TRUST BASED CLUSTER HEAD SELECTION WITH SECURE ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK

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TRUST BASED CLUSTER HEAD SELECTION WITH SECURE ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK

M. Sitha Ram¹, Prof. Kuda Nageswara Rao^2,Prof S. Krishna Rao³

1Research Scholar, Dept. of Computer Science and Systems Engineering

2Professor, Dept. of Computer Science and Systems Engineering

Andhra University College of Engg. (A), Andhra University, Visakhapatnam, Andhra Pradesh, India.

3Professor, Dept. of Information Technology, Sir C R Reddy College Of Engineering, Eluru

Abstract

Wireless sensor networks (WSNs) contain an excellent quantity of battery-driven small nodes that have sensing, computing and communication capabilities. Therefore, it's essential to design an energy- efficient routing protocol to cleverly use the limited energy of WSNs. One-way of managing the energy efficiency is grouping sensors to form a cluster and choose a node as lead to manage referred to as cluster head (CH). In case a malicious node or lower energy node is chosen as a cluster head, the throughput of the network is greatly affected. Thus selection of cluster heads with higher trust and residual energy becomes crucial for the overall network performance. To deal with this problem, this work proposes a Trust based Cluster Head Selection with Secure Routing (TCHS_SR) algorithm for wireless sensor network. The proposed method relies on an effective distributed trust model for cluster head selection and it also considers the secure route for data transfer. The experimental result shows that the proposed TCHS_SR algorithm reduces energy consumption and end-to-end delay, furthermore increases the throughput and packet delivery ratio efficiently.

1. Introduction

Sensors in WSN [1] are little, cheap, low-power, intelligent and disposable. The sensor nodes are self-configuring and contain one or more sensors, integrated with wireless communication devices and data processing components and a limited energy source. Due to the large number of nodes and the possibly hazardous environment in which these nodes are deployed, their batteries are often assumed to be nonreplaced. The failure of a single node in the network could possibly cause network partition and dissect a part of the WSN off from the rest of the network. Network life is, therefore, dependent on the lifetime of individual nodes. This raises the issue of energy-efficient design of the network.

In a WSN, there are probably very large number of sensing nodes and a base station. The sensing nodes have to route information concerning their surroundings to the base station. A sensing node is usually referred to as a source and a base station is usually referred to as a sink. The sink node collects and interprets the data from all the source nodes in the network. The sink node may be connected to a wired network and may not have an energy limitation. The source nodes, on the other hand, are dependent on their limited batteries and become dead when their batteries are completely exhausted. WSN protocols are different from the traditional wireless protocols due to limited power supply, large network size, and inaccessible remote deployment environment. A lot of energy is saved by using multi-hop communication than direct communication, as in short range communication due to the fact that consumption of energy is proportional to the distance. A routing protocol [2] might be good for continuous data sensing while it may not perform well where it will have periodic monitoring. These network protocol operations vary from application to application.

Clustering approach [13] is used for wireless communication in wireless sensor networks. In traditional clustering approaches, nodes send their data to the nearest cluster head (CH) which then forwards it to the base station. In each round the cluster head node sends out a beacon, and nodes that hear the beacon join the cluster. If there are too many nodes in the cluster, the cluster-head can reduce the beacon signal strength so fewer nodes will hear it. On the other hand, if the cluster is too small, the cluster

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head can increase its beacon signal strength to increase cluster membership. Thus, energy potency is raised by using clustering technique.

Clustering sensors into gathering of comparative nature to form a cluster and choose one node as lead to manage cluster referred to as cluster head [14]. The CH is dependable to gather information from member nodes and send to the base station for further processing. Furthermore, CH acts as a local data coordinator and maintains cluster information. Once malicious nodes or lower energy nodes are chosen as cluster heads, the system would be greatly affected.

Trust management system is a method used to find the node’s genuninty judgment process in the network. It aids the members of Network to deal with uncertainty about the future actions of other participants. Many researches on Trust related in WSNs are processed, but it is required to design and develop a light weight realistic trust model to evaluate, update trustworthiness of neighbor nodes and also to handle malicious nodes.

Thus selection of cluster heads with proper trust and residual energy becomes critical for the overall network performance. To deal with this problem, this work proposed Trust based Cluster Head Selection with Secure Routing (TCHS_SR) algorithm for wireless sensor network. The rest of the paper is organized as follows. Section II describes the related work about cluster head selection in WSN. In Section III, the Trust based Cluster Head Selection with Secure Routing Algorithm is explained in detail.

