Secure and energy aware multi-hop routing protocol in WSN using Taylor-based hybrid optimization algorithm

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Please cite this article as: Vinitha, A., Rukmini, M.S.S., Dhirajsunehra, Secure and Energy Aware Multi-hop Routing protocol in WSN using Taylor-based Hybrid Optimization Algorithm, Journal of King Saud University - Computer and Information Sciences (2019), doi: https://doi.org/10.1016/j.jksuci.2019.11.009

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Secure and Energy Aware Multi-hop Routing protocol in WSN using Taylor-based Hybrid Optimization Algorithm

1A.Vinitha, 2Dr.M.S.S.Rukmini 1Associate

and 3Dr.dhirajsunehra

professor

Kshatriya college of engineering,Chepur,,Armoor(T.S) Chepur, Armoor, 503224 [email protected] 2Professor,

Vignan’s University,Guntur(A.P)

3Professor,

JNTU,Jagityal(T.S)

Abstract: The advancements of Wireless sensor network (WSN) in large number of applications made it common. However, the energy is a major challenge in the WSN environment as the battery-operated sensor nodes in the network consumes huge amount of energy during transmission. This work addresses the energy issue and provides an energy efficient multi-hop routing in WSN named Taylor based Cat Salp Swarm Algorithm (Taylor C-SSA) by modifying C-SSA with Taylor series. This method undergoes two stages for attaining multi-hop routing, which includes selection of cluster head (CH), and transmission of data. Initially, the energy-efficient cluster heads are selected using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for effective data transmission, the sensor nodes sends data over the CH, which transmits the data to the base station through the selected optimal hop. The optimal hop selection is done using the proposed Taylor C-SSA. Moreover, the security aware multi-hop routing is performed by introducing trust model that involves indirect trust, integrity factor, direct trust, and data forwarding rate. The proposed

Taylor C-SSA algorithm shows best performance in terms of energy, number of alive nodes, delay, and throughput values of 0.129, 42, 0.291, and 0.1, respectively.

Keywords: WSN, Energy, Multi-hop routing, Trust, LEACH

Secure and Energy Aware Multi-hop Routing protocol in WSN using Taylor-based Hybrid Optimization Algorithm Abstract: The advancements of Wireless sensor network (WSN) in large number of applications made it common. However, the energy is a major challenge in the WSN environment as the battery-operated sensor nodes in the network consumes huge amount of energy during transmission. This work addresses the energy issue and provides an energy efficient multi-hop routing in WSN named Taylor based Cat Salp Swarm Algorithm (Taylor C-SSA) by modifying C-SSA with Taylor series. This method undergoes two stages for attaining multi-hop routing, which includes selection of cluster head (CH), and transmission of data. Initially, the energy-efficient cluster heads are selected using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for effective data transmission, the sensor nodes sends data over the CH, which transmits the data to the base station through the selected optimal hop. The optimal hop selection is done using the proposed Taylor C-SSA. Moreover, the security aware multi-hop routing is performed by introducing trust model that involves indirect trust, integrity factor, direct trust, and data forwarding rate. The performance of the proposed Taylor C-SSA is analyzed and the results are compared with the existing methods. From the results, it can be shown that the proposed Taylor C-SSA shows best performance in terms of energy, number of alive nodes, delay, and throughput values of 0.129, 42, 0.291, and 0.1, respectively. Keywords: WSN, Energy, Multi-hop routing, Trust, LEACH

