ENERGY EFFICIENT COST BASED ROUTING PROTOCOL FOR WSN

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1 International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 9, Issue 3, May - June 2018, pp , Article ID: IJARET_09_03_029 Available online at ISSN Print: and ISSN Online: IAEME Publication ENERGY EFFICIENT COST BASED ROUTING PROTOCOL FOR WSN Syed Mohd Ali Assistant Professor,ECE Dept, JB Institute of Engineering and Tech, Hyderabad, India Syed Abdul Sattar Principal, Nawab Shah Alam Khan college of Engineering & Tech, Hyderabad, India D Srinivasa Rao Professor, ECE Dept., JNTUH, Hyderabad, India ABSTRACT Sensor network is a distributed event-based system that differs from traditional communication network. Transporting information in the network with the quality of service and more efficiency is the goal. Collecting environment information is a major application of sensor network. There are many types of routing in wireless sensor network such as flat-based routing, hierarchical-based routing, location-based routing and quality of service based routing depending on the network structure. The main objective of this research is to design a routing protocol based on path cost and node weight (Energy and Delay) approach to distribute network state information to balance a load to save energy and maximize the network lifetime. The basic idea of this routing protocol is to choose a next hop node based on the optimal path cost and energy level of neighbour nodes i.e, route discovery will be processed with the higher node level energy rates and the alternate route will be chosen if any route causes failure due to the lack of load. Key words: WSN, Clustering, Path Cost. Cite this Article: Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao, Energy Efficient Cost Based Routing Protocol for WSN. International Journal of Advanced Research in Engineering and Technology, 9(3), 2018, pp INTRODUCTION Wireless Sensor Network (WSN) is a self-configure network of small sensor nodes, where the sensor nodes can communicate among themselves using radio signals, and these sensor nodes can sense, monitor and understand the physical environment. It consists of spatially distributed sensors to monitor physical or environmental conditions and to pass the data through the network to a destination location. The bi-directional modern networks enable to control the activity of the sensors. The development of the wireless sensor networks was motivated by military applications such as battlefield surveillance and is also used in many industrial and editor@iaeme.com

2 Energy Efficient Cost Based Routing Protocol for WSN consumer applications like industrial process monitoring and control, machine health monitoring, etc [1][2]. The WSN is built of "nodes", where one or more sensor is connected to each node. Each sensor node consist of several parts, like radio transceiver with an internal antenna to an external antenna, microcontroller, electronic circuit for interfacing with the sensors and an energy source like a battery. In Wireless network the energy and network throughput are basic objectives, due to the inconsistent bandwidth the data transmission on over network is inefficient and it takes much time to deliver the packet. Due to the lack of resources the wireless network reduces performance efficiency. This research analyze the wireless network infrastructure and network topology for understanding the pitfalls of wireless networks; here it defines the problems of inconsistent network throughput and immobile nodes The adoption of node cost function design in network protocols is a promising approach for WSN applications, due to the lack of resource [3]. The main objective of this research is to design a routing protocol based on node cost function and node weightage approach to distribute a network state information to balance a load to save energy and maximize the network lifetime. The basic idea of this routing protocol is to choose a next hop node based on the energy level of neighbour nodes i.e, route discovery will be processed with the higher node level energy rates and the alternate route will be chosen if any route causes failure due to the lack of load. We organize the mobile sink approach to reduce the network load and we organize re-route process to find out optimal. A route selection algorithm that uses the information from different layers to help the routing protocol make a decision about the next hop. Figure 1 Infrastructure less network [1] 2. BACKGROUND STUDY The concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology has been studied, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. The number of sensor nodes in a sensor network can be several orders of magnitude higher than the nodes in an ad hoc network. Sensor nodes are densely deployed. Sensor nodes are prone to failures. The topology of a sensor network changes very frequently. Sensor nodes mainly use broadcast communication paradigm whereas most ad hoc networks are based on point-to-point communications. Sensor nodes are limited in power, computational capacities, and memory. Sensor nodes may not have global identification (ID) because of the large amount of overhead and large number of sensors editor@iaeme.com

