Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments. 1. Introduction Advancements in low-power electronic devices integrated with wireless communication capabilities are one of the recent areas of research in the field of the wireless sensor networks (WSNs). WSNs consist of spatially distributed autonomous sensors distributed over a region of interest to observe some phenomenon through either some random or strategic methods. Considerable amount of work has enabled the design, the implementation, and the deployment of these sensor networks tailored to the unique requirement of sensing and monitoring in real-time applications. These nodes have onboard wireless modules which consist of microcontroller, transreceiver, and power and memory units. A sensor mode is mounted on the node with multiple types of sensors depending on the type of application such as environmental monitoring [1], surveillance [2], military applications, automation in transportation, health [3], and industrial applications [4]. One of the stringent requirements of these nodes is the efficient use of the stored energy. Several algorithms have been designed for efficient management of nodes energy in WSNs using various clustering schemes [5, 6]. WSN divides clusters each having a coordinator (cluster head) responsible for gathering the data from the nodes and sending it to the sink (base station). Sensors are often deployed densely to satisfy the coverage requirement, which enables certain nodes to enter the sleep mode thereby allowing significant energy savings. The cluster heads can be selected randomly or based on one or more criteria. Selection of cluster head largely affects WSNs lifetime. Ideal cluster head is the one which has the highest residual
References
[1]
P. Wang, Z. Sun, M. C. Vuran, M. A. Al-Rodhaan, A. M. Al-Dhelaan, and I. F. Akyildiz, “On network connectivity of wireless sensor networks for sandstorm monitoring,” Computer Networks, vol. 55, no. 5, pp. 1150–1157, 2011.
[2]
C. Komar, M. Y. Donmez, and C. Ersoy, “Detection quality of border surveillance wireless sensor networks in the existence of trespassers' favorite paths,” Computer Communications, vol. 35, no. 10, pp. 1185–1199, 2012.
[3]
J. M. Corchado, J. Bajo, D. I. Tapia, and A. Abraham, “Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 2, pp. 234–240, 2010.
[4]
B. C. Villaverde, S. Rea, and D. Pesch, “InRout-a QoS aware route selection algorithm for industrial wireless sensor networks,” Ad Hoc Networks, vol. 10, no. 3, pp. 458–478, 2012.
[5]
J. Y. Yu and P. H. J. Chong, “A survey of clustering schemes for mobile ad hoc networks,” IEEE Communications Surveys & Tutorials, vol. 7, no. 1, pp. 32–48, 2005.
[6]
A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Computer Communications, vol. 30, no. 14-15, pp. 2826–2841, 2007.
[7]
S. H. Zanakis, A. Solomon, N. Wishart, and S. Dublish, “Multi-attribute decision making: a simulation comparison of select methods,” European Journal of Operational Research, vol. 107, no. 3, pp. 507–529, 1998.
[8]
C. Zopounidis and M. Doumpos, “Multicriteria classification and sorting methods: a literature review,” European Journal of Operational Research, vol. 138, no. 2, pp. 229–246, 2002.
[9]
A. Jahan, F. Mustapha, M. Y. Ismail, S. M. Sapuan, and M. Bahraminasab, “A comprehensive VIKOR method for material selection,” Materials and Design, vol. 32, no. 3, pp. 1215–1221, 2011.
[10]
A. Chauhan and R. Vaish, “Pareto optimal microwave dielectric materials,” Advanced Science, Engineering and Medicine, vol. 5, no. 2, pp. 149–155, 2013.
[11]
T. Yang and C. C. Hung, “Multiple-attribute decision making methods for plant layout design problem,” Robotics and Computer-Integrated Manufacturing, vol. 23, no. 1, pp. 126–137, 2007.
[12]
M. K. Rathod and H. V. Kanzaria, “A methodological concept for phase change material selection based on multiple criteria decision analysis with and without fuzzy environment,” Materials and Design, vol. 32, no. 6, pp. 3578–3585, 2011.
[13]
F. Torfi, R. Z. Farahani, and S. Rezapour, “Fuzzy AHP to determine the relative weights of evaluation criteria and fuzzy TOPSIS to rank the alternatives,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 520–528, 2010.
[14]
W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, vol. 1, no. 4, pp. 660–670, 2002.
[15]
D. Kumar, T. C. Aseri, and R. B. Patel, “EEHC: energy efficient heterogeneous clustered scheme for wireless sensor networks,” Computer Communications, vol. 32, no. 4, pp. 662–667, 2009.
[16]
C. H. Lung and C. Zhou, “Using hierarchical agglomerative clustering in wireless sensor networks: an energy-efficient and flexible approach,” Ad Hoc Networks, vol. 8, no. 3, pp. 328–344, 2010.
[17]
O. Younis and S. Fahmy, “HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,” IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp. 366–379, 2004.
[18]
T. Gao, R. C. Jin, J. Y. Song, T. B. Xu, and L. D. Wang, “Energy-efficient cluster head selection scheme based on multiple criteria decision making for wireless sensor networks,” Wireless Personal Communication, vol. 63, no. 4, pp. 871–894, 2012.
[19]
O. Karaca, R. Sokullu, N. R. Prasad, and R. Prasad, “Application oriented multi criteria optimization in WSNs using on AHP,” Wireless Personal Communication, vol. 65, no. 3, pp. 689–712, 2012.
[20]
E. M. Kasprzak and K. E. Lewis, “Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method,” Structural and Multidisciplinary Optimization, vol. 22, no. 3, pp. 208–218, 2001.
[21]
A. Chauhan and R. Vaish, “Hard coating material selection using multi-criteria decision making,” Materials and Design, vol. 44, pp. 240–245, 2013.
[22]
A. Chauhan and R. Vaish, “A comparative study on material selection for micro-electromechanical systems,” Materials and Design, vol. 41, pp. 177–181, 2012.
[23]
A. Chauhan and R. Vaish, “Magnetic material selection using multiple attribute decision making approach,” Materials and Design, vol. 36, pp. 1–5, 2012.
[24]
F. Comeau, W. Robertson, S. C. Sivakumar, and W. J. Phillips, “Energy conserving architectures and algorithms for wireless sensor networks,” in Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS '06), vol. 9, p. 236, January 2006.