Link quality and energy utilization based preferable next hop selection routing for wireless body area networks

Journal Pre-proof Link quality and energy utilization based preferable next hop selection routing for wireless body area networks Kashif Naseer Qureshi, Sadia Din, Gwanggil Jeon, Francesco Piccialli

PII: DOI: Reference:

S0140-3664(19)31083-7 https://doi.org/10.1016/j.comcom.2019.10.030 COMCOM 5997

To appear in:

Computer Communications

Received date : 31 August 2019 Revised date : 13 October 2019 Accepted date : 24 October 2019 Please cite this article as: K.N. Qureshi, S. Din, G. Jeon et al., Link quality and energy utilization based preferable next hop selection routing for wireless body area networks, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.10.030. 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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Link Quality and Energy Utilization based Preferable Next Hop Selection Routing for Wireless Body Area Networks

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Kashif Naseer Qureshi1, Sadia Din2, Gwanggil Jeon3, Francesco Piccialli4 1

Department of Computer Science, Bahria University, Islamabad, Pakistan School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea 3 Department of Electrical Engineering and Information Technology, University of Naples Federico II, Napoli, Italy 4 Department of Embedded Systems Engineering, Incheon National, University, Incheon, Korea.

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[email protected], [email protected], [email protected], [email protected]

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Abstract

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The rising population and cost of medical services triggered the new technologies including Wireless Body Area Networks (WBANs) based on smart and intelligent biosensors nodes for sensing and monitoring the patient vital signs. The biosensor nodes have implanted inside or outside the human body to send the medical information to medical centers. For data dissemination in these services, different types of solutions and model have been designed to address the interference, body movement, disconnection quality of services issues in the network. This paper presents an Energy Aware Routing (EAR) protocol to minimize energy utilization and select preferable next hop by evaluating the link quality of sensor nodes. The proposed protocol evaluates the energy level, link quality, and remaining energy level to balance the load, minimize the energy utilization, and enhance the data transmission. Various simulations have conducted to evaluate the proposed protocols performance in terms of energy consumption, data delivery, delay, and data throughput. Experimental results indicated that the proposed protocol has a better mechanism for date routing and better solution to minimize the energy of sensor nodes in WBANs. Keywords: - Energy Efficiency, Routing, Link Quality, Interference, Energy Consumption, Data delivery

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1. Introduction

According to World Health Organization, around 17.5 million people have suffered only due to heart attacks [1]. There are various factors of death in all over the world but the mostly cause of poor medical services and proper disease diagnosis [2]. Based on advance communication technologies, Wireless Body Area Networks (WBANs) applications have offered healthcare monitoring systems by smart sensor nodes. These applications provide intelligent and smart monitoring systems to manage and monitor the patient health signs by early detection. The sensor

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nodes are attached (inside or outside) with patient body for sensing the initial signs and transmit for further analysis to sink node or other devices [3, 4]. The bio-sensor nodes are small in size and equipped with sensing and communication capabilities, Nano and micro-technologies, and low power batteries [5, 6]. Regular heart rate monitoring applications provide basic information to the patient to survive normal life without any physical existence in hospitals and medical centers. In addition, these applications offer comfort by eliminating the wires, provide real-time continuous health monitoring for early disease diagnosis [6].

Network Side

Users Side

• GSM, UMTS • LTE, LTE-A • WLAN • WiMAX • Satellite

• IEEE 802.15.6 • ZigBee/IEEE 802.15.4 • Bluetooth • Bluetooth Low Energy • Wireless USB

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Medical Centers Side

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Data communication depends on routing protocols through IEEE 802.15.6 and other communication standards. Basically, the WBANs networks have categorized into different types such as inter, intra and beyond as shown in Figure 1 [7-9]. The intra category is based on implanted sensor nodes for monitoring the real-time vital signs of patient and collect the data for further investigation to inter category. The inter category is working as a gateway between intra category sensor nodes and medical centers. The inter category forwards the data to beyond category which is based on the cloud computing and wireless technologies. Data communication rely on network size, adopted technology, network topology and hardware specifications [10, 11]. Sensor nodes are also categorized based on their responsibilities and capacity such as ordinary nodes, centralized nodes, coordinator nodes. Ordinary nodes monitor the data and send to centralized nodes, which are working as administrators.

• Client Applications • Access Points • Servers

Figure 1: Wireless Body Sensor Area Networks Architecture at Medical Centers, Network and User Side Scenarios

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Explains the previous work and their routing processes in Section 2. Presents the proposed protocol design, routing metrics and algorithm in Section3. Discusses the experimental setup and simulation results in Section 4. Paper concludes in last section with future direction.