3 Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao The flexibility, fault tolerance, high sensing fidelity, low-cost and rapid deployment characteristics of sensor networks create many new and exciting application areas for remote sensing. In the future, this wide range of application areas will make sensor networks an integral part of our lives. However, realization of sensor networks needs to satisfy the constraints introduced by factors such as fault tolerance, scalability, cost, hardware, topology change, environment and power consumption. Since these constraints are highly stringent and specific for sensor networks, new wireless ad hoc networking techniques are required. Our aim is to provide a better understanding of the current research issues in this field. We also attempt an investigation into pertaining design constraints and outline the use of certain tools to meet the design objective 3. NETWORK MODEL AND ASSUMPTIONS Heterogeneous Wireless Sensor Network (HWSN) is a combination of both client and infrastructure based wireless sensor network, which provides connectivity among conventional clients and through sensor gateways. Sensor nodes and gateways communicates using different radio technologies. For example a WIFI network client device can communicate with WiMax client device. All the mesh routers are connected in a mesh topology communicates with all other mesh routers and mesh clients are connected to these mesh routers through wireless connections, and some mesh routers with gateway functionality is directly connected to internet. In our research Heterogeneous wireless sensor network is considered to perform network operations. While performing network operations like routing, packet transmission etc, all these sensor network elements consumes power which leads to more energy consumption. Therefore, minimizing the effect of energy consumption by designing energy efficient techniques and network designs has gained a eminent attention. In this paper, we consider a Heterogeneous WSN with a mobile sink. Let assume that sensor nodes are deployed randomly and sensor positions can be measured by location technology [5,6]. Each node assigned with same communication radius, and the communication is stable with the same communication radius and bandwidth assignment or data rate were defined with respective of each cluster, the mobile sink having higher data rate to distribute gathered data toward sink. The route discovery of the mobile sink is made up through an optimal route selection strategy Figure 2 Network Model [7] editor@iaeme.com

4 Energy Efficient Cost Based Routing Protocol for WSN 4. PROPOSED MODEL In this paper we propose a energy efficient cost based routing protocol in WSN. We organize this routing protocol in two different phases. One is cluster and cluster head selection process, second one is cross-layer routing and mobile sink data gathering phase 4.1. Cluster formation Phase In this phase the cluster forming decides which cluster head or gateway node should be selected, in this criteria, a sensor with tentative status, it would randomly associate itself with a cluster head among its cluster members for improving load balance. The details are presented in Algorithm 1. Fig. 1 shows the cluster formation Algorithm -1 Cluster formation and CH Selection Input: set of nodes:,,,,., clusters,,,.. neighbour list =,,., Output:,,,.. 1 Initialize network and set of possible nodes Network area Set of possible nodes 2 Compute neighbours of each node 0 1 +! = "#(% &' % & ) +(* &' * & ) &,- Compare ::! & 0 1h33 0 & :3 3. Let = -,,., are the nodes operational states and 5 be the two states of node, (where,5 =1,2,3,4) 4 The node states keep on change during the process of data distribution at time 9 &. In certain time period9, the node state change probability is measured as : ;< =: =5 - =0 5 : ;< is measured by following equations: = & : ;? : ;< (+)?,- :?< 6 The amount of energy dissipated in the subsequent time duration is 7. E d = 4 T ( y= 1 t= 1 n P xy * E y & =@ && (@ AB +@ CB +@ DE +@ &FGH ) && = Initial energy of the AB,@ CB,@ &FGH energy utilized at the time of transmission and reception of data editor@iaeme.com

5 Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao 8 Compare "@ & <@ &+ 1h33 & &,- Select CH End if Else Repeat Step - 6 Algorithm 2: Node selection algorithm Forward (3 K, L,99 ) 1: discovery L at node K; 2: if do not found L then 3: = +1 ; 4: if <=0 then 5: return; 6: end if 7: Divide evenly, consider three sub-hops & and = &, & 8: select one node K C from distant neighbours ofk; 9: choose the min M& from & ; 10: Forward (K C,L, M& ); 11: choose two nodes K1; K2 from intermediate neighbours of K; 12: forward L to K1; K2 with rest hops of & ; 13: else 14: send identified results to the initiator of L; 15: end if 4.2. Route selection using node s Cost function This is used to select the best and flawless path to increase the network s lifetime. It is mainly occupied by cost function. The main aim of cost function is to increase weight or cost of the nodes with less energy to increase the life time. Let, & A - Battery capacity of node & (residual energy) & O & A P Battery cost function of node &, Therefore, the battery cost function of node is inversely proportional to the battery capacity of the node. & O & A P=1/ & A. & O & A P=Q & S T U V U WX 1 & (1) Where, & O & A P : Cost of node & at time t Q & : Transmit power of node & editor@iaeme.com