1. Introduction The rapid advancements in WSNs have inspired the growth of low-cost and less power- equipped devices. The sensors consist of signal processing devices, and sensing equipments that provide several capabilities for processing WSN nodes to initiate wireless communications. The sensor networks are helpful in many areas, like chemical plant, disaster area, and nuclear reactor. WSN is considered as a network that contains different nodes, which accumulates the data from neighbours and transmits the data that are sensed to its sink node in a sovereign manner (Jain & Trivedi, 2012). Thus, WSN can monitor the external environments and converts the sensed data into a user-understandable form. WSN cover lots of applications, which involves home, military, environment and hospital (Huang, et al., 2009). Moreover, the forthcoming applications for the huge-scale WSN can be considered in different fields, which include monitoring the environments, military applications, and surveillance. Moreover, the sensors are generally inexpensive and endow less battery power and thus, constrained with energy issues. The major problem in WSN is to increase network lifetime, whenever initial node is not capable to transmit the data to the sink node. In applications of data gathering, each node is responsible for sensing the data packets to sink node. The process of aggregating data minimizes data traffic and stores the energy by integrating different incoming data packets into single packet. Thus, many applications are designed for extending the network lifetime (Hussain & Islam, 2007). The efficiency of energy is a major problem in WSN, as sensor nodes are activated using battery. Thus, the usage of energy is managed for prolonging the system lifetime. The sensor node in WSN poses two roles: First is to accumulate data from the physical environment and second function is data routing from itself to surrounding nodes from the base stations and collect the data from WSN for processing. The multihop network is the common mechanism used in huge-scale network for sending data directly to the sink node (Jakobsen, et al., 2010). WSN faces energy as a major challenge while initiating the communication. Thus, the number of transmissions must be minimized for providing the effective routing to attain extended system lifetime. The wireless sensor system consists of nodes in which node is harmonized and data that are sensed are linked. The uninterrupted monitoring is a fundamental instance for this type of system. The WSN applications suffer from energy constraint as the nodes broadcast the obtained data to the sink node. Thus, usage of different paths for gathering the data in WSN has the ability to balance the energy and network lifetime. The sensor lifetime is the time, when sink node obtains data through the sensors contained in WSN. The solitary path places more overhead in a particular node thereby, causes low lifetime. Thus, balancing the energies amongst the nodes helps to maximize the network lifetime (Hussain & Islam, 2007). The routing mechanisms (Mohan & Ananthula, 2019 ; Mohan & Reddy, 2018) are important in WSN as they provide less energy consumption, latency, Quality of Service (QoS), and data throughput. As WSN is application-specific, various protocols are devised for addressing the problems caused while routing data packets. The existing protocols solve the energy issues in WSN that ranges from physical to application layer (Pantazis, et al., 2013). Several protocols (Johnson, et al., 2001), (Perkins & Royer, 1999), and (Perkins & Bhagwat, 1994) were devised for routing the data starting from initial node to target node involves Link Quality Source Routing (LQSR) (Draves, et al., 2004), are considered for routing the data packets in WSN and are known as traditional routing (Rozner, et al., 2009). In (Zhan, et al., 2012), a multipath routing protocol named Trust-Aware Routing Framework (TARF) is designed in WSN by computing the reliability of neighbouring nodes. The protocol neglects the unreliable nodes and performs routing on the basis of energy efficiency and trust evaluation (Zahedi & Parma, 2018). In (Zahariadis, et al., 2011), distributed algorithm was designed for computing the nodes reliability using Ambient Trust Sensor Routing (ATSR). Here, every node is utilized to monitor the activities of neighbour’s on the basis of particular criteria of trust and computes the value of direct trust using the neighbouring nodes. In (Babu, et al., 2011), Trust dependent Link State Routing Protocol (TLSRP) algorithm was designed for multihop routing based on indirect and direct trust. The multi-hop routing protocols are based on two different categories, Location routing protocols and data centric routing protocols. The data centric routing uses the sink node for transmitting the queries to specific regions (Maheswari, 2018). Likewise, the hierarchical routing is utilized for maintaining the energy consumption among the sensor nodes using multi-hop communication for reducing the transmitted message to the sink. Different routing protocols for sensor networks are deigned, which needs location information of the nodes for further processing. Here, the location information is used for computing the distance between the two nodes and estimate the consumed energy (Akkaya & Younis, 2003). The multi-hop routing assists the routing within the network beyond the communication range, which is constrained by energy factor. Here, delay has been minimized, but energy consumption is high and hence, the routing needs to save the energy. Therefore, researchers’ aim towards developing an energy-efficient routing protocol. The main aim of this work is to development an energy efficient multi-hop routing protocol for WSN using a hybrid optimization algorithm. In this work, two operations are carried out, which include choosing the Cluster Head (CH) and Multi-hop routing. In CH selection, optimal CH is selected using LEACH protocol and then, the data transmission is carried out using the multi-hop routing. Thus, the optimal hop for data transmission is started by placing the hops in an optimal manner, which is done using the proposed Taylor C-SSA. The proposed Taylor C-SSA is designed by incorporating Taylor series in the C-SSA, exhibiting the qualities of both Taylor series and C-SSA. Moreover, the security of the routing is maintained by including the trust model in the fitness function. The trust model contains several parameters such as direct trust, indirect trust, data forwarding rate, and integrity factors. The newly devised fitness function considers several parameters like delay, Link-Lifetime, intercluster distance, energy, intra-cluster distance, and trust model to maintain energy efficiency in the routing process. The major contributions of the research are: Proposed Taylor C-SSA: The proposed Taylor C-SSA is essential for the effective placement of the hops to progress the multihop routing. The proposed technique is obtained through the inclusion of the Taylor series in the C-SSA algorithm, which is the integration of the Cat Swarm optimization (CSO) and Slap Swarm algorithm (SSA) for determining the optimal

hops to perform the multi-hop routing in WSN. The security-aware multihop routing is devised by including the trust model in the fitness function. Thus, the multihop routing is progressed using the proposed Taylor C-SSA-based fitness function. The remaining sections are arranged as follows: Here, the section 2 describes review of the conventional methods of multihop routing along with the challenges. The proposed method of security-aware multihop routing is deliberated in section 3 and the results for proving the effectiveness of proposed method is described in section 4. At last, section 5 illustrates the conclusion. 2. Motivation The section discusses the literature survey of multihop routing techniques in WSN along with their drawbacks. Also, the challenges of the existing methods are deliberated, which can be used for designing the protocol. 2.1 Literature Reviewhe eight existing techniques based on multihop routing in WSN are elaborated as follows: Cengiz, K. and Dag, T. (2018) developed a protocol named energy-efficient multi-hop routing protocol for routing data in WSN. Here, a green routing protocol was devised for reducing the excessive overhead. The method could enhance the lifetime of network significantly that would probably reduce the overhead using an energy efficient protocol. The relay nodes were utilized in this method, which allowed the transmission of accumulated cluster data using inter cluster transmissions. Thus, the scalability of WSN was subsequently maximized and the use of relay nodes provided positive impact while dissipating energy in the WSN. This method did not suitable for large size networks. Purkait, R. and Tripathi, S (2017) developed a protocol named energy efficient cluster based routing protocol based on fuzzy logic using multi-hop routing technique, in which the size of cluster was dynamic. The configuration of size of cluster, and fuzzy logic approach was utilized for implementing the protocol. The performance was evaluated on the basis of devised protocol based on alive nodes count and other protocols. The method improved the network lifetime and attained minimal speed of dead nodes. The computational time of this method was high. Selvi, M. et al. (2017) developed a delay constrained energy efficient routing technique for multihop routing in WSN. This method provided a delay limitation and provides trustworthy routing for reducing the consumed energy by assembling proficient clusters without maximizing delay. This method improved performance based on network lifetime and the overhead caused by the method was tackled effectively. However, this method did not consider mobility parameters for improving the QoS service based on congestion control, flow control, and routing. Sert, S.A et al. (2018) developed a method named Two-Tier Distributed Fuzzy Logic Based Protocol (TTDFP) for extending the lifetime of WSNs by evaluating the efficiencies of routing. This method was termed as a distribution adaptive protocol, which could efficiently run in sensor network applications. The method used fuzzy clustering for optimizing the performance of the WSN. The method was not applicable with other optimization algorithms, like Particle Swarm Optimization (PSO) (kulkarni & Murugan, 2019) and needs addition WSN coupled parameters. Chen and H. Shen (2018) developed a method named grid-based reliable multi-hop routing protocol for performing routing in WSN. The method had the ability for balancing the energy consumption and optimized CH selection based on residual energy and location of the node. The method improved the stability period and showed improved performance on the basis of energy, delays and ensured reliable transmission. However, the method did not yield a stable and scalable protocol. Thus, nodes posing higher energy are utterly elected to work as relays. In addition, the entire consumption of energies for both transmitter and receiver had been fused for modelling the links weight amongst nodes. At last, the Dijkstra algorithm was utilized for searching the path with minimum cost. Moreover, two MH protocols were commenced on the basis of BEEMH algorithm. A. E. Fawzyet al. (2018) designed an algorithm named Balanced and Energy Efficient Multi-Hop (BEEMH) algorithm to perform multihop routing in WSN. This method was devised on the basis of Dijkstra algorithm. This method provided great interest in node’s residual energy. Thus, the nodes with higher energy were considered for transmitter and receiver node for providing the inter-cluster head communications among clustering routing protocols. The method provided effective platform for optimizing the cluster head selection using several parameters, like energy and location. However, the method did not optimize the grid regions and affected the reliable communication among the nodes that yielded to poor performance. I. S. Akila and R. Venkatesan (2018) designed a method, named geo-clustering process on the basis of location of the node to obtain effective energy-saving in WSN. This method maximized the lifetime of the network, but this method was complicated as the performance parameters adapted for the routing provided complex tradeoff between the parameters. Thus, the energy efficient clustering extended the lifetime of network, but the throughput was not acceptable. A. Laouidet al. (2017) developed a method named balanced multi-path routing algorithm based on energies of residues and the count of hops of each node for determining optimal routes and to interleave them in routing table. The method was devised on the basis of automata network modelization and Ant Colony Optimization (ACO). Here, the algorithm performance was based on optimal routes and less number of hops and provided the low energy routes, but the fault tolerance and the scalability was poor in this method. T. Huynh and C. Tran (Huynh & Tran, 2016). modelled the distributed clustering approach that offered proper trade-off between energy consumption and end-to-end delay, but the problem was regarding the selection of the optimal hops. M. Sajwan, et al. (Sajwan et al., 2018) developed the Hybrid energy-efficient multi-path routing algorithm that minimized the energy consumed at the nodes, but the distance was a major factor that affected the performance.

