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Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000

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Procedia Computer Science 134 (2018) 178–185

The 13th International Conference on Future Networks and Communications (FNC 2018)

Energy efficient clustering and routing in a wireless sensor networks Asha G.Ra, Gowrishankarb * aa

Asst.Professor, Department of Computer Science & Engineering, B.M.S.College of Engineering,Bangalore and 560019,India bb Professor, Department of Computer Science & Engineering, B.M.S.College of Engineering,Bangalore and 560019,India

Abstract Wireless Sensor Network (WSN) is instrumental in transferring the data gathered by Sensors mounted on the Sensors Nodes (SNs) to the Base Station (BS). Lifetime of WSN is solely depends on the energy/battery life of SNs and higher the battery life longer the lifetime of Network. The sustained operation of WSN is achieved through the efficient consumption of SNs energy. In the recent past several energy conservation mechanism were proposed and among them LEACH-WSN was the most widespread methodology [1]. Further, the work pertaining to the sustained energy conservation mechanisms which are enriched with bio inspired technique or neural computational systems such as PSO-PSO-WSN, PSO-GSO-WSN, GSO-KGMO-WSN, FCM-PSOGSO-WSN EBC-S or RSOM-WSN are reported [2-6]. In order to conserve energy in WSN; SNs are clustered using a set of criteria and gather the data at each Cluster Head (CH) to avoid redundant transfer of data to the BS. Further, gathered data is routed to the BS efficiently using intelligent routing process. In this paper, PSO-PSO-WSN, LEACH-WSN and EBC-S are compared with PSO-GSO-WSN, GSO-KGMO-WSN, FCM-PSO-GSO-WSN and RSOM-WSN. These methods are subjected to the Performance Evaluation (PE) in terms of alive nodes, dead nodes, energy consumption, throughput and total data/packet delivered. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The Authors. Published by of Elsevier Ltd. Peer-review under responsibility the scientific committee of the 13th International Conference on Future Networks and This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Communications, FNC-2018 and the 15th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2018. Keywords: Wireless Sensor Networks; Clustering; Routing; Energy Consumption ; Network Lifetime

1. Introduction Wireless Sensor Network is one of the most popular areas of research in the field of networking due to the constant improvement in the field of wireless technology and embedded system. In addition those WSNs are extensively deployed in both civilians and military applications [7-8].These applications include monitoring, * Corresponding author. Tel.:+91-80-26670908; fax: +91-80-26614357. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier Ltd.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 13th International Conference on Future Networks and Communications, FNC-2018 and the 15th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2018. 10.1016/j.procs.2018.07.160