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Sensor nodes have limited resources in terms of batteries, processing power and data storage. Limited resources have serious impacts on network operations such as nodes depletion, overhead, data loss and delay. In addition, due to patient movement’ the routing faces complexities in the network. Another significant issue is battery life of sensor nodes, which have serious impact on data communication where the battery replacement and charging is not possible especially for inside implanted sensor nodes in patient body [12, 13]. The sink nodes have extra responsibilities to collect the data from ordinary nodes and further send to other devices. All applications of WBANs need real time data monitoring and have sensitive patient data, which needs privacy, and in time data delivery. Due to complex network scenarios, routing has suffered and not reach to destination within specific time. In addition, some other factors also have negative impact on data routing such as short communication range of nodes, interference, path loss and resource allocation issues [14, 15]. However, due to unique features of WBAN, the existing protocols are not well suited to handle network issues. There is a need to design an efficient data routing for reliable data services. To address, these issues of data routing in WBANs, we propose an Energy aware next node selection protocol using link quality and energy level for routing decision. The proposed protocol enhances the data routing and utilize less energy. The paper contributions are as follows:

2. Related Work

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Heterogeneous advance communication technologies have different issues related to scalability of networks, limited single standard or model, different quality of services factors and data security. In WBANs, different types of technologies have adopted where every technology has own features and requirements such as IEEE 802.15.6 uses ZigBee, IEEE 802.16 uses WiMAX, IEEE 802.15.6 uses Bluetooth and IEEE 802.11 uses WiFi [16, 17]. Traditional Wireless Sensor Networks (WSNs) based solutions are not feasible for WBANs due to critical and complicated human body, which requires different frequencies. Medical data monitoring requires reliability and in time data delivery. In addition, patient body movement also has serious impact on sensor nodes performance such as wrist based sensor nodes moving with hand movement. Different types of solutions have designed to overcome routing challenges associated with human body monitoring such as cross layer, thermal-aware, cluster based, delay-tolerant, and Quality of Service (QoS) [18-20]. In this section, we review some selected solutions to address above challenges using different methods. Liang, et al. [21] proposed a Reinforcement Learning based Routing Protocol (RL-QRP) for WBANs for data routing. This protocol selects optimal route through ordinary and sink nodes.

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This protocol utilizes sensor geographical information, duty cycle, and buffer status and consider traffic load factor. In order to achieve the data routing, this protocol adopts reinforcement-learning method. In this process, the ordinary sensor nodes check the quality parameters of neighbor nodes and store the record for future decisions. With many advantages, this protocol still suffered due to its computational complexities, which leads to network overhead. The network overhead is resulting to consume more energy, which leads to nodes depletion.

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Djenouri and Balasingham [22] proposed a protocol to enhance QoS services in WBANs. This protocol designed to provide reliable data routing, maximizing energy consumption and improve latency. Basic idea of this proposed protocol is classifying the data routing into different types based on suitable routing metrics. In addition, to fulfill the network requirements, protocol uses memory and computation estimation method to estimate the values for multi paths toward the sink node. In addition, this protocol also considers energy and select more powerful sensor node for data routing in the network. This protocol is designed for media access layer. The all-medical data is very significant and need to route on time in the network. With many advantages, this protocol has suffered with high network overhead due to its memory and computation estimation method. More processing time consumes more energy where sensor nodes depleted early in WBANs.

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Zeng, et al. [23] proposed a Real-Time Data Report and task Execution (RTRE) protocol for WBANs. This protocol uses a multi-actuator coordinator and multi-event task assignment methods. The main objective of this protocol to enhance the energy level with timely reactions of sensor nodes. In this protocol, the routing process is collecting the data by collaboratively from mobile actuator and ordinary sensor nodes. Actuator nodes have more capabilities in terms of energy due to self-awareness reaction tasks method compared to ordinary sensor nodes. The actuators nodes have ability to gather the data from ordinary sensor nodes due to its mobility mechanism and priority assignment to important data. However, in this type of protocol where all the load on actuator nodes still suffered with complexities issues due to body movement and complex topologies. On the other hand, the ordinary nodes have more energy due to less load compared to actuator nodes.

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Khan, et al. [24] Energy efficient Peering Routing (ERP) proposed a protocol for data routing. This protocol is especially developed to handle indoor WBANs applications. ERP estimates the communication cost of neighbor sensor nodes and add this information in its routing table. This protocol selects the downstream sensor node by residual energy and geographical position of nodes. ERP also has control mechanism to minimize the hello packet transmission. The design of this protocol is based on three main objectives where the first objective is controlling the hello packets generation. In the second objective, protocol construct routing tables to update the neighbor nodes position and location. In last objective, protocol construct owns updated routing table. The routing table construction is itself a load on sensor nodes and also utilize more time for data routing. It also leads to network overhead and ultimately time consuming methods. Time is one of the significant factor and requirement for real-time data routing applications.