2.2 Research Gaps The research gaps in the multi-hop routing are listed as follows:  The energy constraints and the limited computing resources of sensors nodes are the major challenges in WSN. The major drawback is to preserve the sensor’s energies by maximizing the lifetime of WSN (Huang, et al., 2009).  The batteries were used for operating the sensor nodes, which pose a fixed energy source and thus, replacing many batteries is impractical and a major issue. Thus, the power-efficient techniques are important in WSN to extend WSN lifetime. Thus, the sink node suffers from an isolation problem that defines the isolation of the sink node, which results in energy starvation (Abdulla, et al., 2012).  The major challenge in sensor network is the security, which lie in between maximizing security and minimizing resource consumption. Also, the constraints of sensor nodes affect the privacy, which is hosted on platform of sensor node. The attacks generated on the WSN can intend one node, which becomes liable to leak confidential information and impersonating nodes (Riad, et al., 2013).  The sink node is liable to receive the interpretation of individual sensor as single packet instead it receives the data as the weighed sum during the multihop routing on the basis of Compressive sensing technique (Wang, et al., 2010).  The cost of transmission and the uneven distribution of traffic load throughout network is a challenge, which should be minimized. The usage of a large number of sensor nodes for individual compressive sensing cause’s energy consumption in an unproductive way (Heinzelman, 2000).  The distribution of traffic load and transmission cost over the network suffers from a major challenge as it should be reduced. The use of less sensor nodes for compressive sensing cause energy consumption in an ineffective way (Heinzelman, 2000). 3. Proposed Multihop Routing Using Taylor Based C-SSA Algorithm In this section, the security-aware multi-hop routing protocol based on multiple objectives in WSN is illustrated using the newly designed optimization algorithm. Here, a trust model is employed considering various trust factors, such as direct trust, indirect trust, integrating factor, and forward rate factors along with other parameters, which involves distance, delay, intra-cluster distance, link lifetime, energy and inter-cluster distance. The trust model is included for offering high security to the network. Then, the multihop routing is performed by the proposed Taylor-based Cat Salp Swarm Algorithm (Taylor CSSA). The proposed Taylor C-SSA is designed by integrating the Taylor series (Mangai, et al., 2014) with C-SSA. The proposed Taylor C-SSA and the multi objectives are used to perform security-aware multi-hop routing in WSN. The two steps are considered for multihop routing. In first step, CH is selected using the LEACH (Masdari, et al., 2013) protocol to obtain the optimal CH with maximal energy. Then, the second step is progressed by the selection of optimal hops using the proposed Taylor C-SSA based on the devised multi-objectives. Figure 1 shows the schematic diagram of proposed multihop routing based on Taylor C-SSA algorithm.

Figure 1: Proposed multihop routing using Taylor based C-SSA

3.1 LEACH protocol for cluster head selection The LEACH protocol (Masdari, et al., 2013) considers a dense sensor network containing nodes with equivalent energy, whose task is to send the data to sink node. Thus, an optimal CH is chosen for collecting and broadcasting the data to sink node. In some cases, sink is placed in far, and thus, the CH requires more energy for transmission. Thus, LEACH must select a CH in such a way that the selected node poses higher energy. Thus, the LEACH protocol uses a random number of rotations of CH for evenly distributing the energy amongst sensors. In LEACH, the nodes are completely distributed and hence, do not require any control information from the base station, and does not require the global information of the network. The network lifetime is concentrated and increased and there is no need for the information regarding the location of the node. Additionally, using the LEACH facilitates the aggregation of the collected data in the CH that minimizes the traffic in the network. LEACH is a MAC protocol that assumes the homogenous network of nodes engaged in collecting the data and transmitting to the sink node. Since the nodes consume large amount of energy, the LEACH evenly distributes the energy in the nodes so that the energy load is minimized. Here, LEACH protocol is arranged in a specific manner and once