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tracking, event detection, surveillance and disaster management. The long term usefulness of WSN mainly relies on the lifetime of SNs. The lifetime of SN is purely depends on its battery source. Since these batteries can be hardly replaceable; the improvement in the life time of WSNs can be achieved through the conservation of battery energy [9]. Recently, many researchers applied the concept for clustering of SNs and efficient routing to conserve the energy of SNs and these techniques will significantly improve the life time of WSN. Low Energy Adaptive Clustering Hierarchy in Wireless Sensor Network (LEACH) comprise of two phases i.e. setup phase and steady phase. In setup phase the clusters of SNs were created and Cluster Head (CH) is elected among SNs present in the cluster. Further, SNs present in the cluster will send data to CH and CH will route the data to BS through TDMA technique. In Particle Swarm Optimisation (PSO) - PSO-WSN technique, PSO is used for a clustering as well as determining the efficient route from CH to BS.The Energy Based Clustering Self Organizing Map (EBC-S) uses Self Organising Map - NN to form the cluster of sensor. Here, clusters are formed based on energy level of SN and the position of SN. The SN which having higher energy will get higher bias value and it is more likely to become the CH. Sensed Data from the nearby SNs will be sent to the CH for onward transmission to BS [10]. The Particle Swarm Optimisation (PSO) – GlowWorm (GW) Swarm Optimisation (GSO) -WSN technique, initially clusters are formed using K-means method and the same is optimised in each round using PSO. Further, routing is improved at each round using GSO mechanism. The GSO-Kinetic Glow Molecule Optimisation (KGMO) -WSN technique uses K-means mechanism to create clusters of SNs and further optimised using GSO technique to achieve energy efficiency in clustering and routes are optimised at each round using KGMO technique. In Fuzzy C-Means (FCM) PSO-GSO-WSN mechanism initial membership of cluster is determined by FCM and later at each round the cluster is optimised through PSO technique and routing process is optimised through GSO mechanism. In Recurrent Self Organising Map (RSOM) - WSN, both clustering and routing is optimised by considering the present and past condition of the SN at each round through RSOM technique. Compared to the LEACH-WSN, PSO-GSO-WSN, GSO-KGMO-WSN, FCM-PSO-GSO-WSN, PSO-PSO-WSN and EBC-S the RSOM-WSN will perform better in terms of alive nodes, dead nodes, energy consumption, throughput and total packet send to the BS. 2. Related works The lifetime of WSN is solely depends on the longevity of the batteries mounted on SNs. Hence numerous research works concentrated on increasing the lifetime of the SN’s battery by adopting various energy conservation techniques in operation of WSN. The earlier research works on energy efficiency were either concentrated on aggregation of the SNs data by clustering or focused on energy efficient routing. In recent time the energy efficiency is achieved by comprehensive approach of efficient clustering and optimised routing. In decentralized hierarchical cluster-based mechanism energy efficiency is achieved by avoiding the transmission of redundant control messages from SNs to BS. Here the clusters of SNs were formed with a criterion of intra cluster distance among SNs and CH was chosen among the set of SNs based on shortest distance between an SN and BS. At each round the CH is revalidated based on the remaining energy of SN. Thus the overloading or quick depletion of energy of a particular SN was addressed. However, this algorithm operates on multiple criteria which involve heavy computational complexity in data transfer process [11]. An Extended Ad-hoc On-Demand Distance Vector (EAODV) routing technique centred on Distributed Minimum Transmission (DMT). Here, SNs will join the multicast group to get the optimised route between SNs to BS. The SN also has an option to join different multicast group based on the characteristic or interest of a particular SN. However, the main deficiency is an extensive delay caused due to the route discovery by SNs in process of transferring the data [12]. In unequal clustering and routing protocol the energy balancing is achieved through network partitioning based on intra cluster distance. Here, clusters of unequal sizes were formed by various competitive radii and this kind of cluster formation is used for balanced the energy consumption. In choosing CH residual energy of SNs and distance