Section IV gives results and analysis. Section V provides conclusions from work.

2. Related Work

Momani et al [3] have defined Trust as the probability of reliability between nodes in performing actions a record is maintained with the transactions between nodes and it will form the trust directly as well as indirectly. A trust value is determined from the entries of Record.

Lopez et al [4] stated that Trust is the evaluation metric by which node X depends on node Y to fulfill its fairness in performing actions and reliability in reporting sensed data. Opinion of one none on other nodes in a network is modeled by using a mathematical representation.

Virmania et al [8] proposed a dynamic cluster head selection technique, Secure and Fault Tolerant Dynamic Cluster Head (SFDCH) Selection algorithm. The presented algorithm selects the nodes having the threshold value on top of the average. From the chosen nodes, the node with maximum available energy, at a minimum distance and having maximum throughput is selected as the cluster head .The proposed method is dynamic in nature as selection process is refreshed periodically. The Proposed SFDCH is compared with existing K-means and K-sep methods using netsim simulator. The concluded that the efficiency of SFDCH in terms of accuracy, energy efficiency and enhancement of network lifetime over the existing methods.

Liu et al [9] presented a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. They define the benefits and objectives of cluster for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In specific, they systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to their taxonomy and several significant metrics.

BDCP (LEACH) and Clustering-FL has proposed by Kumara et al [10], the extension of time to network stability before the death of the first node and the reduction of unstable time before the death of the last node. This protocol is based on the election of cluster head by the balance of the possibilities of the remaining energy for each node. In this paper, they proposed to improve Clustering techniques by Clustering -fuzzy logic (Clustering-FL). In this work, they proposed a fuzzy-based simulation system for WSNs, in order to calculate the lifetime of sensor by considering the remaining battery power, sleep time rate and transmission time rate.

Kaur [11] proposed an approach is introduced for the selection of cluster head by using swarm intelligence. This proposed approach is based on energy distributed clustering (EDC) algorithm. Honey bee optimization with some parameters is employed over EDC algorithm for effective cluster head selection. This approach helps in reducing the energy consumption. This proposed technique works in

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three stages: Cluster nodes sends data to CH, CH sends data directly to Leader and leader sends data to BS. Simulations results demonstrate that EDC-HBO algorithm improves the life time of network.

Arshad et al [12] explained the energy efficient cluster head selection in mobile wireless sensor networks on the basis of residual energy and randomized selection of the node, who is not appointed as a cluster head in previous round. The objective of this analysis work is to reduce energy consumption throughout communication and maximize information gathering at the base station. Mobile Data Collector (MDC) based routing protocol using for communication from source to destination. MDC is moving in predefined mechanical phenomenon from top to bottom in each corner of the network and transmit beacon message in every five sec for CH and BS to update the MDC location and residual energy. When CH received the beacon message from MDC, then CH measure the MDC’s energy and selects the maximum residual energy MDC to deliver the sensed aggregated data towards the base station.

The basic parameters of networks are: fixed base station that is situated far from the sensors, consistent and energy controlled sensor nodes and no high-energy nodes throughout the communication.

Based on Low Energy adaptive cluster Hierarchy (LEACH) protocols, Nguyen et al [13]

presented 2 new distance-based cluster routing protocols, that they known as DB-LEACH and DBEA- LEACH. The first approach (distance-based) chosen a cluster head node by considering geometric distance between the candidate nodes to the base station. To more improve DB-LEACH, DBEA-LEACH (distance-based energy-aware) additionally selected a cluster head not only based on distance, but also by examining residual energy of the node greater than the average residual energy of nodes within the network. They concluded that the two proposed algorithm outperformed a traditional LEACH as well as its derivatives in terms of conserving energy such that prolonging network lifetime.

Bagheri et al [14] have presented the protocol, where nodes are enabled by the GPS system. The cluster head section is based on the remaining energy of the node. The multipath routes are made through the cluster heads. A cluster head selects another path, if it fails. LEACH (low-energy adaptive clustering hierarchy) is the first cluster-based routing protocol for WSN proposed by Heinzelman et al [15]. LEACH operations can be divided into two phases. The operation of LEACH is divided into rounds with each round having a setup phase where the clusters are formed and a CH (cluster-head) is chosen for each cluster and a steady state phase where data is sensed and sent to the central base station.