an optimal percentage of CH is determined, then LEACH protocol proceeds in

1 rounds. For each round, a group of CH is g g denote the CH. Here, each round contains

where h represents the total number of rounds and , steady-state phase and set-up phase. The set up phase contains three subphase, which includes advertisement, cluster setup, and broadcast schedule subphase. The advertisement phase is proceeded as follows, Here each node h produces a random number in the range 0 and 1 and evaluate it with the predefined threshold. The threshold is given by, determined with size

where,

hg

 represents

g  ; if e    1 Q(h)  1  g  (c mod ) g  0 ; Otherwise the node that is never been a CH,

g denote

CH, and

c indicates

(1) update interval of current

topology. Thus, the nodes that make a decision to be a CH notifies its neighbour with advertisement packet. In cluster head setup phase, all other nodes present in the network replies the CH advertisement to inform their decisions. In broadcast phase, the replies of all the nodes are gathered for deciding the membership of particular cluster. Here, the CH produces a TDMA schedule based on total nodes in the cluster. This schedule strikes a chord among node concerning time to broadcast the message in a specific time. Finally, for transmitting data, the data are collected in the CH and then, the obtained data is sent to sink node. Here, node transmits the data to its CH and the CH then sends to base station. Thus, the LEACH protocol is used for selecting the optimal CH based on energy parameter. Hence, the CH formed by the LEACH protocol is given by, where,

n specifies the total CH using LEACH protocol.

G  {G1 , G2 ,  , Gt ,  , Gn } ;1  t  n

(2)

3.2 Proposed Taylor C-SSA for multihop routing The multihop routing in WSN has optimized the communication over the network. The multi hop routing is usually employed for effective data transmission. However, the energy is the major constraint in multihop routing. Thus, for resolving the issues of energy, the proposed Taylor C-SSA employs multihop routing in which the source node sends their data to the CH through the intermediate nodes inside each cluster. Here, each source node in the cluster sends their data to the neighbour node for minimizing the transmission energy. The proposed Taylor C-SSA derives the optimal hops based on the newly devised fitness function for progressing the routing in WSN. 3.2.1 Solution encoding The solution encoding is the representation of the solution determined using the proposed Taylor C-SSA algorithm. Here, the solution is the optimal route selected for transmitting the data. The optimization methods use the proposed Taylor C-SSA algorithm based on newly devised multi-objective fitness function for finding the optimal hop from the set of hops present in the WSN. The hops are the chosen CH that has the ability to provide effective routing in the network by decreasing the information loss that occurred throughout the transmission. Here, the proposed Taylor C-SSA algorithm is adapted for determining the best path from source node. Figure 2 represents the solution encoding of proposed Taylor C-SSA based on multiobjective fitness function. Consider

S1 is

the initial node and

S4 is

the target node, then the optimal route

S1  S 2  S 3  S 4 is selected using the proposed Taylor C-SSA and is represneted as in figure 2. S1

S2

S3

S4 Figure 2. Solution encoding

3.2.2 Fitness function based on multiple objectives for multihop routing The fitness function is evaluated for finding the optimal solution from a solution using a set of parameters. The fitness computed for proposed Taylor C-SSA uses seven parameters, namely trust model, energy, distance, link lifetime, intercluster distances, delay and intra-cluster distances. Here, the fitness is considered as a maximization function. Thus, the solution yielding the maximum trust, link lifetime, intra-cluster, energy and less delay, distance and inter-cluster distance is used for the routing. Hence, the solution providing maximum value of fitness is considered for multihop routing. The fitness of the proposed Taylor C-SSA is formulated as,





O  W1  P  W2  1  T   W3  1  X *  W4  X  W5  1  D   W6  M  W7  K

(3)

where,

W1 , W2 , W3 , W4 , W5 , W6 and W7

represents the weights computed using the fuzzy membership function *

(Dennis & Muthukrishnan, 2014). P denote the node’s energy, T refers transmission delay, X indicates the inter-cluster distance, D represents the distance between two hops, X is the intra-cluster distance and M is the link-time, and the trust model is denoted as K . The weight is computed using the following equation,

0 ; if r  f r  f  ; if f  r  p p f W   q  r ; if p  r  q q  p  0 ; if r  q

(4)

r denote the vertices of triangular membership function T ( f ) . Here, p is the lower boundary, q is the medium boundary with membership value 1 and r is the upper boundary with membership value 0. where,

p, q,

and

i) Energy: The network energy is defined as the summation of the energies of all hops, which indicates the energy remained in the nodes. The energy must pose a high value and is formulated as,

P where,

1 b  E J k  b k 1

b represents number of hops that take part in multihop routing, and E ( J k ) denote the energy of the k

(5) th

hop.

ii) Delay: The delay is computed using the hops that take part in routing and the delay should be less for performing effectual routing. The delay is computed as the ratio of hops needed for the routing total nodes contained in WSN and is formulated as,

T

b l

(6)

where, l represents the total nodes present in WSN, and b indicates the total number of hops needed for the routing. iii) Intra-cluster distance: The intra-cluster distance is computed by summation of distances between hop and the individual nodes present in the hop and is minimal. If the intra-cluster distance is minimum, then the nodes are closer to the hop thereby the energy and information loss is reduced. The formula of intra-cluster distance is expressed as,

where,



represents the regularization factor,

  b s    X ( J k , Lt )  1  X   k 1tk  b    (7)   th th X ( J k , Lt ) denote the distance between the k hop and t node, the

total nodes is represented as s . iv) Inter-cluster distance: The ratio of the distance computed between two clusters is known as inter-cluster distance and must be maximal for providing the effective routing. The formula of inter-cluster distance is given by,

where,

 n n     X (Gx , Gt )   X *   x 1t  x 1        X (G x , Gt ) denote the distance between two clusters, and n represents the total CH.

