the fuzzy type of priority in a server which cannot be relied upon including the two type of fuzzy variables. The variables here designed are aimed in giving the priority for the nodes that do not drop the packets and whose energy consumption is more.
In this work, priority is calculated based on traffic length and queue size. If the traffic length is higher than the buffer size then those nodes will be assigned with higher priority for avoiding higher traffic thus the data loss can be prevented. This will prevent the network performance degradation with higher throughput and packet delivery ratio. Likewise nodes are classified into 3 types of clusters namely the one with low, medium and high. That which has low priority does not have any data that are critical and have to be sent without any delay. Hence,
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we can make increasingly the value of the BE for giving a chance to the rest of the nodes for the access of channels. In the other hand, the nodes that are classified as a high in priority have to transmit the data first. This can be achieved by the decrementation of the BE value. The nodes that are considered to be medium, can select one random value of the BE that are given as per the standard. The logical scheme is given as in figure 7.
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Start
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Node ready to send
Queue length assessment
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Traffic rate assessment
Phase selector
Medium priority
High priority
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Low priority
Figure 7.The fuzzy logic scheme.
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The structure of logical system is shown in Figure 8. The inputs rate of traffic and length of the queue are fuzzed inside the variables that are linguistic. It has certain level of uncertainty within given range. These features makes the fuzzy type of algorithms are one of the best adaptive in a dynamic scenario.
Queue length
Traffic rate
Fuzzy rules
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Membership function
Fuzzification Input
Inference Output
Defuzzification Set priority
Figure 8. The fuzzy logic system
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To improve QoS and potential of the energy, dynamic queue management is combined with mixes traffic rate activity in planned fuzzy algorithm. This will also reduce the packet losses. Queue length of each node is managed in such way that packet losses are minimized. The communication at channel interval is ensured by dynamic planning approach. The variables will
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have a random range which is defined by the membership function. Inputs are Triangular functions and output is singleton functions. They are three levels in the amendment. They are
Queue-Length = Qnϵ{Empty, Medium, Full} (1) Traffic-Rate = Tnϵ{Low, Medium, High}. (2)
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low, high and middle. 2 sets in the fuzzy rule are mapped as follows,
Totally 9 combination of pairs can be arranged. The output fuzzy variable for each
Priority= Pnϵ{Low, Medium, High}. (3)
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combination should be established. This is given by,
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Decision table is used to consider the entire appropriate rule. Decision table has nine rules and they are constructed with the help of the Max-Min way. In the phase of the normalization, all the measured value of the system is notified for providing a value that belongs to a common speech. For the normalization of the linguistic value, the proceeding is made in the following manner. If the variation field of the entry variable “y” is [a, b], it can be converted to [−1, 1] using the following linear relation. V = 2y − b − a / b – a
(4)
In initial stage of fuzzy type of rules, algorithm is able to calculate the variables of linguistics that are considered in the work. They are rate of trafficand mean length of the queue. After the completion of normalization, algorithm is applied on all nodes in a network after
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defining the membership function (µk (Qn), µk (Tn)). 7. RESUTLS AND DISCUSSION NS2 simulation environment is used to evaluate and compare the performance of the proposed method. Performance of the proposed method is improved by using various new 19
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algorithms. The comparison evaluation is done between QoS-Oriented Distributed routing protocol (QOD) and Priority and Interference aware Multipath Routing Protocol (PIMRP). Table 1 shows the setting value of the simulation. Based on the application this values can be optimized and varied.
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Table 1: Setting Values for Experiments Value of Experiment
Explanation
E i simulation t Time of
0-100 ms
Duration of simulation
Area of Dimension
(1090*1000) M
No. of mobile nodes
550
Placement of the node
Random waypoint
Speed of mobility
(0-154) Kmph(Kilometer per
X,Y Dimension of direction No. of mobile nodes in a N/W Direction change randomly
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hour)
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Parameter of the
Nodes mobility
5000
hoc N/W Model of mobility
Randomly
Direction of mobility
DSR, Link state routing protocol.
Finding the path
IEEE802.11g
Wired and Wireless routing
Wired and Wireless Channel
t l types Channel
10
No. of base stations
Nodes in infrastructure
1000
No. of nodes in infrastructure N/W
N/W Initial energy of nodes
200 joules
Routing Protocol MAC protocol Channel type
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Base stations
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No. of nodes in each Ad
No. of nodes in a network
In the following sub sections, performance metrics are discussed in detailed based on simulation outcome by utilizing the graphical format. 20
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7.1. Packet Delivery Ratio: It is a ratio of successfully received packet to totally sent packet. The numerical values obtained are given in the following table 2.