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between CH and BS were considered while choosing the route. The algorithm is less adaptive than compare to the other clustering protocols [13]. The statistical distribution method and multi-objective criteria mechanism were introduced to create effective clustering and optimised routing. A Pareto based optimisation method is used to elect the single CH among the competitive set of CHs. Thus, energy consumption is reduced by minimising the number of CHs which in turn improve the quality of the link. The residual energy of individual SNs were not considered during the rounds of operation [14]. The bio-inspired learning method, Artificial Bee Colony (ABC) metaheuristic to form low-power clusters and a cost based function for the routing phase is experimented for energy conservation. However this approach fails to scale for larger numbers of SNs [15]. 3. Proposed methodology The SNs in WSN are miniature, low power device that aggregates the operations of sensing, processing and transreceving. The SNs are deployed in the region of interest to effectively monitor the environment. In general the SNs are deployed not only sense the area of interest but also to have a good connectivity among themselves. In the proposed approach the SNs are deployed primarily for effective monitoring of the environment and the connectivity & energy efficiency is achieved through the soft computing algorithm through efficient clustering and optimised routing. Here, PSO-GSO-WSN, GSO-KGMO-WSN, FCM-PSO-GSO-WSN and RSOM-WSN uses variety of clustering & cluster optimising technique and adaptable, energy efficient & intelligent technique in routing. The processes operation of PSO-GSO-WSN, GSO-KGMO-WSN, FCM-PSO-GSO-WSN and RSOM-WSN are: 1) Initialize the N number of SN. 2) Deploy the SN randomly in region of interest and BS is positioned out of the region of sensing & wellconnected to the external world. 3) Gather the initial condition of WSN like location of SN, distance between the SNs, residual energy and Received Signal Strength (RSS) from SNs to BS. 4) Clusters were formed through popular clustering technique like K-means or FCM with a criterion of inter SNs distance and initial CH is elected among the set of SNs present in the cluster. 5) The election of CH is revalidated through the optimisation technique such as PSO, GSO or RSOM by considering the present condition of CH such as intra cluster distance, residual energy and RSS. 6) Routing is performed over the elected CHs by choosing optimal path by considering inter CH distance, residual energy of CH and RSS between the CHs by applying GSO,KGMO,PSO or RSOM technique 7) At each round BS monitors the CH and announces the energy threshold for standby SN. 8) The standby SNs and dead SNs are eliminated from the set of SNs present in the network and repeat the process from step 5. 9) The process will be continued until the threshold number of rounds of data transmission is reached. 3.1. PSO-GSO-WSN 3.1.1. PSO Clustering In a realistic situation, WSN comprises of large number of SNs with heterogeneous characteristics in terms of sensing, battery life and transreceving capability. Here, the large group of SNs can be effectively be clustered through unsupervised clustering mechanism. Hear, K-Means clustering technique is used to from initial cluster of SNs with the criterion of inter SN distance and accordingly CH is elected [16]. The centroid of cluster is further fed to PSO mechanism for selecting the optimized centroids and to find the optimal CH from a group of SNs. PSO is an evolutionary computing technique based on the principle of bird flocking. Initially, set of potential solutions called ‘particles’ are randomly initialized. In each round PSO will provide two best values named as first and the second. The first one is best solution (fitness), it is named as individual best and the second best value is obtained by particle swarm optimizer and best value is obtained by each particle of population. Comparing the present and previous

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fitness values, the global best value is determined and that is the local best. The velocity and positions of the particles are updated by using the following equations (1) and (2) [17].





v id t   w  v id t  1   c 1 r1  p id  x id t  1   c 2 r2 p gd  x id t  1  x

id

t  

x

id

t

 1



v

id

t 

(1) (2)

Where the particle velocity, position and amount of generations (iterations) are denoted as v, x and t respectively. W is the inertia weight c1 and c2 are the two positive constant. Random numbers are mentioned as r1 and r2 and it is generated in the range of [0, 1].pid and pgd are the particles best position and global best values. The global best value at each round is used for choosing CH among the set of SNs present in the cluster. 3.1.2. GSO Routing The GSO is a swarm intelligent heuristic computing technology which is consequent of biological phenomenon of firefly shining to attract mates and flying to the brightest individual. The same can be applied to the fields where the system can be modeled as nonlinear global optimization problem [18]. The agents of GSO called as GW and each agent contains luminance quantity called luciferin. The luciferin intensity is determined by the performance parameter of the individual problem. The GWs are attracted towards a GW having higher luciferin intensity and that GW is the optimal solution to the optimisation problem. Here, CH is considered as GW and luciferin intensity of each CH is computed by residual energy of CH, distance between SNs & CH, the number of SNs present in the cluster and RSS between CH & BS. In each round the new CH may be elected based on the luciferin intensity of SNs. Thus load balancing between CH and SNs are achieved. Hence optimised selection of CH and route at each round result in conservation of battery of SN and subsequently improve the lifetime of WSN. 3.2. GSO-KGMO-WSN 3.2.1. GSO Clustering Initially, clusters of SNs were formed by K-means clustering with criterion of inter SN distance and CH was elected at each cluster. Subsequently the CH of cluster is optimised through GSO technique. Here, each SN in the cluster considered as GW and luciferin intensity of each SN is calculated based on residual energy of SN, Euclidian distance and the RSS between SN & BS. In each round the CH is updated based on the luciferin intensity of SNs 3.2.2. KGMO Routing The elected CH from the clustering process, the KGMO is used to optimal the route between CH to BS. In this process the fitness function determines the intermediate CH/BS involves in the routing process, the parameter for the fitness function are residual energy of the CH/BS, inter cluster distance and number of hops between CH to BS. In each round the fitness function automatically eliminates the dead and standby SNs from the optimised path. The load balancing and energy conservation at SNs are achieved from the KGMO technique. 3.3. FCM-PSO-GSO-WSN 3.3.1. FCM and PSO Clustering Fuzzy C-Means clustering is the one of the popular clustering algorithm and the same is used for initial clustering of SNs. The SNs are allocated to a cluster based on the location and CH is elected based on Euclidean distance from an SN to a cluster center [19]. Assume that in the region of interest of sensing has M number of SNs and the same is partitioned into C number of clusters like c1,c2,c3,…,cm and the number of SNs in a given cluster. K n   cj