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Alwan and Agarwal [25], proposed a Multi objective QoS Routing (MQoSR) protocol for WBANs. The routing process of this protocol is based on next node selection from neighbor nodes using their location and link information. The location is identified through GPS services and link metric evaluates through distance and sink node reliability plus energy level of neighbor nodes. In addition, data transmission, end-to-end delay and network lifetime are taking into account. MQoSR uses on demand protocols method and creates multiple node-disjoint using fault tolerance approach. The routing policies are based on source and sink nodes available QoS requirements. QoS requirements are depending on applications of WBANs. However, there are various different types of QoS requirements needed in network and focus to fulfill all requirements need more computational processing which leads to network overhead in the network.

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Jamali [26] presented a Stable and more Reliable in Multi-Path QoS Multicast Routing (SRMQMR) protocol that focuses on providing and efficiently utilizing the required bandwidth. The routing process is achieved by utilizing feasible bandwidth using TDMA-based time slots. The basic idea to support the QoS routing protocol is the availability of path to establish communication between source and destination nodes. The transmission process is carried out through a spare time slot where the node should be ready to receive the data according to the transmission time slot. When source node generates the route request packet for required bandwidth then the bandwidth is calculated based on link between source and neighbor nodes. This protocol introduces an idea for stability, which is based on statistical data collected by a node about its neighboring nodes and mobility prediction of nodes and their link expiration time. For route maintenance and to ensure that the routes are broken or not all the route links are inspected from source to destination. The SR-MQMR shows its strength by optimizing the high data delivery, resource allocation, less overhead and stability of route. However, this protocol utilizes more bandwidth to perform routing in the network where nodes consume more energy and depleted.

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Khan, et al. [27] proposed an energy efficient routing protocol for WBANs to address the energy consumption issues. The basic idea behind this protocol is setting two sensor nodes for recording the critical data and not part of multi-hopping routing. The cost function of this protocol sets by two parameters including minimum distance and maximum energy level. In addition, this protocol also adopts distance parameters where more distance leads to energy consumption issues. The threshold value has considered where less value is set for dead nodes. In order to address the energy consumption issue, this protocol uses multi-hop strategy for regular data. Single hop routing is also taken into account whenever any critical data need to forward. The forwarder node main responsibility is collecting the data and further transmit to sink node. After completion of one round in the network, the forwarder node has selected based on distance towards the sink node and maximum residual energy of nodes. However, the link quality or signal strength is another significant parameter to select the forwarder node and only distance and energy level is not feasible because the high energy level sensor node not give surety of better signal strength in the network.

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As stated in literature, the sensor nodes 80% of energy consumed due to routing processes. The WBAN networks have limited resources and short communication range where the interference and path loss chances are more. Direct communication through sensor nodes towards gateway node consume more energy. The existing routing protocols have adopted various different types of tradeoff mechanism to maximize energy level in WBAN. However, many protocols select the route with shorter path rather than link quality evaluation. The above discussed routing protocols have neglected the link quality parameter for next hope selection which degrades the network performance in terms of data loss and disconnectivity issues. This motivation has adopted to design a new link quality based routing protocol for WBAN. 3. Protocol Design

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Proposed routing protocol is designed for healthcare monitoring applications. In this section, we present the proposed protocol ERP routing process, and its next hop selection strategy in WBANs. This section also discusses some assumptions, proposed protocol model and its operational methods. Before explaining the proposed routing protocol design and routing process, there are some assumptions for modeling the network. We assume that all deployed sensor nodes have same transmission range in the network. All nodes are aware about neighbor nodes locations. The sink node has more capabilities compared to normal sensor nodes and collected the data periodically in the network. Figure 2 shows the physical and logical model of WBAN network for ERP implementation. Sink node is one the body and a gateway node is placed near with patient or at any suitable place where patient exist. Sink node is used for data collection because in state of the art protocols’ the sink node is located at near with patient and in mostly cases not able to collect the data due to various sensor nodes data and patient mobility or movement.

Figure 2: Physical and Logical Model of WBAN Architecture

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3.1 Protocol Architecture

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The transitional ad hoc networks adopt beaconing approach to update the sensor nodes location. The beacon messages have short information of sensor nodes location. These beacon messages broadcast periodically in the network and update the nodes if any change occurs in the network in terms of sensor node position and place. These short messages have some drawbacks such as network overhead, utilizing sensor processing power and energy consumption. In order to overcome beaconing load in WBANs, the proposed protocol adopts handshake mechanism by using control messages. The control messages have some significant information of residual energy and link quality of sensor nodes. The process to exchange the control messages among sensor nodes control the overhead of beaconing and make network more reliable in terms energy utilization and data delivery. Whenever any sensor node wants to transmit the data it sends the control message to its neighbor node then the neighbor nodes calculate the residual energy and link quality of neighbor nodes parameters and reply with score function value and acknowledgment to sender node. The multi-metric score function is based on routing metrics including residual energy and link quality. After receiving the acknowledgment message, sender node initiates the data routing. The control messages establish a reliable path among sensor nodes without beaconing overhead in WBANs. In next sub section, we explain that how the routing metrics calculated among sensor nodes.