(8)

v) Distance: The summation of the distance computed between the two hops represented in equation (9). The distance should be minimal for multihop routing and is represented as,

  b 1   X  J k , J k 1   1  k 1  D   b      

(9) vi) Link Lifetime: The network lifetime is derived from the link lifetime and should be maximal to attain effective routing. The link lifetime is represented as,

1 b 1 M ( J k , J k 1 ) M   b k 1  where,

(10)

M ( J k , J k 1 ) represents the link lifetime of the k th hop and (k  1) th hop.

vii) Trust model: The trust model (Zhu, 2018) (Das & Islam, 2012) provides security in the proposed technique during the routing process. Trust computation model is used for computing the trust of agents in the presence of suspicious behaviour. Several parameters are considered for computing the trust, which involves direct trust, indirect trust, forwarding rate factor, and integrity factor. Here, each hop in the WSN provides higher trust degrees for evaluating the trust level among the hops and the neighbouring hop. It offers higher scalability as the value is computed using the information of network topology. The trust model is formulated using four parameters named direct trust, indirect trust, forwarding rate factor, and integrity factor and is represented as,



K  Kd  Ki  KF  KI d

i



F

(11) I

where, K represents the direct trust, K is the indirect trust, K denote the forwarding rate factor, and K indicates the integrity factor. a) Direct trust: The direct trust (Das & Islam, 2012) is also termed as local trust and it presents the trust value that an agent calculates from the familiarities while interacting with the target agent.

K  (k , k  1)  fun d z y

 

where, K measure,

d z y

y

represents the direct trust for

th

z

transaction and

th

time interval,

z y (k , k

 1)

fun represents

(12) the satisfaction

y indicates transactions, z is the time interval, k represents the evaluation hop and the k  1 specifies the hop to

be evaluated. The satisfaction measure is used to compute the satisfaction degree of an agent that has about the specified service. Thus, the satisfaction measure keeps the record of the satisfaction level using exponential averaging update function which is given by,

fun yz ( k , k  1)    fun v  (1   )  fun yz 1 ( k , k  1) where,

funv

denote the satisfaction value of recent transaction, and

(13)

fun (k , k  1) denote the satisfaction value of z y 1

y  1 transaction at z th time interval,  indicates the weight. 0 ; if trnasaction is fully unsatisfactory  funv  1 ; if trnasaction is fully satisfactory  (0,1) ; otherwise  The weight  varies based on accumulated deviation

Z (k , k  1) z y

(14)

and is given by,

 Y  j

 yz (k , k  1) 1  Z yz (k , k  1)

 (k , k  1) | fun (k , k  1)  funv | z y

z y 1

(15) (16)

Z yz (k , k  1)  j   yz (k , k  1)  (1  j )  Z yz1 (k , k  1) where, Y denote the threshold and poses fixed value and is set to 0.25,

j

(17) indicates the user defined constant factor,

 (k , k  1) represents the recent error, and Z (k , k  1) is the accumulated deviation. At first, the weight  is set to z y

z y

1 and changes according to equation (15). b) Indirect trust: Indirect trust (Das & Islam, 2012) is calculated from the experience gained by other hops. Each hop uses the experience of other hops to provide effectual decisions for each transaction. To attain indirect trust, each hop request other hops to provide the recommendation about the other hop. The resultant hop collects the suggestions from other hops alongside feedback credibility of the recommended hops. Thus, the indirect trust of is given by,

th k th hop with respect to (k  1) hop

 aV {k } H yz (k , a )  ( K d ) zy (a, k  1)  ; if | V  {k} | 0 ( K ) (k , k  1)    H yz (k , a) aV {k }  0 ; If | v  {k} | 0 i

z y

(18)

k  1 , a denote a hop, which interacts with other hops for z making the prediction about keeping trust, feedback creditability is denoted as H y . The feedback credibility is utilized for V

where,

represents the set of agents interacted with

computing the accuracy of the feedback information that the recommended hop provided to the estimator. Thus, the feedback creditability is formulated as,  ln( N yz (k , k  1)) ; if N yz (k , k  1)   1  H (k , k  1)   ln  0 ; Otherwise  z y

N

where,

z y denote

(19)

the similarity. The similarity measure is described as to determine till what extent the two hops are

similar. Here, the similarity is computed by finding the personalized difference based on satisfaction rating with respect to the interacted agents and then utilized the difference rating for describing the similarity. Thus, the similarity of two hops and

( k  1) is formulated as,  z 1  N yz 1 (k , k  1) ; if R yz (k , k  1)    N y 1 (k , k  1)    z N y (k , k  1)   N yz 1 (k , k  1)  z ; otherwise N y 1 (k , k  1)   

 denote

where,

the similarity deviation constant,

punishment factor, and

R (k , k  1) z y

E represents

the set of agents,

 and 

k

(20)

represents reward and

denote personalized difference and is given by, R yz (k , k  1) 

aE ( k ,k 1) ( fun yz (k , a)  fun yz (k  1, a))

2

| E (k , k  1) | (21) c) Forwarding rate factor: The nodes in WSN have less energy that is dispersed while sensing and transferring the data. Thus, it becomes possible for analyzing and judging if the node is assailed or not by evaluating the forwarding nodes data. Thus, the forwarding rate factor (Zhu, 2018) is given by,

( K F ) z (k , k  1)  A (k , k  1) z

where,

represents the count of feedback packets,

A z (k , k  1) B z (k , k  1)

(22)