Heinzelman et al [16] proposed LEACH-C, which is centralized routing protocol which is based at centralized clustering algorithm for cluster formation. Its steady state phase is the same of LEACH protocol. In set up phase each node transmits its current position and residual energy level to the BS. The BS runs the centralized cluster formation algorithm to determine cluster heads and clusters for that round.

Simulated annealing is used by LEACH-C to find close optimal clusters. LEACH-C determines cluster heads randomly; the BS makes sure that only nodes with more energy are participating in the cluster head selection. Once the clusters are created, the base station broadcasts the information to all the nodes in the network. Each of the nodes, except the cluster head, determines its TDMA slot used for data transmission.

The node goes to sleep until it is time to transmit data to its cluster head.

PEGASIS (power-efficient gathering in sensor information system), are the typical protocols hierarchical-based routing proposed by Lindsey and Raghavendra [17]. It is developed based on LEACH.

It is a classical chain-based routing protocol that is an improvement over LEACH and it forms a chain among the sensor nodes. Each node communicates with a close neighbor and takes turns being the leader in transmission to the base station. This approach reduced the average energy spent by each node per round.

Rehman et al [18] have proposed an secure CH selection calculating weight of each node to deal with secure selection using minimum energy consumption. The weight of node is a combination of different metrics including trust metric (behaviors of sensor node) which promotes a secure decision of a CH selection; in terms of this, the node will never be a malicious one. The trust metric is definitive and permits the proposed clustering algorithm to keep away from any malignant node in the area to select a CH, even if the rest of the parameters are in its favor. Other metrics of node include waiting time, connectivity degree, and distance among nodes.The selection of CHs is completed utilizing weights of member nodes.

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Wei & Yu [19] have proposed a method which relies on an effective distributed trust model for cluster head selection and it also considers the residual energy in the selection process. They showed that more trusted nodes with proper residual energy are selected as cluster heads, which in turn provides a higher packet delivery ratio from the cluster member nodes to the base station and a better balanced energy consumption of the network

Quynh et al [5] have proposed an event-based multipath clustering protocol. When an occurrence is detected, all nodes near the event will become active. One of the nodes near to the event having most residual energy elects itself as the cluster head. The rest of the active nodes join the cluster head and create the cluster. The cluster head chooses the relay node and backup relay node towards the sink to make the multipath.

Mazaheri et al [6] have proposed a QoS base multipath hierarchical routing. Among the nodes in the range r select the cluster head based on the remaining energy and the distance from the sink. For multipath construction, cluster head chooses the set of cluster heads inside the vary R (R > r) supported the residual energy, remaining buffer size, signal to noise ratio and distance to the sink.

Sharma et al [7] have proposed a cluster based mostly multipath routing protocol, that uses the cluster and multipath techniques to decrease energy consumption and increase the dependability. The basic idea is to reduce the load of the sensor node by giving more responsibility to the base station (sink).

They had implemented and compared the protocol with existing protocols and found that it is more energy-efficient and reliable.

3. Trust based Cluster Head Selection with Secure Routing Algorithm:

Because of the nature of efficiency and load balancing, hierarchical routing protocols mixed with trust management techniques can be one of the good choices to design a trustable and secured wireless sensor network where each node can trust highly on the next hop on their forwarding path. Trust management models can be used as much powerful while aiming at designing a secured and attack resistant protocol for routing in wireless sensor networks. In case of trust management models, there are various methodologies and ways exist for computing trust value of a normal sensor node and/or that of a cluster head node and afterwards the resulted trust value can be used in different ways to find a secure routing path. This section proposed Trust based Cluster Head Selection with Secure Routing Algorithm in detailed.

3.1 Cluster Formation:

Algorithm 1 explains cluster formation based on communication range. This algorithm first creates N number of nodes and randomly place in grid area. Furthermore, this algorithm sets each nodes communication range. Followed by, this algorithm finds the node which is situated in center position of grid area denoted as S1. Then, create an initial cluster with S1 and set S1 as initial cluster head.

Furthermore, this algorithm randomly selects another node ni from the node list. Then, find the distance between ni to neighbor cluster (nCr). If the distance is less than communication range, this algorithm adds ni to nCr otherwise create a new cluster with ni.