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a) Energy Level: The energy consumption is one of the main concern to design any routing protocol for WBANs. There are different reasons behind energy consumption and depletion of sensor nodes such as routing overhead, wrong and complex routing decisions, interference, and path loss and bit error rate. Recharging or replacing the sensor nodes batteries are difficult in these networks due to inside placement of sensor nodes in human body. There is only solution to replace the inside sensor nodes is minor or complex surgeries. By calculating the energy level, the high energy level sensor node will be next forwarder for data routing. The energy level of sensor node checks by Equation 1. 𝑆𝑁𝑅−𝑒𝑛𝑒𝑟𝑔𝑦 = 𝑆𝑁𝐴−𝑒𝑛𝑒𝑟𝑔𝑦 − 𝑆𝑁𝐶−𝑒𝑛𝑒𝑟𝑔𝑦 (1)

Where, 𝑆𝑁𝑅−𝑒𝑛𝑒𝑟𝑔𝑦 presents the senor node residual energy, 𝑆𝑁𝐴−𝑒𝑛𝑒𝑟𝑔𝑦 average energy and Cenergy consumed energy. Received and transmitted bits among sensor nodes evaluate the consumed energy level. Consumed energy is calculated in Equation 2.

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𝑆𝑁𝐶−𝑒𝑛𝑒𝑟𝑔𝑦 = 𝑇𝐵𝑎 × 𝑆𝑁𝑇−𝑒𝑛𝑒𝑟𝑔𝑦 + 𝑅𝐵𝑏 × 𝑆𝑁𝑅−𝑒𝑛𝑒𝑟𝑔𝑦 (2)

Where 𝑇𝐵𝑎 , 𝑅𝐵𝑏 are received and transmitted bits in sensor nodes. 𝑆𝑁𝑇−𝑒𝑛𝑒𝑟𝑔𝑦 in addition, 𝑆𝑁𝑅−𝑒𝑛𝑒𝑟𝑔𝑦 denote the total value of received and transmitted energy and calculated as in Equations 3 and 4. 𝑆𝑁𝑇−𝑒𝑛𝑒𝑟𝑔𝑦 = 𝑆𝑁𝑇𝑁−𝑒𝑛𝑒𝑟𝑔𝑦 + 𝑆𝑁𝑇𝐴−𝑒𝑛𝑒𝑟𝑔𝑦 × 𝐷𝑖𝑠 (3)

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𝑆𝑁𝑅−𝑒𝑛𝑒𝑟𝑔𝑦 = 𝑆𝑁𝑇𝑁−𝑒𝑛𝑒𝑟𝑔𝑦 (4) Where 𝑆𝑁𝑇𝑁−𝑒𝑛𝑒𝑟𝑔𝑦 and 𝑆𝑁𝑇𝐴−𝑒𝑛𝑒𝑟𝑔𝑦 present the energy needed for transmitter. The needed energy denotes with TN-energy and TA-energy showing transmitter amplifier and Dis denotes the distance between next forwarder and source node.

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b) Link Quality: After energy level calculation, the next parameter is link quality where the data transmission is evaluated by measuring link quality. The timer method uses for data forwarding decisions in the interval of (0, 1).

1 𝐿𝑄 = {− 0

𝐿𝑄𝐴𝑀𝑎𝑥 − 𝐿𝑄𝐴𝑗 𝐿𝑄𝐴𝑀𝑎𝑥

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Link quality evaluates by using Equation 5

𝑖𝑓 𝐿𝑄𝐴𝑗 > 𝐿𝑄𝐴𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑖𝑓 𝐿𝑄𝐴𝑊𝑜𝑟𝑠𝑡 < 𝐿𝑄𝐴𝑗 < 𝐿𝑄𝐴𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝑖𝑓 𝐿𝑄𝐴𝑗 < 𝐿𝑄𝐴𝑊𝑜𝑟𝑠𝑡

(5)

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In order to check the link quality, the links are classified using 10-90 rating values based on Packet Reception Ratio (PRR) as used in [28]. Different values are assigned to presenting the connectivity level between sensor nodes including connected links PRR>90%, disconnected PRR<10% and the transitional state is between 10% and 90%. Link Quality Average (LQA) thresholds: LQAOptimal and LQAWorst are used for connected and disconnected states. Furthermore, these thresholds further classified into link j for disconnected. Whenever the receiver (RVi ) sensor node receives a packet (LQAj) or less than (LQAWorst ) or this is classifying into connected link (LQAj) when value is higher than (LQAOptimal) or, this is classifying into transitional for (LQAj) range between LQAOptimal and LQAWorst. The source node S is connected with FNi with higher probability of fast data transmission with LQ=0. For disconnected state the LQ returns with value 1 and considered low quality link. In last, the transitional link has values 0 or 1 and showing unreliable LQ.

