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Wireless Personal Communications

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03-08-2016

A Hybrid Swarm Optimization for Energy Efficient Clustering in Multi-hop Wireless Sensor Network

Authors: D. Rajendra Prasad, P. V. Naganjaneyulu, K. Satya Prasad

Published in: Wireless Personal Communications | Issue 4/2017

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Abstract

Wireless sensor network refers to distributed sets of embedded devices, all of them having processing units, wireless transmission interface and sensors or actuators. Data accumulation through effective network organizations helps nodes to be split into small sets known as clusters. This grouping of sensor nodes as clusters is known as clustering. All clusters have leaders known as cluster heads (CHs). Clustering networks for minimizing total distance is an NP-hard issue. For a particular network topology, it is hard to discover optimum quantity of cluster-heads as well as their positions. The current article suggests a hybrid differential evolution with multi objective bee swam optimization (MOBSO-DE) for efficient clustering. CH selection process is based on communication energy and factors like residual energy and energy constraint metric. Simulation shows that the new MOBSO-DE method outperformed LEACH and MOBSO for packet delivery ratio and network lifetime.

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Kole, S., Vhatkar, M. K., & Bag, M. V. (2014). Distance based cluster formation technique for LEACH protocol in wireless sensor network. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(3), 334–338.

Mamalis, B., Gavalas, D., Konstantopoulos, C., & Pantziou, G. (2009). Clustering in wireless sensor networks. In Y. Zhang, L. T. Yang, & J. Chen, (Eds.), RFID and sensor networks: Architectures, protocols, security and integrations (pp. 324–353). CRC Press: USA.

Zahmatkesh, A. & Yaghmaee, M. H. (2012). A genetic algorithm-based approach for energy-efficient clustering of wireless sensor networks. International Journal of Information and Electronics Engineering, 2(2), 104–108.

Rijin, I. K., Sakthivel, N. K., & Subasree, S. (2013). Development of an enhanced efficient secured multi-hop routing technique for wireless sensor networks. Development, 1(3), 2320–9801.

Schurgers, C., & Srivastava, M. B. (2001). Energy efficient routing in wireless sensor networks. In Military communications conference, 2001. MILCOM 2001. Communications for network-centric operations: creating the information force. IEEE (Vol. 1, pp. 357–361). IEEE.

Sahoo, R. R., Singh, M., Sardar, A. R., Mohapatra, S., & Sarkar, S. K. (2013, March). TREE-CR: Trust based secure and energy efficient clustering in WSN. In Emerging trends in computing, communication and nanotechnology (ICE-CCN), 2013 international conference on (pp. 532–538). IEEE.

Sathian, D., Baskaran, R., &Dhavachelvan, P. (2012, July). Lifetime enhancement by cluster head cooperative trustworthy energy efficient MIMO routing algorithm based on game theory for WSN. In Computing communication & networking technologies (ICCCNT), 2012 third international conference on (pp. 1–5). IEEE.

Enam, R. N., Misbahuddin, S., & Imam, M. (2012, May). Energy efficient round rotation method for a random cluster based WSN. In Collaboration technologies and systems (CTS), 2012 international conference on (pp. 157–163). IEEE.

Li, X., Gang, W., Zongqi, L., &Yanyan, Z. (2013, May). An energy-efficient routing protocol based on particle swarm clustering algorithm and inter-cluster routing algorithm for WSN. In Control and decision conference (CCDC), 2013 25th Chinese (pp. 4029–4033). IEEE.

10.

Elhabyan, R. S., &Yagoub, M. C. (2014, September). Energy efficient clustering protocol for WSN using PSO. In Global information infrastructure and networking symposium (GIIS), 2014 (pp. 1–3). IEEE.

11.

Maleki, I., Khaze, S. R., Tabrizi, M. M., & Bagherinia, A. (2013). A new approach for area coverage problem in wireless sensor networks with hybrid particle swarm optimization and differential evolution algorithms. International Journal of Mobile Network Communications and Telematics (IJMNCT), 3(6), 61–76.CrossRef

12.

Wang, L., Ye, W., Mao, Y., Georgiev, P. G., Wang, H., & Fei, M. (2013). The node placement of large-scale industrial wireless sensor networks based on binary differential evolution harmony search algorithm. International Journal of Innovative Computing Information and Control, 9(3), 955–970.

13.

Du, T., Qu, S., Liu, F., & Wang, Q. (2015). An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Information Fusion, 21, 18–29.CrossRef

14.

Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.MathSciNetCrossRefMATH

15.

Azharuddin, M., Kuila, P., & Jana, P. K. (2015). Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Computers & Electrical Engineering, 41, 177–190.

16.

Abraham, A., Jatoth, R. K., & Rajasekhar, A. (2012). Hybrid differential artificial bee colony algorithm. Journal of Computational and Theoretical Nanoscience, 9(2), 249–257.CrossRef

Title: A Hybrid Swarm Optimization for Energy Efficient Clustering in Multi-hop Wireless Sensor Network
Authors: D. Rajendra Prasad
P. V. Naganjaneyulu
K. Satya Prasad
Publication date: 03-08-2016
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-016-3562-8

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