Packet delivery ratio QoD 0.62 0.68 0.69 0.72 0.72
200 400 600 800 1000
1 0.9
0.6 0.5 0.4 0.3 0.2 0.1 0
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Packet Delivery Ratio
0.8 0.7
PIMRP 0.73 0.76 0.86 0.92 0.95
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.
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Number of nodes
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Table 2. Packet delivery ratio comparison values
200
400
600 Number of nodes
QoD PIMRP
800
1000
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Figure 9. Number of nodes Vs Packet Delivery Ratio
Figure 9 clearly shows that the proposed method has improved packet delivery ratio. Number of nodes is represented in the x axis and packet delivery ratio is depicted in y axis. In this work, multi path routing is performed which leads to increased packet delivery ratio with
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increased number of nodes. The simulation results confirm that the proposed method significant increased average packet delivery ratio performance than the existing QOD. 7.2. Throughput: Throughput is the rate at which information is sent through the network. The
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numerical values obtained are given in the following table 3. Table 3. Throughput comparison values
QoD 115 187 293 375 450
700 600
300 200 100 0
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Throughput kb/s
500 400
PIMRP 169 239 375 501 590
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200 400 600 800 1000
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Throughput in Kbps
Number of nodes
200
400
600 Number of nodes
QoD PIMRP
800
1000
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Figure 10. Number of nodes vs Throughput comparison
In figure 10, the comparison of throughput parameter is shown. In this comparison figure, number of nodes is represented in the x axis and throughput value is depicted in y axis. The throughput of the proposed work is increased considerably by utilizing the multiple roué paths
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for the data transmission and priority assignment where the urgent packets will be forwarded with more bandwidth allocation. 7.3. Energy consumption: The energy consumed by a network to complete a successful data transmission is called as Energy consumption. The numerical values obtained are given in the
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following table 4.
Number of nodes
Energy consumption(mJ) QoD 200 365 625 850 1000
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200 400 600 800 1000
800 600 400 200 0
PIMRP
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1000
200
PIMRP 30 45 330 450 730
400
600 Number of Nodes
800
1000
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Energy consumption in milli joules
QoD 1200
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Table 4. Energy comparison values
Figure 11: Number of nodes Vs Energy Consumption
Figure 11shows energy consumption of proposed system (PIMRP) is less. Number of nodes is represented in x axis and energy consumption is represented in the y axis. Priority based 23
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data transmission is done in this research work which enables network to assign the higher bandwidth to the nodes where it is required. Thus the unwanted bandwidth allocation is avoided which significantly leads to reduced energy consumption in the proposed research work. 7.4.End to End Delay: Time taken by packet reach its destination form source in a network is
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given by End-to-end delay. The numerical values obtained are given in the following table 5.
200 400 600 800 1000 QoD
500 400 300 200 100 0
PIMRP
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End to End delay in milli seconds
600
End to End delay (ms) QoD PIMRP 140 97 225 115 365 250 410 270 490 325
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Number of nodes
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Table 5. End to End delay comparison values
400
600 Number of Nodes
800
1000
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200
Figure 12. Number of nodes vs End to End delay
Figure 12 shows the comparison evaluation of the proposed and existing research techniques in terms of end to end delay. This comparison evaluation leads to prove that the proposed PIMRP gains lesser end to end delay than the existing QOD. End to End delay of the 24
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proposed research work is reduced considerably by assigning more priority to the data packets to be sent urgently and adapting the multipath data transmission. 8. CONCLUSION
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Disaster management using UAV becomes the most popular research issues in the real world. This research work implemented a method to ensure the successful packet delivery rate with the concern of prioritization in emergency areas. The overall research work ensures the
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reliable data communication between the UAV vehicles in the reliable manner. This research work also ensures the faster data transmission by focusing on the multi path data transmission factors. This is achieved by introducing the PIMRP which works by considering the interference of route paths in order to avoid the data transmission failure. This work attempts to select the
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most optimal route path by considering interference measure and different QoS attribute to make sure successful packet delivery rate. And emergency services are supported by considering the priority factors where most prioritized data packets will be transmitted by pausing the ongoing data transmission to avoid interference issues. The performance assessment of this work also
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concluded that the proposed technique can guaranteed optimal performance than existing work. Proposed PIMRP shows better performance than QOS where 8.42% increased packet delivery ratio, 19.016% increased throughput, 19.518% reduced energy consumption and 18.663% reduced end to end delay.
In future, security issues can be focused which might reduce the network performance by provide false information. Recent techniques can be utilized to improve the network performance in the future.