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After evaluating the residual energy and LQ, now the time is for combining these routing metrics into one function for decision. An aggregation function has utilized to combine the routing metric into single value. The IEEE 802.11 standard’s random back-off timer is based on a slot timer, which contains random number. Score function is based on multiplication of specified variables where L routing metrics are αi{αi1, αi2}. For every routing metric, the source or candidate node ni has a range of values between [α𝑀𝑖𝑛 , α𝑀𝑎𝑥 ]. We assumed that proposed protocol has maximized 𝑖 𝑖 values. We use multi-metric function as used in [29] and calculated as in Equation 6. β

β

β

ℎ (𝛼𝑖1, 𝛼𝑖2 ) = Y × αi11 , αi22 , … … . . αiLL + Xmax (6)

In above Equation, the X presents the score function which has maximum value h (αi1,αi2). The Y denotes the variable dependent weights for limited condition. The values (𝛼1 and 𝛼2 ) are L

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weight array, which is used to give priority for routing decision. High value is considering which has more impact on self-election process.

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After evaluating the routing metrics and high score function, the routing decision initiates to select next forwarder node. Routing process of ERP shows in below flow chart in Figure 3 and algorithm in Algorithm 1. Start

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Listen to the Channel

If channel is idle, broadcast control hello messages to neighbor nodes

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if sink node available

No

No

Wait for random time

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if neighbor node found

Yes

Call the wighting function with neighbor nodes and check a) Residual Energy b) Link quality Calculate ti Set timer to [ti] Yes

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Higher Weighting Score

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Node select as a next forwarder

Send the data packet to sink node

Exit

Figure 3: Proposed protocol flow chart

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Algorithm 1: ERP (Next Forwarder Selection) Input: Initiates Weighting Score

Listen to channel If Channel is idle Broadcast Hello message to Nnodes if find Sink Node do a. Forward data packets 6. else 7. Call weighting score a. Calculate RE (residual energy) b. Calculate LQ (Link Quality)

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a. weighting score =1 b. Select NFN 9. else a. Wait for random time b. Repeat from line 5 10. end if 11. end if 12. exit

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Output: NFN (Next forwarder node)

4. Performance Evaluation

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This section presents the performance evaluation of ERP and compare it with MQoSR and SRMQMR routing protocols. In order to evaluate the performance, the various simulations have carried out using well know packet level simulator NS-2.34. The NS-2.34 is one of the feasible event driven simulator for analysing the dynamic nature of communication networks. This simulator is an open source platform and supports 802.11 and MAC layer implementation. All the required functions of internet protocols, transmission control protocols and user datagram protocol exist in NS-2.34 [30]. Simulation has been carried on NS-2.34 simulator [31]. The simulation setup shows in Table 1, where the total sensor deployment area is set 10 m × 10 m. The sensor nodes are deployed in deterministic manner. The simulation time is set at 150 s where five (5) sensor nodes are deployed on patient body supervised by sink node, which is placed at centre. Each sensor node has 1m transmission range and initial energy level set at 0.5 J in the network. To evaluate the proposed protocol performance, three performance metrics have used including transmission load, energy consumption, packet delivery ratio and network overhead. These all performance metrics have positive or negative impact on energy consumption during data routing. If any sensor node has more processing overhead due to complex routing, then it

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consumes more battery and has serious impact on other sensor nodes. Transmission load is another important parameter to evaluate overall energy level in the network. Through transmission load, we check the ERP performance and its optimization in data communication. When the network has overhead, the packet delivery ratio automatically decreases which has serious impact on network especially where the critical data need to transmit in emergency cases. We also evaluate the packet delivery ratio of ERP with two protocols through various simulation rounds. The detail of simulation parameters shows in Table 1. Table 1: Simulation Parameters

4.1 Simulation Results

Simulation Parameters Communication Type Data packet size Beacon interval value

Detail Wireless 32 Bytes 3 second

Data transmission rate Node Mobility Data Transmission power

250 Kbps None 0.3 mW

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MAC layer standard Sensor node Energy Communication range Traffic type

Detail 15 300 sec MicaZ-CC2420 motes IEEE802.15.4 2 Joules 1 meter CBR

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Simulation Parameters No of sensor nodes Simulation time Sensor node assumed type

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For detail comparison analysis, different parameters need to evaluate such as network lifetime, average energy ratio, packet delivery ratio, and data packet delay. These experiments have conducted to evaluate the proposed protocol ERP with state of the art protocols. 4.1.1 Energy Consumption

In the first experiment, energy consumption has evaluated by considering number of nodes, simulation time and network area. Energy consumption refers to required energy for data transmission and receiving. Average energy consumption per transmission has a direct impact on network lifetime and less energy consumption indicates the better network lifetime.