6 Energy Efficient Cost Based Routing Protocol for WSN Y & : Full charge capacity of node & & A : Residual energy (Remaining battery capacity) of a node & at time t. 1 & : weight factor which depends upon various factors, like battery s quality, battery s capacity, life time, battery s back up, and price Cost of the path Let : Z be the path from source to destination d through intermediate nodes \+ \ at time t. : Z A =! \+ \ We consider two different costs for each path. The first cost is chosen as maximum cost of any intermediate node on the path : Z at time t, it is also called as primary cost, and it is denoted by ] O: Z A P=max & O & A P/ & : Z A (2) Where max is function that selects the maximum cost of interrelate node on the path : Z at time t.the second cost is average cost it is sum of cost of all intermediate nodes on the path : Z at time t divided by total number of intermediate nodes, it is also called secondary cost, it is denoted by ] O: A Z P= c b W Ude UOV UP (3) \ At a given time 0 t, we take a close look at node A and node B. Here we assume at certain time 0 t their velocity vectors are V1 and V2 respectively. A as a stationary node and node B as a moving sink node, then we can calculate the relative velocity vector of sink node B to node A f = f f We take A as the reference node, from the view of node A, node B distribute a data at a relative data rate (V) A period time t, node B will travel out of the transmission range of node A, the relative movement track is g g The distance during this period time t isρ, then we can get the expression according to the law of cosine. The delay of attempting to transmit a single packet over a discovered path is E;?\HA = 9? + <i The delay associated with the transmission attempt, Tc, is equal to the delay associated with a successful transmission, Ts: 9? = 9 D = + F;A; + D&bbD + ;?\ The mean total delay for one single packet with L retries is then approximately E;?\HA+j = +1.9? + <i The energy when each packet has to be transmitted with L data load 1 ika =l m,@ & n = l S,@ E;?\HA+j (+1).9? + & X <i The total energy CikA&o ika & editor@iaeme.com

7 Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao Let s take a look at the cluster based WSN displayed in Fig (3). Node A does not have a information about the network; however the node A known about the neighbour nodes and their characteristics of links, within its transmission range (i.e., nodes B, C, and D in this case). All other links are unknown to node A view. The problem develops if node A keeps choosing node D as its router node each time it needs to send data to the sink without taking in consideration other alternates. In this case, node D would deplete its battery reserve and die. To resolve this issue, the proposed algorithm-2 changes the router node dynamically. Figure 3 Route Representation in WSN [9] Algorithm 3: Optimal route selection algorithm RP (route, target ) 1: for each & in do 2: compute path weight p & ; q( A, & ) p & = Z,q( A, & ) Deploy a distance function q( A, & ) it computes the distance between node A and node A. routing path =,,,.., 3: end for 4: select the node K with max p & ; 5: build caching for at node K; Figure 4.3 Flow chart for the Routing algorithm editor@iaeme.com

8 Energy Efficient Cost Based Routing Protocol for WSN 5. PERFORMANCE EVALUATION We use Network Simulator Version-2 (NS2) to simulate our proposed algorithm. In our simulation, the channel capacity of mobile hosts is set to the same value: 2 Mbps. We use the distributed coordination function (DCF) of IEEE for wireless LANs as the MAC layer protocol. It has the functionality to notify the network layer about link breakage. In our simulation, mobile nodes move in a 1000 meter x 1000 meter region for 10 seconds simulation time. All nodes have the same transmission range of 250 meters. The simulated traffic is Constant Bit Rate (CBR). Our simulation settings and parameters are summarized in table 1 Table 1 Simulation Settings No. of Nodes 30,40,60 and 100. Area Size 1000 X 1000 Mac Radio Range 250m Simulation Time 10 sec Traffic Source CBR Packet Size 512 Receiving Power Sending power Idle Power Initial Energy 10.3 J Rate 2Mbps Routing protocol EECr 5.1. Performance Metrics We evaluate mainly the performance according to the following metrics. Average Packet Delivery Ratio: It is the ratio of the number.of packets received successfully and the total number of packets transmitted. Average Packet Drop: It is the average number of packets dropped by the misbehaving nodes. Delay: It is the time taken by the packets to reach the receiver. Energy Consumption: It is the amount of energy consumed by the nodes for the data transmission. We compare our Energy Efficient Cost Based Routing Protocol with an Artificial Neural Network based Energy-Efficient and Robust Routing Scheme ELDC [10] technique Results In our first experiment we vary the number of nodes as 30,40,60 and 100.Some of the metrics are as follows which are used for understanding the performance of routing approach and for comparing it with ELDC[10] editor@iaeme.com