REFERENCES
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[1] Kirschenbaum, A. (2019). Chaos organization and disaster management.Routledge. [2] Erdelj, M., Natalizio, E., Chowdhury, K. R., &Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24-32 [3] Li, Z. and Shen, H., 2014. A QoS-oriented distributed routing protocol for hybrid wireless networks. IEEE Transactions on mobile computing, 13(3), pp.693-708. 25
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[4] Periyasamy, P. and Karthikeyan, E., 2013. Survey of current multipath routing protocols for mobile ad hoc networks. International Journal of Computer Network
and Information
Security, 5(12), p.68. [5] Cho, J., Kim, E. and Song, J., 2008. A study on enhanced multipath routing protocol in
of
hybrid wireless mesh network. In Novel Algorithms and Techniques In Telecommunications, Automation and Industrial Electronics (pp. 395-400). Springer, Dordrecht.
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[6] Velasquez-Villada, C. and Donoso, Y., 2013. Multipath routing network management protocol for resilient and energy efficient wireless sensor networks. Procedia Computer Science, 17, pp.387-394.
[7] Iqbal, Z., Khan, S., Mehmood, A., Lloret, J. and Alrajeh, N.A., 2016. Adaptive Cross-Layer
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Multipath Routing Protocol for Mobile Ad Hoc Networks. Journal of Sensors, 2016. [8] Lutimath, N.M. and Abhilash, D., Interference Aware Hybrid Multipath Protocol for Mobile Ad hoc Network. 2017, 25-34 .
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[9] Jayavenkatesan, R. and Mariappan, A., 2017. Energy efficient multipath routing for MANET based on hybrid ACO-FDRPSO. Int. J. Pure Appl. Math, 115(6), pp.185-191. [10] Singh, D., Sharma, B.K. and Kumar, A., 2014. A survey on challenges in multipath routing for adhocnetwrorks. International Journal of Emerging Technology and Advanced Engineering, ICADET-14, India (2250-2459), 4(1).
[11] Radi, M., Dezfouli, B., Bakar, K.A. and Lee, M., 2012. Multipath routing in wireless sensor networks: survey and research challenges. Sensors, 12(1), pp.650-685. [12] Vats, J., Khurana, S., Singh, T.P. and Sharma, S., 2012. Routing Protocols in MANETs: An Enhanced Multipath Routing Protocol (AODLB) using Load Balancing Approach. International
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Journal of Advanced Research in Computer Science, 3(2). [13] Varalakshmi, K.V., 2016. Energy-Saving
Routing Protocol for Battery Improvement in
Multipath Wireless Ad Hoc Networks.
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[14] Gomathy, C. and Shanmugavel, S., 2005, January. Implementation of modified Fuzzy Priority Scheduler for MANET and performance analysis with mixed traffic. In Proc. 11th National Conference on Communication (NCC’05). [15] Sajwan, M., Gosain, D. and Sharma, A.K., 2018. Hybrid energy-efficient multi-path
of
routing for wireless sensor networks. Computers & Electrical Engineering, 67, pp.96-113. [16] Rahat, A.A., Everson, R.M. and Fieldsend, J.E., 2016. Evolutionary multi-path routing for
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network lifetime and robustness in wireless sensor networks. Ad Hoc Networks, 52, pp.130-145. [17] Aswale, S. and Ghorpade, V.R., 2018. Geographic Multipath Routing based on Triangle Link Quality Metric with Minimum Inter-path Interference for Wireless Multimedia Sensor Networks. Journal of King Saud University-Computer and Information Sciences.
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[18] Rajalakshmi, S. and Maguteeswaran, R., 2015. Quality of service routing in Manet using a hybrid intelligent algorithm inspired by cuckoo search. The Scientific World Journal, 2015. [19] Adam, S.M. and Hassan, R., 2013. Delay aware reactive routing protocols for QoS in
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MANETs: A review. Journal of applied research and technology, 11(6), pp.844-850. [20] Sharma, H., 2012. QoS Oriented Reservation Based Routing Mechanism for Wireless Adhoc Networks. Journal of global research in Computer Science, 2(12), pp.22-28. [21] Aswale, S., &Ghorpade, V. R. (2018). Geographic multipath routing based on triangle link quality metric with minimum inter-path interference for wireless multimedia sensor networks. Journal of King Saud University-Computer and Information Sciences. [22] Radi, M., Dezfouli, B., Bakar, K. A., Razak, S. A., &Nematbakhsh, M. A. (2011). Interference-aware multipath routing protocol for QoS improvement in event-driven wireless
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sensor networks. Tsinghua Science and Technology, 16(5), 475-490. [23] Li, B. Y., & Chuang, P. J. (2013). Geographic energy-aware non-interfering multipath routing for multimedia transmission in wireless sensor networks. Information Sciences, 249, 2437.