Cluster Formation

Before Clustering After Clustering

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Fig 3.1 Cluster formation architecture

Example:

For Example, Let 21 wireless sensor nodes randomly placed in a grid area. Suppose we want to group the nodes to a WSN using just their distance from base station (one-dimensional space) as follows:

n = 21

15,15,16,19,19,20,20,21,22,28,35,40,41,42,43,44,60,61,15,15,16

Initial clusters (random centroid):

k = 2 c1 = 16 c2 = 22

𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 1 = |𝑥𝑖 − 𝑐1| 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 2 = |𝑥𝑖 − 𝑐2|

Table 4.1 shows calculation of this example based on clustering in single iteration. The initial choice of centroids can affect the output clusters, so the algorithm is often run multiple times with different starting conditions in order to get a fair view of what the clusters should be.

Node Id xi c1 c2 Distance 1 Distance 2 Nearest Cluster

1 15 16 22 1 7 1

2 15 16 22 1 7 1

3 16 16 22 0 6 1

4 19 16 22 9 3 2

5 19 16 22 9 3 2

6 20 16 22 16 2 2

7 20 16 22 16 2 2

8 21 16 22 25 1 2

9 22 16 22 36 0 2

10 28 16 22 12 6 2

11 35 16 22 19 13 2

12 40 16 22 24 18 2

13 41 16 22 25 19 2

14 42 16 22 26 20 2

15 43 16 22 27 21 2

16 44 16 22 28 22 2

17 60 16 22 44 38 2

18 61 16 22 45 39 2

19 15 16 22 1 7 1

20 15 16 22 1 7 1

21 16 16 22 0 6 1

Algorithm-1: Cluster Formation Based on Communication range

Input : N wireless Sensor Nodes Output : Cluster

Step 1: Create Number of Nodes (N) and randomly place in grid area.(n1,n2,n3 …, nN) Step 2: Set the node communication range

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Step 3: Find the node (S1), which is located in center position of grid area.

Step 4: Create an initial cluster with S1 and also set as initial cluster head (Temporary) Step 5: While remaining nodes from the Node- List

Step 6: Randomly select another node ni from the Node- List Step 7: Find the distance of the neighboring cluster (nCr) to ni

Step 8: If the distance is less than communication range Step 9: Add ni to nCr

Step 10: else

Step 11: Create a new Cluster with ni

Step 12: End If Step 13: End While

3.2 Cluster Head Selection:

Algorithm 2 explains cluster head selection based on nodes trust value. This algorithm computes all nodes trust value in each cluster. Trust value is calculated based on QoS and Social properties.

Algorithm-2: Cluster Head Selection Based on Trust properties

Input : Cluster of nodes(Clu) Output : Cluster Head(CH)

Step 1: maxTrustVal=0, CH=””

Step 2: For Each node ni from Clu

Step 3: CT = Calculate Communication Trust for ni based on Eq(1) Step 4: ET = Calculate Energy Trust for ni based on Eq(2)

Step 5: DT = Calculate Delay Trust for ni based on Eq(3)

Step 6: NT = Calculate Neighbors Trust for ni based on Eq(4) Step 7: RT = Calculate Recommendation Trust for ni based on Eq(5) Step 8: niTrust = CT + ET + DT + NT + RT

Step 9: If (niTrust > maxTrustVal) then Step 10: maxTrustVal = niTrust

Step 11: CH = ni

Step 12: End If Step 13: End For Step 14: return CH

Communication Trust, Energy Trust, Delay Trust are the QOS Trust Properties considered in the TCHS_SR Routing algorithm for Cluster Head selection. Respective Trust values are calculated as follows

Communication Trust

𝐶𝑇 = ^𝑆𝐶^𝑖𝑗⁺¹

𝑆𝐶_𝑖𝑗+𝑈𝐶_𝑖𝑗+𝜀 (1)

Where SCij = Total no of successful communication between node-i to node-j UCij = Total no of unsuccessful communication between node - i to node-j

 = random number (0 to 1)

Energy Trust

Energy Trust is calculated based on Residual energy of each node. In WSN, all nodes initial energy is 100J (Joules). Each node in a network consumes certain amount of energy when sending and receiving a packet. For packet transferring 0.06J and for receiving 0.04J energy will consume.

ET = IE – EC (2)

IE is the nodes initial energy; EC is the node energy consumption.

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Delay Trust

Average Time taken to send a packet.