B (k , k  1) denote the count of packets to forward, z

k is the evaluation hop, and k  1 represent the hop to be evaluated. d) Integrity factor: Whenever the data packet is transmitted to the neighbouring node, the source node examines if the data packet is interfered or not and detects if the data packet is transmitted with specific time, and assures the integrity and correctness of the data. The integrity factor (Zhu, 2018) is formulated as, U z (k , k  1) K (k , k  1)  z E (k , k  1) I

where,

U (k , k ! ) z

is the number of completely forwarded packets and

E (k , k  1) z

(23)

denote the number of packets

to forward. 3.2.3 Proposed Taylor C-SSA algorithm for optimal hop selection This section elaborates the proposed Taylor C-SSA, which is the integration of Taylor series (Mangai, et al., 2014) and CSSA inheriting the advantage of both Taylor series and C-SSA algorithms. The Taylor series is used to predict the linear part and describes the historical stored values. The advantage of Taylor series is simpler and easiest method to compute the solutions, even under the presence of the complex functions. The main advantages include that the Taylor series ensures the accurate estimation of the common functions and attains convergence easily. Moreover, the C-SSA is designed by integrating the CSO (Chu, et al., 2006) and SSA (Mirjalili, et al., 2017) algorithm and acquires the qualities of both the algorithms and inherits a proper tradeoff between exploration and exploitation phases, to attain best solution. The algorithm is simpler to execute, and contains a single control parameter, which is updated adaptively. The C-SSA exhibits the advantages of both the algorithms that exhibits a proper balance between the exploitation and exploration phases, and obtain the global optimal solution. The C-SSA is easy to implement and possess a single control parameter that updates adaptively. Thus, the main aim of the hybrid algorithm is to resolve the issues associated with the standard SSA algorithm through the integration of CSO. The proposed algorithm is capable of obtaining the global optimal solutions for the unimodal, multimodal, and composite data. Thus, the goal of the proposed Taylor C-SSA algorithm is to solve the limitations of C-SSA algorithm by integrating the Taylor series so that Taylor-C-SSA becomes capable to obtain global optimal solution for composite data. The integration of the two techniques assists to decide an optimal solution and enhances the convergence and provides an optimal solution to attain effective multihop routing. The steps that are carried out on the proposed Taylor C-SSA algorithm are discussed below:

i) Initialization The algorithm is started with the initialization phase in which the population of Salp is initialized and is represented as,

C  {C1 , C 2 ,  , C o ,  C u }

where, Co represents the position of

o th Salp, and u is the position of

u

th

(24)

Salp.

ii) Evaluation of fitness function The next step is to evaluate the multiobjective fitness function for attaining the best solution. The multi-objective fitness is generated by utilizing seven objectives, namely distance, delay, inter-cluster distance, intra-cluster distance, link lifetime, energy, and trust as is expressed in equation (3). The fitness of each solution contained in the population is evaluated based on the derived fitness function. The solution providing best fitness value is considered as the optimal solution. iii) Computation of the new solutions In this step, the solutions are updated based on the proposed algorithm. CSO algorithm possesses higher search-ability and provides proper tradeoff between the exploitation and exploration phases, to obtain the global optimal solution. Thus, the standard equation of CSO is used for obtaining the better solution. Thus, the update position of the CSO (Chu, et al., 2006) algorithm is utilized based on position and the velocity of the cat at the current iteration. Once the prey is determined, the velocity of the cat is changed accordingly to catch the prey. Hence, the update position of the cat o is given by,

C uo1  C uo   uo1 where,

(u  1)

th

C

o u

represents the position of the cat

o

at current iteration u , and



(25)

o u 1 is

the velocity of the cat

o in

iteration.

After determining the position of the prey, it decides to move and thus, the velocity of the cat is changed and the updated velocity

 uo1 is represented as,

 uo1   uo  1  1 (C *  C uo ) where,



is a random number,

1 denote constant, C

*

represents the best solution, and



o u

(26)

represent the velocity of cat

o in u th position, C uo represents the cat’s position in current iteration. The velocity of the cat is to be changed according to Taylor series (Mangai, et al., 2014) to improve the accuracy of estimation and in addition to attain the convergence. Moreover, the Taylor series is simple and easy to compute under complex functions. Thus, the resultant velocity equation obtained by applying Taylor series is given by,  uo1  0.5 uo  1.3591 uo1  1.359 uo2  0.6795 uo3  0.2259 uo4  0.555 uo5

(27)

 0.0104 uo6  1.38e 3 uo7  9.92e 5 uo8

Thus, the velocity of the cat in current iteration is given by, uo

uo1  1.3591uo1  1.359uo 2    (28) 1  o o o    0.6795u 3  0.2259u  4  0.555u 5 0.5   3 o 5 o o    e e 0 . 0104  1 . 38  9 . 92   u 6 u 7 u 8  

The velocity update using the Taylor series is done by substituting the equation (28) in equation (26) that is given below.  uo1  1.3591uo1  1.359uo2  (29)   1 o o o o   1  1(C*  Cuo )  0.6795u 3  0.2259u 4  0.555u 5 u 1  0.5   o 3 o 5 o   0.0104u 6  1.38e u 7  9.92e u 8  uo1 

uo1

1.3591uo1  1.359uo2  0.6795uo3    1 o 1  0.2259uo4  0.555uo5  0.0104uo6   1  1(C*  Cuo ) u 1  0.5 0.5   1.38e3uo7  9.92e5uo8  

 

1.3591uo1  1.359uo 2  0.6795uo 3    1  0.2259uo 4  0.555uo 5  0.0104uo 6    1  1 (C *  Cuo )  0.5   3 o 5 o 1.38e u  7  9.92e u 8 

(30)

(31)

Rearranging the above equation, the resultant equation can be written as,  2 .7182 uo1  2 .718 uo 2  1 .359 uo 3   (32)    0 .4518 uo 4  0 .111 uo 5  0 .0208 uo 6    1  1 (C *  C uo )   o o  0 .00276  u  7  0 .0001984  u  8  2 .7182  uo 1  2 .718  uo  2  1 .359  uo  3   (33)    1  1 ( C *  C uo )   0 .4518  uo  4  0 .111 uo  5  0 .0208  uo  6     o o  0 .00276  u  7  0 .0001984  u  8 

 uo1

 uo 1

From above equation, the velocity of the cat in current iteration can be written as,  2 . 7182  uo  1  2 . 718  uo  2  1 . 359  uo  3   



 0 . 00276  uo  7  0 . 0001984  uo  8 

 

 uo   0 . 4518  uo  4  0 . 111  uo  5  0 . 0208  uo  6  

(34)

The C-SSA algorithm assures better accuracy and the effectiveness for multihop routing. The update in C-SSA is derived by combining the update rule of CSO (Chu, et al., 2006) and SSA (Mirjalili, et al., 2017). Additionally, the C-SSA provides a proper balance between the exploitation and exploration phases, to generate the global optimal solution According to CSSA algorithm, the update equation is given by,