27
Journal Pre-proof
[24] Jabbar, W. A., Ismail, M., &Nordin, R. (2017). Energy and mobility conscious multipath routing scheme for route stability and load balancing in MANETs. Simulation Modelling Practice and Theory, 77, 245-271. [25] Sutagundar, A. V., &Manvi, S. S. (2013). Location aware event driven multipath routing in
of
Wireless Sensor Networks: Agent based approach. Egyptian Informatics Journal, 14(1), 55-65. [26] Hou, R., Qu, S., Lui, K. S., & Li, J. (2013). Coding-and interference-aware routing protocol
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in wireless networks. Computer Communications, 36(17-18), 1745-1753.
[27] Chao, G. U., & Qi, Z. H. U. (2013). Interference aware routing for mobile ad hoc networks based on node's sending and receiving capabilities. The Journal of China Universities of Posts
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and Telecommunications, 20(4), 73-79.
[28]Peng, Y., Yu, Y., Wang, X., Song, Q., Yu, Y., & Yu, C. (2013). A new coding-and interference-aware routing protocol in wireless mesh networks. Computers & Electrical
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Engineering, 39(6), 1822-1836.
[29] Li, Z., &Shen, H. (2012). A QoS-oriented distributed routing protocol for hybrid wireless networks. IEEE Transactions on mobile computing, 13(3), 693-708. [30] Chu, S. C., Tsai, P. W., & Pan, J. S. (2006, August). Cat swarm optimization.In Pacific Rim international conference on artificial intelligence (pp. 854-858).Springer, Berlin, Heidelberg. [31] Orouskhani, M., Orouskhani, Y., Mansouri, M., &Teshnehlab, M. (2013). A novel cat swarm optimization algorithm for unconstrained optimization problems. International Journal of Information Technology and Computer Science (IJITCS), 5(11), 32. [32] Li, Q., Tu, W., &Zhuo, L. (2018). Reliable rescue routing optimization for urban emergency 77.
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logistics under travel time uncertainty. ISPRS International Journal of Geo-Information, 7(2),
[33] Manaseer, S., &Alawneh, A. (2017). A new mobility model for ad hoc networks in disaster recovery areas. International Journal of Online Engineering (iJOE), 13(06), 113-120.
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Journal Pre-proof
[34] Jahir, Y., Atiquzzaman, M., Refai, H., Paranjothi, A., &LoPresti, P. G. (2019). Routing protocols and architecture for disaster area network: A survey. Ad Hoc Networks, 82, 1-14.
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AUTHORS PROFILE
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Murugeswari.T completed the AIME in Electronics and Communication Engineering, The institution of Engineers (India), Calcutta and M.E. Power Electronics & Drives in Sri Ramakrishna Engineering College, Coimbatore, (Anna University, Chennai). At present, she is pursuing Ph.D.,in the field of Networking in Anna University, Chennai. She has about 19 years of teaching experience and 6 years of experience in industrial field. She has published about 9 technical papers in various international journals. She has presented various technical papers in 8 international and 17 national conferences. She has organized a seminar, a conference and two guest lectures. She has been working as an Assistant professor in Electrical and Electronics Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India since 2005.
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Dr. S. Rathi is an Associate Professor, Department of Computer Science Engineering, Government College of Technology, Coimbatore, Tamilnadu, India. She did her Ph.D. in Mobile Computing (Anna University, Chennai). Her fields of interests include Computer Networks, Mobile Computing, Wireless Security & the Fault Tolerant system Design. She completed her ME and BE in CSE from Government College of Technology, Coimbatore. She also leads and teaches modules at both B.E, M.E. levels in Computer Science. She published 22 technical papers in the national & international journals and 24 conferences. She has organized 2 conferences for students and faculty, 7 training programmes for faculty and 12 training programmes for students.
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CRediT Author Statement
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CRediT (Contributor Roles Taxonomy) was introduced with the intention of recognizing individual author contributions, reducing authorship disputes and facilitating collaboration. The idea came about in 2015 at a Harvard workshop and it became a collaborative effort led by the Welcome Trust and Digital Science, with input from publishers, including Elsevier, represented by Cell Press. CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to the published work.
The corresponding author is responsible for ensuring that the descriptions are accurate and agreed by all authors. The role(s) of all authors should be listed, using the relevant above categories. Authors may have contributed in multiple roles. CRediT in no way changes the journal’s criteria to qualify for authorship.
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CRediT statements should be provided during the submission process and will appear above the acknowledgement section of the published paper as shown further below.
T. Murugeswari:
Conceptualization
Methodology
Software
Data curation
Writing- Original draft preparation
Writing- Reviewing and Editing
Jo
Dr.S.Rathi:
Visualization
Investigation and Supervision
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