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Figure 4 shows the average energy consumption with different number of sensor nodes. The energy consumption ratio of MQoSR and SR-MQMR is higher than ERP in all cases. The performance of MQoSR is better compared to SR-MQMR due to its routing overhead and route discovery process where protocol discover the route and transmit request packets to its neighbour nodes and bandwidth is calculated. On the other hand, the MQoSR is based on heuristic path selection mechanism and has different selection policy compared to SR-MQMR. In addition, MQoSR is on demand protocol, build node-disjoint paths, and has fault tolerant method to find alternate path in the network. MQoSR uses quality of service parameters including delay, distance, end-to-end delay, network lifetime and reliability. Due to these all QoS parameters, protocol has processing overhead and more energy consumption issues. The proposed protocol

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MQoSR

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0.09 0.08 0.07

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0.06 0.05 0.04 0.03 0.02 0.01 0 2

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Avarage Energy Consumption [j]

ERP has lighter routing matrices including energy level and link quality, which makes this protocol as a lightweight solution to solve the energy consumption issue in WBANs.

6

8

10

Number of Sensor Nodes [N]

Figure 4: Energy Consumption with Number of Sensor Nodes

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Figure 5 shows the energy consumption results with time analysis. It is clear that ERP protocol outperforms compared to state of the art protocols. This is because ERP has lightweight routing metrics and simple strategy compared to MQoSR and SR-MQMR. Energy consumption increases with time because of more sensor nodes and heavy traffic in the network. More sensor nodes are updating their information in the network and load on network increases. The trend of ERP is stable at 80, 90 and 100 seconds, which lead to better data delivery and consume less energy in the network. On the other hand, the SR-MQMR energy consumption graph is higher than MQoSR due to its mobility prediction of nodes and their link expiration time. This process is taking time, utilize more processing power, and leads to consume more energy. The ERP energy consumption is 0.04 j at 100 ms where the MQoSR and SR-MQMR is at 0.07 and 0.1 respectively.

0.12 0.1

ERP

MQoSR

SR-MQMR

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Average Energy Consumption [j]

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0.02 0 60

70

80

90

100

Time Analysis (ms)

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Figure 5: Energy Consumption with Time Analysis

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Figure 6 shows the average energy consumption with different network size. The energy consumption with network size shows that the proposed protocol ERP has less energy consumption and that is 0.05 at 500-network size where the MQoSR has 0.06 and SR-MQMR has 0.07. The proposed protocol ERP has more capabilities to handle large network size network and stability. If we compared the results of MQoSR and SR-MQMR so we observed that MQoSR is better than SR-MQMR. This trend is due to SR-MQMR routing overhead and route discovery process where protocol discover the route and transmit request packets to its neighbour nodes. On the other hand, the MQoSR is based on heuristic path selection and selection policy methods. The MQoSR has node-disjoint and fault tolerant methods and on demand protocol. It also uses prediction method, which consumes more energy and cause of sensor node depletion.

0.09 0.08 0.07

ERP

MQoSR

SR-MQMR

200

300

0.06

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0.05 0.04 0.03 0.02

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Average Energy Consumption [j]

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0.01 0 100

400

500

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Network Size

Figure 6: Energy Consumption with Network size

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The comparison of proposed ERP protocol in terms of energy consumption with different number of sensor nodes, time analysis and network size with state of the art protocols is presented in Table 2. It clearly shows that energy consumption increases with traffic load, and network size and simulation time. The rate of increment is due to protocols complex processes to select the next best path or best forwarder node. Table 2: Energy Consumption Analysis with number of sensor nodes, time analysis, network size Fig 3: Number of Sensor Nodes No of

ERP

Nodes

4 6 8 10

Fig 4:Time Analysis

SR-

Time

MQMR

Analysis

ERP

MQoSR

Fig 5: Network size SR-

Network

MQMR

Size

ERP

MQoSR

SRMQMR

0.012

0.02

0.03

60

0.014

0.04

0.05

100

0.018

0.022

0.028

0.023

0.03

0.04

70

0.027

0.04

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200

0.032

0.038

0.043

0.044

0.05

0.06

80

0.037

0.06

0.07

300

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4.1.2 Packet Delivery Ratio

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Packet delivery ratio is calculated by using number of successful received data packets at the BNC or sink node to the total number of sent data packets at the sensor nodes. In these experiments, we use number of sensor nodes, simulation time and network size to check the proposed ERP protocol with existing MQoSR and SR-MQMR for WBAN.