DT = ^∑ ^𝑇𝑇

𝑁𝑖=1

𝑁 (3)

TT = Time taken to send a packet N = Packet sent count

Neighbors Trust, Recommendation Trust are the two Social trust Properties considered along with Qos Trust properties for cluster head selection in the TCHS_SR Routing algorithm . Respective Trust values are calculated as follows

Neighbors Trust

Count the number of neighbors for a node is also called Neighbors Trust.

NT = CS – 1 (4)

Here NT is Neighbors Trust and CS is Size of the Cluster.

Recommendation Trust

Recommendation from third-party node, which is an important factor to assist trust evaluation process, often becomes the main target of some malicious attacks like unfair rating attack. When assessing recommendation data, models in some researches adopt negative strategies where subject node removes suspicious recommendations largely deviated from integrated data.

Such approaches enhance the validity of indirect trust to a certain extent, but there is no further punishment for potential attackers. To deal with the abovementioned problems, TRPM introduces the following two recommendation trust metrics to effectively compute recommendation trust: Response of recommendation request: When subject node sends a recommendation request for a common neighbor to object node, subject node checks whether a response message of the recommendation request from the same object node is received within a limited time interval.

If it is true, it will be counted as a successful recommending participation; otherwise counted as a failure. Recommendation accuracy: If subject node receives recommendation data from object node which serves as a recommender, it compares the recommendation data with the direct trust of recommended neighbor node. If the difference between two kinds of data is lower than a certain threshold ϕ, it will be counted as a successful recommending participation; otherwise counted as a failure.

According to such two metrics, recommendation trust of object node is shown as 𝑅𝑇_𝑖𝑗= ^𝑆𝑅𝑇^𝑖𝑗⁺¹

(𝑆𝑅𝑇𝑖𝑗+1)+ (𝑈𝑅𝑇_𝑖𝑗+1) (5)

where RTij represents the recommendation trust of i to j, while SRTij and URTij denote the total numbers of successful and unsuccessful recommending participation via recommendation trust metrics respectively.

3.3 Secure Routing Algorithm:

Source node requires all available routes to destination node. Secure Routing algorithm is explained in Algorithm 3. First Source node generates Route Request (RREQ) which contains its own id

& destination id (Step 1). Then source sends RREQ to its Cluster Head (CH) (Step 2). Followed by, CH checks destination node is available or not in its cluster (Step 3). If destination is unavailable in its cluster, CH attaches its Id to RREQ (Step 4) and forwards RREQ to another CH (Step 5). Then Repeat Step 3 to Step 6 until CH checks D is available in its own cluster. If destination is available in its cluster, CH collects all Ids from RREQ and generates Route Reply (RREP) with its own id (Step 7). i.e. (Source Id- Intermediate Nodes ids-Destination Id) Furthermore CH sends RREP to Source Node (Step 8). Followed by, Source node gathers all routes from received RREP’s (Step 9). Finally Source node finds the shortest route with higher trust value for secure routing (Step 10).

Algorithm-3: Secure Routing Algorithm

Input : Clusters, Source Node(S), Destination Node(D),All Cluster Heads

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Output : Shortest Route with Higher trust values Between S and D

Step 1: S generates RREQ with its own Id & Destination Id attached Step 2: S sends RREQ to CH // CH- Own Cluster Head Step 3: WHILE ( D is unavailable in own cluster)

Step 4: CH Attach its own Id to RREQ

Step 5: CH Forwards RREQ to another neighboring CH Step 6: End WHILE

Step 7: CH which contains D as cluster node collects all Id’s from RREQ and generate RREP with its own Id attached

Step 8: CH sends RREP to S Step 9: S collects routes from RREP

Step 10: From the available routes S chooses short route with higher trust value

For example, Table 4.3 shows how to find the Secure Route between 5 source nodes to 5 destination nodes, each nodes are placed in various clusters.

Table 4.3: Secure Routing Example Source Node

Id

Cluster Id Trust Score Cluster Head Destination Node Id

Secure Route

1 1 29 4 3 1-4-5-3

2 2 78 5 1 2-5-4-1

3 2 35 5 4 3-5-4

4 1 56 4 2 4-5-2

5 2 88 5 1 5-4-1

Table 4.3 shows source node-1 wants to find the secure route to destination node-3. So it sends the route request (1-3) to its cluster head-4. After receiving the route request from node-1, cluster head-4 checks destination node-3 is available or not in its own cluster. If destination node-3 is unavailable, it sends the route request (1-4-3) to neighbor cluster head-5. Followed by, cluster head-5 checks destination node-3 is available or not in its own cluster. If destination node-3 is available it sends the route response (1-4-5-3) to source node-1. Similarly, other nodes routes are detected.