 kM 1 1



n jk

d

2 jk

(3)

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Where, µjk describes the degree, at which an SN k belongs to the cluster j, the µ value can be in the range of 0 to 1. The value of µ, 0 or 1 makes the crisp clustering, which is similar to the K-Means clustering. Load balancing among clusters is achieved system by having µ in the range of 0 to 1 for each SN in the WSN. The Euclidean distance between the SN k to the cluster j is given by and djk and oj are the clustering parameters to form the set of clusters of SNs. The centroid of the cluster k that is x� is determined by xk 



M j 1





M j 1

n jk



o

(4)

j

n jk

The fuzzy membership of SN j for the cluster k is given by µjk 

jk

1

 

c l 1

 d jk   d lk

  

2 n  1 

(5)

Further the clusters are optimised through PSO technique to obtain a CH. The CH is optimised in each round with the PSO parameters. The clusters formed through FCM and PSO technique contain SNs belong to the multiple clusters and will be the ideal candidate SNs for the routing process. The routes are further optimised through GSO technique at each round. 3.4. RSOM-WSN In RSOM-WSN, SNs are analogous to the neurons of RSOM Neural Network (NN) . Hence, the number of neurons in the RSOM-WSN O/P layer is equal to the number of SNs in the region of interest. RSOM is a powerful variant of Kohonen's SOM (K-SOM). The RSOM has the ability to process Temporal Sequences (TSs) through the recurrent connection and suitable bias value [20-22]. The RSOM network is a two-layer NN i.e. Input (I/P) and Output (O/P) layer. The neuron of I/P layer is fully connected to the output layer with appropriate weights. Inputs to the RSOM NN are current status of SNs and results of the previous round.

3.4.1. RSOM Clustering and Routing Initially, clusters are formed using K-means clustering technique and subsequently CH is elected. The cluster and CH is further optimised through NN by giving the current status of the SNs. The optimised routes are determined by the residual energy of CHs. The recurrent connectivity primarily aids the system in achieving the load balancing by avoiding the repetitive selection of a particular SN as a CH in clustering and choosing an SN in routing the data. 4. Results and discussion CRAWDAD dataset is used for simulating WSN environment [23]. Information pertaining location of SNs, RSS and Energy levels of SNs are obtained through the ‘trace-set’. The processed trace is fed to the simulation designed in MATLAB 2018a. Parameters for simulation setup are:

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Table 1 Simulation parameters Parameter

Value

Parameter

Value

Area

250*250 m2

Eelec

50 PJ/bit

Sensor Nodes

300

εfs

10 PJ/bit/ m2

Cluster Heads

45

εmp

0.0013 PJ/bit/ m4

Threshold Value

50

d0

87.0m

Initial energy of SNs

1.0 J

EDA

5 nJ/bit

simulation rounds

300

Packet size

4000 bits

Communication range

100 nm

Message size

200 bits

In PE, performance measures such as alive nodes, dead nodes, energy consumption, throughput and total packet delivered are analyzed for PSO-PSO-WSN, LEACH-WSN, EBC-S, PSO-GSO-WSN, GSO-KGMO-WSN, FCMPSO-GSO-WSN and RSOM-WSN.

Fig. 1. (a) Alive & standby SNs V/s Rounds; (b) Dead SN V/s Rounds.

Fig. 2. (a) Packets sent v/s Rounds; (b) Throughput V/s Rounds.