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Figure 7 shows the data delivery ratio analysis of proposed protocol ERP compared to MQoSR and SR-MQMR with different number of nodes. The results indicate that ERP has better data delivery ratio. The results also indicate that the results are slightly lower when the data communication among 8 to 10 sensor nodes in the network. The heavy traffic load degrades the data delivery in the network as compared to data communication between few sensor nodes as shows in graph. The proposed protocol ERP has 72% data delivery for 10 sensor nodes where the MQoSR and SR-MQMR have 65% and 60% respectively. More nodes in the networks take more time to update their neighbor nodes and process routing metrics to check best forwarder in the network. The trend clearly shows the ERP has positive results and higher percentage than state of the art protocols. 90

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In second experiment, we check data delivery with simulation time. Figure 8 shows the higher percentage of proposed ERP protocol because at the start all sensor nodes start from their initial stage and has better energy and power to communicate. With passage of time when the network routing process will stable or decline. The results with simulation time indicates the higher probability of data delivery as compared with number of sensor nodes. However, the existing protocols MQoSR and SR-MQMR have complex routing methods and taking more time at start to update and select next forwarder. The proposed protocol ERP has 80% to 89% data delivery

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with 100 ms where the MQoSR and SR-MQMR have 75% and 70% respectively. The trend clearly shows the ERP has positive results and higher percentage than state of the art protocols. 100 90

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Figure 9 shows the data delivery ratio analysis of proposed protocol ERP compared to MQoSR and SR-MQMR with different network sizes. It is observed, that ERP has better percentage with different network sizes. Even though, the ERP is suitable and feasible for 500 network size. On the other hand, the existing routing protocols MQoSR and SR-MQMR have lower data delivery. The results also indicate that the results are slightly lower when we increase the network size and data load at 400 to 500. The heavy traffic load degrades the data delivery in the network as compared to data communication between few sensor nodes as shows in graph. The proposed protocol ERP has 81% data delivery where the MQoSR and SR-MQMR have 72% and 69% respectively. Network size creates very visible change in the network and on data delivery ratio. The trend clearly shows the ERP has positive results and better choice for WBAN small and large network sizes.

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The comparison of proposed ERP protocol in terms of packet delivery ratio with different number of sensor nodes, time analysis and network size with state of the art protocols is presented in Table 3. It clearly shows that data delivery of ERP is higher with number of nodes, time and network sizes. Table 3: Data Delivery Ratio Analysis with number of sensor nodes, time analysis, and network size Fig 3: Number of Sensor Nodes No of

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4.1.3 Average End-to-End Delay Analysis We begin by evaluating the effects of ERP in terms of end-to-end delay using number of sensor nodes, time analysis and network size. Average end-to-end delay is calculated based on delay per sensor node for every transmission time period which packet takes to reach the sink node in the

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network. The average end-to-end delay is determined through total end-to-end delay divided by number of received packets and number of sensor nodes.

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Figure 10 shows the end-to-end delay of ERP is less than MQoSR and SR-MQMR in the presence of different number of sensor nodes. Specifically, ERP obtains delay of 0.016 second, while MQoSR and SR-MQMR obtain 0.02 and 0.022 respectively. The results indicate that ERP has less delay even in the presence of more sensor nodes in the network. The existing protocols MQoSR and SR-MQMR delay trend is high due to their queue size of neighbor nodes method for selecting the next forwarder node. The queue size method has serious impact on data delay. This finding may be attributed to the fact that ERP selects the high link quality node as a next forwarder, which has more chances to deliver data in time. 0.055 0.05

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In second experiment, we use simulation time as a parameter to evaluate the performance of ERP and examine the data delay time. Figure 11 indicates the less delay of ERP with simulation time compared MQoSR and SR-MQMR. The data delays are due to various different causes such as latency, overhead and complex routing processes. When design any routing protocol for WBAN, the first criteria are its less overhead mechanism and lightweight routing metrics to select the next forwarder node. The existing protocols MQoSR and SR-MQMR have complex routing processes such as prediction, fault tolerant and queue size methods. Due to these complex methods, the existing protocols have suffered and taking more time to deliver the data in the network. The results indicate that ERP has 0.02seconds delay with network size 100 where the MQoSR and SR-MQMR delay time is around 0.026 and 0.023 respectively. This finding may be attributed to the fact that ERP selects the high link quality node as a next forwarder, which has more chances to deliver data in time with less delay.