4. Experimental Results & Discussions:

This section evaluates the efficiency of the proposed Trust based Cluster Head Selection with Secure Routing algorithm for eliminating the high load, high energy utilization and network lifetime problem. For simulation studies, randomly generated networks are used. This ensures that the simulation results are independent of the characteristics of any particular network topology. The results of the simulation are positive with respect to performance. Matlab is used for simulation to evaluate the TCHS_TSR algorithm. This simulation assumes that 140 mobile nodes are uniformly and randomly distributed in a 100 m × 100 m unit area. Radio propagation range for each node is 100 meters and nodes initial energy is 100 J is chosen. Base Station located in center position of the area (50, 50). In order to evaluate TCHS_TSR algorithm, compare TCHS_TSR with other famous hierarchical protocols namely LEACH [13], LEACH-C [14] and PEGASIS [15] in terms of Packet delivery ratio, Average throughput and Node average energy consumption.

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4.1 Packet Delivery Ratio:

It is the ratio between the number of packets that received by base station and the number of packets sent by the source. It is calculated by this formula:

Packet Delivery Ratio (%) = received packets number / sent packets number (6)

Various Hierarchical Protocols comparison based on Number of Nodes vs Packet Delivery Ratio Results are showed in Figure 4.1.

Figure 4.1: No.of Nodes Vs Packet Delivery Ratio

Compared with LEACH-C, PEGASIS provides better packet delivery ratio. But Compared with PEGASIS, LEACH provides better packet delivery ratio. Furthermore, Compared with LEACH, proposed TCHS_TSR provides highest packet delivery ratio.

4.2 Throughput:

Throughput is the number of (average data) packet received at the base station per bit/sec and byte/sec generated by source. It's calculated by the following Eq. (7):

Throughput (Bit/s) = (Number of Delivered Packet * Packet size*8) / Total duration of

simulation (7)

Various Hierarchical Protocols comparison based on Number of Nodes vs Throughput Results are showed in Figure 4.2.

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Figure 4.2: No.of Nodes vs Throughput

Compared with PEGASIS, LEACH provides highest Throughput. But Compared with LEACH, LEACH-C provides highest Throughput. Furthermore, Compared with LEACH-C, proposed TCHS_TSR provides better Throughput.

4.3 Average Energy Consumption:

The average energy consumption is the average difference between the initial level of energy and the final level of energy that is left in each node. It is calculated by Eq. (8)

𝐸𝑎 = ¹

𝑁 ∑^𝑁_𝑘=1(𝐸𝑖𝑘 − 𝐸𝑓𝑘) (8)

Eik = the initial energy level of a node, Efk = the final energy level of a node. N = number of nodes in the simulation. This metric is important because the energy level of the network used is proportional to the network’s lifetime.

Various Hierarchical Protocols comparison based on No of Nodes vs Average Energy Consumption Results are showed in Figure 4.3.

Figure 4.3: No of Nodes vs Average Energy Consumption

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Compared with PEGASIS, LEACH consumes less Energy. But Compared with LEACH, LEACH-C consumes less Energy. Furthermore, Compared with LEACH-C, proposed TCHS_TSR consumes less Energy.

5. Conclusion:

This work proposed Trust based Cluster Head Selection with Secure Routing algorithm for avoid malicious nodes or lower energy nodes are chosen as cluster heads and improve the throughput and packet delivery ratio efficiently. In this work, simulation results provide an insight into varying the number of nodes over the network and analyzing its impact on the various performance metrics. In all three metrics, LEACH-C and LEACH protocols are better than PEGASIS in energy consumption and throughput. LEACH and PEGASIS protocols are better than LEACH-C in term of Packet Delivery Ratio.

But proposed TCHS_TSR is better than all existing LEACH, LEACH-C and PEGASIS protocols in term of Packet Delivery Ratio, throughput and energy consumption.

For further perspective, we will try to evaluate the performance of cluster head selection based wireless sensor routing protocols with different mobility patterns, different types of traffic and other metrics.

References:

[1] Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. Wireless sensor networks: A survey.

Comput. Netw. 2002, 38, 393–422.

[2] Al-Karaki, J.N.; Kamal, A.E. Routing techniques in wireless sensor networks: A survey. IEEE Wirel.