9 Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao Figure 4 Average Throughput: EECR vs ELDC Fig 4 Present average throughput comparison of EECR vs ELDC. EECR performs better throughput than ELDC. According to the results both the protocols performance was decreased while number of nodes increase Figure 5 Packet Delivery Ration: EECR vs ELDC According to Fig. 5, EECR has better packet delivery ratio than ELDC for different network topologies. The packet delivery ratio of EECR protocol is around 94.72% and for ELDC is about 76.34% editor@iaeme.com

10 Energy Efficient Cost Based Routing Protocol for WSN Figure 6 Average Energy Consumption: EECR vs ELDC Fig. 6. Presents an average energy consumption rate of EECR and ELDC while compare to ELDC the energy consumption ratio is less the energy consumption rate is almost 16 % less than ELDC. Figure 7 End-to-End Delay: EECR vs ELDC editor@iaeme.com

11 Syed Mohd Ali, Syed Abdul Sattar and D Srinivasa Rao Fig. 7 Presents the comparison of EECR, and ELDC end to end delay performance where the ELDC protocol end to end delay increased while number of nodes increased and EECR 17% rate delay improved while compare to ELDC 6. CONCLUSION In this paper we design an energy efficient cross-layer routing protocol for WSN to increase the energy efficiency of sensor networks. In this paper the proposed cross-layer the proposed routing protocol discovered optimal route based on efficient energy nodes minimizing routing overheads using mobile sink data collector. Our performance results obtained with the configurations used demonstrate that our cross-layer approach is effective in reducing unnecessary routing maintenance operations invoked by the ELDC protocol when receiving nodes are within the transmission range of the transmitting node. REFERENCES [1] M. Sujeethnanda, P. Nayak, and G. Ramamurthy, A Novel Approach to an Energy Aware Routing Protocol for Mobile WSN: QoS Provision, in International Conference on Advanaces in Computing and Communications (ICACC), pp. 38 4, [2] L. Tran-Thanh and J. Levendovszky, A Novel Reliability Based Routing Protocols for Power Aware Communications in Wireless Sensor Networks, in Wireless Communications and Networking Conference (WCNC), pp. 1-6, [3] T. Melodia, M. C. Vuran, and D. Pompili, The state of the art in cross-layer design for wireless sensor networks, in Wireless Systems and Network Architectures in Next Generation Internet, vol of Lecture Notes in Computer Science, pp , Springer, Berlin, Germany, [4] C.-L. Fok, G.-C. Roman, and C. Lu, Rapid development and flexible deployment of adaptive wireless sensor network applications, in Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS '05), pp , June [5] I. Hakala and M. Tikkakoski, From vertical to horizontal architecture: a cross-layer implementation in a sensor network node, in Proceedings of the 1st International Conference on Integrated Internet Ad hoc and Sensor Networks (InterSense '06), Nice, France, May 2006 [6] B. A. Forouzan and S. Fegan, Data Communications and Networking, McGraw-Hill, New York, NY, USA, 3rd edition, 2003 [7] JiqiangTanga,b, HongyuHuanga, SongtaoGuoa,c, Yuanyuan Yangd,, Dellat: Delivery Latency Minimization in Wireless Sensor Networks with Mobile Sink, J. Parallel Distrib. Comput. 83 (2015) [8] N. Zhao and L. Sun, Research on Cross-Layer Frameworks Design in Wireless Sensor Networks, in Proc. of the Third International Conference on Wireless and Mobile Communications, France, [9] ToleenJaradat, DrissBenhaddou, Manikanden Balakrishnan, Ala Al-Fuqaha, Energy Efficient Cross-Layer Routing Protocol in Wireless Sensor Networks Based on Fuzzy Logic, IEEE Xplore: 22 August 2013 [10] Amjad Mehmood ; ZhihanLv ; Jaime Lloret ; Muhammad Muneer Umar, ELDC: An Artificial Neural Network based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs, IEEE Transactions on Emerging Topics in Computing, 20 February editor@iaeme.com