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Figure 12 shows the average delay analysis with different network sizes. Again, the results are in favor of ERP where it takes less time even though we set the network size at 500. The proposed ERP protocol is best choice to adopt with different network sizes. On the other hand, the MQoSR and SR-MQMR have taking more time for data delivery and not behave well in different network sizes. Whenever, the network size increases, these protocols are taking more time for data delivery. Specifically, ERP obtains delay of 0.024 second, while MQoSR and SR-MQMR obtain 0.030 and 0.034 respectively. This finding may be attributed to the fact that ERP selects the high link quality node as a next forwarder, which takes less time for routing decision as compared to complex predication strategies.

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Figure 12: Network Delay with Network Size The comparison of proposed ERP protocol in terms of data delay with different number of sensor nodes, time analysis and network size with state of the art protocols is presented in Table 4. It clearly shows that complex routing strategies are taking more time for data delivery. Data delay is not suitable for WBAN applications because of data seriousness. Overall, delay analysis results of proposed protocol ERP is better than state of the art protocols. Table 4: Network Delay Analysis with number of sensor nodes, time analysis, and network size Fig 3: Number of Sensor Nodes No of

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4.1.4 Throughput

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Throughput is one of the significant performance parameter to check the total successful received packets measures by packet per second. These results present the average throughput of proposed ERP protocol compared to state of the art routing protocols in the presence of different sensor nodes, simulation time and network size.

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Figure 13 shows the average throughput analysis of proposed ERP protocol with existing protocols MQoSR and SR-MQMR. In ERP, the best next forwarder is selected through link quality and residual energy calculation. The results indicate that in the presence of heavy traffic or more sensor nodes the results slightly decrease. This trend is because of data traffic load at communication channel and at network layer. The ERP has 1200 to 1800 packets throughput per second where the MQoSR has around 1100 and SR-MQMR has 1150 packets throughput. The overall, ERP has achieved better results and suitable to handle the data communication even the node increasing in the network. 2000

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In second experiment, we test ERP throughput analysis with simulation time. Figure 14 indicates almost same trend as indicated with number of sensor nodes. This means that ERP supports number of sensor nodes and simulation time. Simulation time shows the protocol stability in the network for long term of data communication. In ERP, the best next forwarder is selected through link quality and residual energy calculation, which has a positive impact on protocol stability. The ERP has 1200 to 1300 packets throughput per second where the MQoSR has around 1170 and SR-MQMR has 1050 packets throughput. The overall, ERP has achieved better results and suitable to handle the data communication.

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In third experiment, we use network size to evaluate the ERP throughput. Figure 15 shows the outstanding performance of ERP with different network sizes. The existing routing protocols have suffered when the network size increases and degraded their throughput performance. The performance of MQoSR is almost same with SR-MQMR due to its routing overhead and route discovery process where protocol discover the route and transmit request packets to its neighbour nodes and bandwidth is calculated. The ERP throughput is higher than state of the art protocols. This result indicates the high throughput compared to previous results in Figures 11 and 12. The ERP has 1210 to 1220 packets throughput per second where the MQoSR has around 1100 and SR-MQMR has 1110 packets throughput. The overall, ERP has achieved better results and suitable to handle the data communication with different network sizes.

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Figure 15: Average Throughput with network size The comparison of proposed ERP protocol in terms of data throughput with different number of sensor nodes, time analysis and network size with state of the art protocols is presented in Table 5. It clearly shows that average throughput of proposed protocol with traffic load, and network size and simulation time is high. ERP is best choice for WBAN applications. Table 5: Data Throughput Analysis with number of sensor nodes, time analysis, network size Fig 3: Number of Sensor Nodes No of

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Conclusion

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In this paper, we presented an Energy Aware Routing (EAR) protocol to minimize energy utilization and select preferable next hop by evaluating the link quality of sensor nodes. The proposed protocol has adopted residual energy and link quality as a routing metrics for routing decision. Through weighting score function, the next forwarder is selected for route the data towards the sink node in WBAN. The proposed protocol has tested in simulation in terms of energy consumption, data delivery ratio, delay and throughput. The conducted experiments are based on number of nodes, simulation time and network size. The results are indicated that ERP has better performance compared to state of the art protocols to fulfill all the routing requirements in WBAN. Proposed protocol has less overhead and efficient computational mechanism. In future, we will consider node mobility to achieve the better results. Declarations

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Availability of data and materials: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. Competing Interest: There is not any financial and non-financial competing interests in this paper. Funding: Our university support the funds. Authors Contribution: First author is the main author of this paper where second author helped first author in simulations and results generations.

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Title: Energy Utilization and Select Preferable Next hop based Energy Aware Routing Protocol for Wireless Body Area Networks

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Authors: Kashif Naseer Qureshi, Sadia Din, Gwanggil Jeon, Francesco Piccialli

Kashif Naseer Qureshi, Sadia Din, Gwanggil Jeon, and Francesco Piccialli are aware of this submission.

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Conflict of Interest None Declared.