Commun. 2004, 11, 6–28.

[3]M. Momani, “Bayesian Methods for Modeling and Management of Trust in Wireless Sensor Networks,” Ph.D. Thesis, University of Technology, Sydney, July, 2008.

[4] Javier Lopez, Rodrigo Roman, Isaac Agudo, Carmen Fernandez-Gago, “Trust management systems for wireless sensor networks: Best practices”, Computer Communications, Elsevier, 33(2010) pp.

1086 – 1093

[5] T. N. Quynh, T. T. Vinh, and M. B. T. Quynh, “Multipath routing for cluster-based and event-based protocols in wireless sensor networks”, In Proceedings of the Third ACM Symposium on Information and Communication Technology SoICT’12, pages 172–179, 2012.

[6] M. R. Mazaheri, B. Homayounfar, and S. M. Mazinani, “Qos based and energy aware multi-path hierarchical routing algorithm in WSNS”., Wireless Sensor Network, 4:31–39, 2012.

[7] Suraj Sharma, Sanjay Kumar Jena, "Cluster based Multipath Routing Protocol for Wireless Sensor Networks"., ACM SIGCOMM Computer Communication Review, Volume 45, Number 2, April 2015.

[8] Deepali Virmania, Savneet Kaurb, Satbir Jain, "Secure and Fault Tolerant Dynamic Cluster Head Selection Method for Wireless Sensor Networks", International Conference on Information and Communication Technologies (ICICT 2015) Procedia Computer Science 46 (2015) 989 – 996.

[9] Xuxun Liu, "A Survey on Clustering Routing Protocols in Wireless Sensor Networks", Sensors 2012, 12, 11113-11153; doi: 10.3390/s120811113.

[10] Sharath Kumara Y, Geetha N B, Mohamed Rafi, "Prediction of Sensor Lifetime by Using Clustering-Fuzzy Logic in Wireless Sensor Networks", IJCSMC, Vol. 4, Issue 4, April 2015, pg.835 – 841.

[11] Kiranpreet Kaur, Harjit Singh, "Cluster Head Selection using Honey Bee Optimization in Wireless Sensor Network", International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 5, May 2015.

[12] Muhammad Arshad, Mohamad Y. Aalsalem, Farhan A. Siddiqui, "Energy Efficient Cluster Head Selection In Mobile Wireless Sensor Networks", Journal Of Engineering Science And Technology Vol. 9, No. 6 (2014) 728 - 746 © School Of Engineering, Taylor’s University.

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ISSN: 2005-4238 IJAST 30

[13] Tri Gia Nguyen, Chakchai So-In, Nhu Gia Nguyen, "Two Energy-Efficient Cluster Head Selection Techniques Based on Distance for Wireless Sensor Networks"., International Computer Science and Engineering Conference (ICSEC), 2015.

[14] T. Bagheri and A. Ghaffari, “Recm: Reliable and energy effective clustering based multi-path routing algorithm for wireless sensor networks”, In Proceedings of IEEE World Congress on Information and Communication Technologies (WICT’11), pages 1340–1345, Dec 2011.

[15] Heinzelman, W. R., Chandrakasan, A., and Balakrishnan, H. 2000. “Energy Efficient Communication Protocol for Wireless Microsensor Networks.”, In Proceedings 33rd Hawaii Inter- national Conference on System Sciences, 3005-14.

[16] Heinzelman, W. B., Chandrakasan, A. P., Member, S. and Balakrishnan, H. 2002, “An Application- Specific Protocol Architecture for Wireless Microsensor Networks.” IEEE Transactions on Wireless Communications 1 (4): 660-70.

[17] Lindsey, S., Raghavendra, C., and Sivalingam, K. M. 2002, Data Gathering Algorithms in Sensor Networks Using Energy Metrics. IEEE Transactions on Parallel and Distributed Systems 13 (9): 924- 35.

[18] Eid Rehman, Muhammad Sher, Syed Hussnain Abbas Naqvi, Khan Badar Khan, and Kamran Ullah 2017, “Energy Efficient Secure Trust Based Clustering Algorithm for Mobile Wireless Sensor Network”. Hindawi Journal of Computer Networks and Communications Volume 2017.

[19] Zhe Wei and Shuyan Yu 2018 “Energy Aware and Trust Based Cluster Head Selection for Ad-hoc Sensor Networks” International Journal of Network Security, Vol.20, No.3, PP.496-501, May 2018