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The Fig. 1(a), represents the energy related performance measure i.e. alive & standby SNs over a period of operation/round. Higher the number of alive or standby SNs in the WSN over a number of rounds indicates efficient energy conservation. Here, LEACH-WSN is less energy efficient, PSO-PSO-WSN, PSO-GSO-WSN, FCM-PSOGSO are slightly better and EBC-S, GSO-KGMO-WSN & RSOM-WSN are very efficient in energy conservation techniques. Fig.1 (b), represents the performance measure with respect to energy consumption that is lifetime of SNs in the WSN. Higher the dead node lesser the life time of WSN. The lifetime of LEACH-WSN is inferior than compare to other technique and RSOM-WSN performs better in this PE. In Fig. 2(a), the PE related to the packet delivery ratio is plotted. The performance measure with respect to packet delivery ratio is packet delivery from SNs to BS. The RSOM-WSN delivers more packet compare to other techniques. Throughput is another performance measure of WSN and the same is plotted in Fig. 2(b). The higher throughput guarantees better system. Here, RSOM-WSN provides better throughput compare to the other techniques. 5. Conclusion The popular and proposed mechanisms of operation of WSN are investigated on measure such as Dead /Alive SNs, Packet Sent and throughput with number rounds. Rounds determines the life time of the WSN. The standard WSN operating process such as LEACH-WSN life time is comparatively lesser than the other process due to the absence of energy conservation mechanism. The more number of packets are sent to the BS in the RSOM-WSN due to the unique recurrence characteristics of a system. In each round the CH can be changed using temporal information with option appropriate weight assignment to the recurrent connection. The throughput of the WSN is reasonably better in RSOM-WSN due to the elimination of dead and standby SNs in the optimal route between CH and BS. It is evident from the experimentation among all energy conservation technique the RSOM-WSN outperforms the other techniques. Acknowledgements The work reported in this paper is supported by the college through the TECHNICAL EDUCATION QUALITY IMPROVEMENT PROGRAMME [TEQIP-III] of the MHRD, Government of India. References [1] Filippini, Massimo, and Lester C. Hunt. (2011) “Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach.” Energy Journal 32 (2): 59–80. [1] L. Yadav, and Ch. Sunitha,(2014), “Low energy adaptive clustering hierarchy in wireless sensor network (LEACH).” International Journal of Computer Science and Information Technologies 5 (3):4661-4664. [2] Asha G.R , and Gowrishankar, (2018), “A hybrid approach for cost effective routing for WSN using PSO and GSO algorithms.” , In proceedings of International conference on Big Data, IoT and Data Science (BID), 1-7. [3] Asha G.R and Gowrishankar, (2018), “An energy aware routing mechanism in WSNs using PSO and GSO Algorithm”, In proceedings of 5th International conference on Signal processing and integrated Network (SPIN 2018) : 1-7. [4] Asha G.R and Gowrishankar, (2018) , “An efficient clustering and routing algorithm for wireless sensor networks using GSO and KGMO techniques” , In proceedings of 6th International conference on Advanced Computing , Networking and Informatics(ICACNI 2018) : 1-10. [5] Asha G.R and Gowrishankar, (2018), “An efficient routing mechanism in WSN using PSO and GSO Algorithm”, In proceedings of 2nd International conference on innovative research in science, technology and management (ICIRSTM-18) :1-9 [6] Asha G.R and Gowrishankar, (2018), “RSOM Based clustering and routing in WSNs”, In proceedings of 2nd International conference on smart innovations in communication and computational sciences (ICSICCS-2018) : 1-10. [7] Singh S.P. and Sharma, S.C, (2014). “Cluster based routing algorithms for wireless sensor networks.” International Journal of Engineering & Technology Innovations (IJETI), 1(4): 1-8. [8] Amjad K. and Abu-Bakar (2016), "Energy efficient routing in cluster based wireless sensor networks: Optimization and analysis.” Jordan Journal of Electrical Engineering, 2(2) : 146-159. [9] F. Liu, Y. Wang, M. Lin, K. Liu, and Dapeng Wu, (2017), “A Distributed Routing Algorithm for Data Collection in Low-Duty-Cycle Wireless Sensor Networks”, IEEE Internet of Things Journal, 4(5):1420-1433.

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