C uo1 

1  1  1  o 1  uo  1  1  C *   C u   1  1  1 1  2 1  1   (35)

The final Taylor C-SSA is obtained after substituting equation (34) in (35), and is represented by,     1  1  1  o 1  Cu  Cuo1  1  21  1     

where,



is a random number,

 2.7182uo1  2.718uo 2  1.359uo 3      * o o o 0.4518u  4  0.111u  5  0.0208u  6    1  1  C  (36)   o o  0.00276u  7  0.0001984u 8   1  1  1     

1 denote constant, C * represents the best solution, and  uo represent the velocity of cat

o in u th position. 4 Results and Discussion In this section, the analysis of proposed method based on alive nodes, energy, delay, and throughput is carried out with respect to other existing methods. 4.1 Experimental Setup The proposed method is implemented in MATLAB having Windows 10 OS, 2.16 GHz processor and 2GB RAM. The simulation parameters are provided in Table 1.

Table 1. Simulation Parameters Parameters simulator area no.of nodes transmission range simulation time receiver energy consumption transmitter energy consumption initial energy traffic type

Values matlab 100 x 100 m 50 and 100 40m 2000 rounds 5x10^-8 J 5x10^-8 J 0.5 J CBR

4.2 Performance metrics The performance metrics are employed for analyzing the performance of the methods that are used for secured multihop routing. The metrics utilized for the analysis includes alive nodes, energy, throughput, and delay. 4.2.1 Energy: The transmission of the data between the nodes in the WSN consumes the energy. Thus, the node having highest energy is chosen for the evaluation. 4.2.2 Delay: The delay is computed based on the number of nodes. If the number of nodes increases, then the delay is high. The delay should be less to achieve effective routing. The delay is computed from the time taken by application request or information to give a response. 4.2.3 Alive nodes: Alive nodes are the nodes used for initiating the communication in a WSN.

4.2.4 Throughput: The throughput is defined as the number of data packets obtained in a specific time and then the packet delivery is acknowledged.

Throughput  N r where, N r is the total number of nodes obtained and

T

T is the simulation time.

4.3 Comparative Methods The performance of the proposed method can be evaluated by comparing the performance achieved by the existing methods. Thus, the comparative methods employed for the analysis include C-SSA, Grid clustering (Huang, et al., 2017), Geo clustering (Akila & Venkatesan, 2018), FABC-EACO clustering (Kumar, et al., 2017), and proposed Taylor C-SSA.

4.4 Simulation Results

(a)

(b)

(c)

(d) Figure 3. Simulation results a) Using 50 nodes with 2 hops b) Using 50 nodes with 3 hop c) Using 100 nodes with 2 hops d) Using 100 nodes with 3 hops

This section illustrates the simulation results of proposed multihop routing approach using 50 and 100 nodes. The simulation of proposed Taylor C-SSA is executed in WSN model with 50 and 100 nodes as depicted in figure 3. The WSN model with 50 nodes and 2 hops during the transmission is depicted in figure 3a. Similarly, the WSN model with 50 nodes and 3 hops is depicted in figure 3b. Here, the nodes indicated by green, red, blue and yellow circles are the sensor nodes, which transmit the message, whereas the triangular shaped node represents the cluster head, which is responsible for transmitting the data packets from one node to another node. Likewise, the network with 100 nodes using 2 hops is depicted in figure 3c and the network with 100 nodes using 3 hops is depicted in figure 3d respectively.

4.5 Comparative analysis The comparative analysis of the performance achieved by the Taylor C-SSA-based routing with the existing approaches in terms of delay, alive nodes, energy, and throughput using 50 nodes and 100 nodes with 2 and 3 hops is illustrated. The existing methods used for the analysis are C-SSA, Grid clustering, Geo clustering, FABC-EACO clustering. 4.5.1 Analysis based on 50 nodes and 2 hops: Figure 4 shows the comparative analysis using 50 nodes with two hops based on alive nodes, energy, delay, and throughput. The analysis of the existing C-SSA, Grid clustering, Geo clustering, FABC-EACO clustering, and proposed Taylor C-SSA method (Set A) based on alive nodes is depicted in figure 4a. Initially, 50 alive nodes are considered by the Set A, when the number of rounds is 1. On the other hand, when the number of rounds is 2000, then the alive nodes measured by Set A are 24, 21, 13, 5, and 25 respectively. Figure 4b depicts the comparative analysis of the Set A based on delay parameter. When the number of rounds is 1, the corresponding delay values measured by Set A are 0.011, 0, 0, 0, and 0.0126 respectively. Similarly, when the number of rounds is 2000, the corresponding delay values measured by Set A are 0.316, 0.524, 0.501, 0.481, and 0.291 respectively. Figure 4c shows the comparative analysis based on energy for the Set A. When the number of rounds is 1, the corresponding energy values measured by Set A are 0.549, 0.548, 0.548, 0.549, and 0.549 respectively. Likewise, when the number of rounds is 2000, the corresponding energy values measured by Set A are 0.055, 0.044, 0.044, 0.009, and 0.131 respectively. Figure 4d shows the comparative analysis based on throughput for Set A. Initially, the throughput value of the Set A is 1, when the number of rounds is 1. However, when the number of rounds is 2000, the throughput value is 0.1 for Set A. 4.5.2 Analysis based on 50 nodes and for 3 hops: The comparative analysis using 50 nodes for three hops based on alive nodes, energy, delay, and throughput is depicted in figure 5. The analysis in terms of alive nodes is depicted in figure 5a. Initially, 50 nodes are considered for the evaluation. When the number of rounds is 2000, then the alive nodes computed by Set A is reduced to 9, 7, 4, 1, and 10, respectively. Figure 5b depicts the comparative analysis of the methods based on delay parameter. When the number of rounds is 2000, the corresponding delay values measured by Set A are 0.505, 0.607, 0.521, 0.520, and 0.475, respectively. The comparative analysis based on energy for the method is depicted in figure 5c. When the number of rounds is 2000, the corresponding energy values measured by Set A are 0.152, 0.142, 0.108, 0.0526, and 0.188, respectively. Figure 5d depicts the comparative analysis based on throughput for Set A. When the number of rounds is 2000, the throughput value is subsequently reduced to 0.1 for Set A.

(a)

(b)

(c)

(d) Figure 4. Comparative analysis using 50 nodes with two hops based on a) Alive nodes b) Delay c) Energy d) Throughput

(a)

(b)

(c)

(d) Figure 5. Comparative analysis using 50 nodes with three hops based on a) Alive nodes b) Delay c) Energy d) Throughput

4.5.3 Analysis based on 100 nodes and 2 hops: Figure 6 illustrates the comparative analysis using 100 nodes with two hops based on alive nodes, energy, delay, and throughput. The analysis in terms of alive nodes is depicted in figure 6a. At first, 100 nodes are considered for the evaluation when the number of rounds is 1, whereas when the number of rounds is increased to 2000 then the number of alive nodes measured by Set A is reduced to 39, 0, 12, 25, and 42 respectively. The comparative analysis of the methods in terms of delay parameter is depicted in figure 6b. For 2000 rounds, the corresponding delay values measured by Set A are 0.508, 0.688, 0.858, 0.691, and 0.488 respectively. Figure 6c shows the comparative analysis in terms of energy parameter for the method is depicted. When the number of rounds is 2000, the corresponding energy values measured by Set A are 0.119, 0, 0.003, 0.0654, and 0.129 respectively. Figure 6d depicts the comparative analysis based on throughput for Set A. Initially, the throughput value is 1 for Set A. Likewise, when the number of rounds is 2000, the throughput value is subsequently reduced to 0.1 for Set A.

(a)

(b)

(c)

(d) Figure 6. Comparative analysis using 100 nodes with two hops based on a) Alive nodes b) Delay c) Energy d) Throughput

4.5.4 Analysis based on 100 nodes and for 3 hops: The comparative analysis using 100 nodes with three hops based on alive nodes, energy, delay, and throughput is depicted in figure 7. The analysis in terms of alive nodes is depicted in figure7a. Initially, 100 alive nodes are considered by the Set A for the evaluation when the number of round is 1. But, when the number of rounds is 2000, then the alive nodes computed by Set A are reduced to 30, 0, 1, 15, and 31 respectively. Figure 7b depicts the comparative analysis of the methods based on delay parameter. When the number of rounds is 2000, the corresponding delay values measured by Set A are 0.485, 0.621, 0.889, 0.687, and 0.465 respectively. The comparative analysis based on energy for the method is depicted in figure 7c. When the number of rounds is 2000, the corresponding energy values measured by Set A are 0.081, 0, 0.005, 0.041, and 0.090 respectively. Figure 7d depicts the comparative analysis based on throughput for Set A. When the number of rounds is 2000, the throughput value is subsequently reduced to 0.1 for Set A. 5.5 Discussion This section illustrates the comparative result of methodologies in terms of energy, delay, throughput, and alive nodes. Table 2 depicts the comparative results obtained using a network consisting 50 and 100 nodes with two and three hops based on the evaluation metrics at the maximum rounds.

(a)

(b)

(c)

(d) Figure 7. Comparative analysis using 100 nodes with three hops based on a) Alive nodes b) Delay c) Energy d) Throughput

Table 2 Performance Comparison Metrics

Energy

NodesC- Grid Geo FABC- Proposed SSA clustering clustering EACO Taylor clustering C-SSA 50 0.152 0.142 0.108 0.052 0.188 100

Delay

50 100

Throughput 50 100 Alive nodes 50 100

0.119

0

0.003

0.065

0.129

0.316

0.524

0.501

0.481

0.291

0.485

0.621

0.889

0.687

0.465

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.1

24 39

21 0

13 12

5 25

25 42

The comparative analysis of the methodologies is conducted by taking the best results from the network with 50 and 100 nodes. The energy values computed by the proposed Taylor C-SSA is highest with value 0.129, whereas the existing methods, like C-SSA, Grid clustering, Geo clustering, FABC-EACO clustering are 0.119, 0, 0.003, and 0.065 respectively. The delay values must be bare minimum value in order to get improved performance. Here, the proposed Taylor C-SSA shows minimal value with 0.291, whereas the existing C-SSA, Grid clustering, Geo clustering, FABC-EACO clustering are 0.316, 0.524, 0.501, 0.481 respectively. The maximum throughput is achieved by Taylor C-SSA with value 0.1. The maximum alive node is acquired by the proposed Taylor C-SSA with 42 alive nodes. Therefore, it can be concluded from the above comparison result, that the proposed Taylor C-SSA technique outperforms the existing approaches with improved performance. In the proposed work, the CHs are selected using the LEACH protocol, which minimizes the traffic in the network. Then, the proposed hybrid optimization algorithm selects the optimal paths based on the energy constraints, such as energy, delay, inter-cluster distance, intra-cluster distance, Link-Lifetime, distance, and trust model. The proposed Taylor C-SSA is easy to implement and possess a single control parameter that updates adaptively. The proposed algorithm is capable of obtaining the global optimal solutions for the unimodal, multimodal, and composite data. The Taylor series is used to predict the linear part and describes the historical stored values. The advantage of Taylor series is simpler and easiest method to compute the

solutions, even under the presence of the complex functions. The main advantages include that the Taylor series ensures the accurate estimation of the common functions and attains convergence easily. 6. Conclusion This paper focuses on a security aware multi-hop routing protocol and considers security as an important paradigm for performing the multihop routing. The protocol is designed by considering a trust model, which incorporates several trust factors such as direct trust, indirect trust, data forwarding rate, and integrity factor. The process undergoes two stages for attaining effective multihop routing. First stage is CH selection, and second stage is data transmission. In CH selection, the optimal node is selected as CH using the LEACH protocol and then the data transmission is performed from one node to another node based on different hops which is selected optimally using the proposed Taylor C-SSA based multiobjective fitness function. 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Conflict of Interest: none