Abstract
Energy minimization in sensor nodes is the problem in wireless sensor networks (WSNs). The most widely accepted method to preserve energy of sensor nodes is clustering. In cluster based networks, energy consumption is higher in the nodes which are closer to the sink as compared to the nodes which are located farther away from base station (BS). Various unequal clustering algorithms were proposed in the past to correct this issue. The major drawbacks in the findings are that the nodes, which join a specific cluster head, cause overburdening of the cluster head. In this paper, we present an algorithm namely energy efficient unequal sector clustering (EUSC) using multi-sector and unequal clustering approaches which improve the network’s energy efficiency. EUSC divides the network into multiple sectors based on nodes distances from BS. Selection of cluster heads in each sector is based on nodes distances to BS, residual energy and neighbor nodes proximities in that sector. Selection of a relay cluster head for data transmission is also based on residual energy, nodes distances to BS and queue length of nodes. In this paper, we have carried out some theoretical analysis of the nodes energy consumption in each sector and derived an expression for optimal number of clusters in each sector to minimize nodes energy consumption. Various simulations were carried out with MATLAB package to differentiate the competency of the proposed EUSC algorithm with that of the existing protocols ECHA and ‘PSO based protocol’. Simulation results in different network scenarios indicate that EUSC has given much improved performance than ECHA and ‘PSO based protocol’ in terms of number of clusters formed during each round, network lifetime and energy efficiency.
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Ehsan, S., Hamdaoui, B.: A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks. IEEE Commun. Surv. Tutor. 14(2), 265–278 (2012)
Aweya, J.: Technique for differential timing transfer over packet networks. IEEE Trans Ind. Inform. 9(1), 325–336 (2013)
Tang, J.D., Cai, M.: Energy-balancing routing algorithm based on LEACH protocol. Comput. Eng. 39(7), 133–136 (2013)
Heinzelman, W. R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Hawaii, 1–10 (2000)
Liu, X.: A survey on clustering routing protocols in wireless sensor networks sensors. J. Sens. 12(8), 11113–11153 (2012)
Gajjar, S., Sarkar, M., Dasgupta, K.: Performance analysis of clustering protocols for wireless sensor networks. Int. J. Electron. Commun. Eng. Technol. 4(6), 107–116 (2013)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Younis, O., Fahmy, S.: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mobile Comput. 3(4), 366–379 (2004)
Ye, M., Li, C., Chen, G., Wu, J.: EECS: An energy efficient clustering scheme in wireless sensor networks. In: Proceedings of the 24th IEEE International Performance, Computing and Communications Conference (IPCCC), 535–540 (2005)
Zhu, X., Shen, L., Yum, T.S.P.: Hausdorff clustering and minimum energy routing for wireless sensor networks. IEEE Trans. Veh. Technol. 58(2), 990–997 (2009)
Tarhani, M., Kavian, Y.S., Siavoshi, S.: SEECH: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens. J. 14(11), 3944–3954 (2014)
Quang, T.V., Huu, P.N., Miyoshi, T.: A transmission range optimization algorithm to avoid energy holes in wireless sensor networks. IEICE Trans. Commun. B 94(11), 3026–3036 (2011)
Halder, S., Ghosal, A., Bit, S.D.: A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network. Comput. Commun. 34(11), 1294–1306 (2011)
Bencan, G., Tingyao, J., Shouzhi, X., Peng, C.: An energy-heterogeneous clustering scheme to avoid energy holes in wireless sensor networks. Int. J. Distrib. Sens. Netw. 9(10), 1–8 (2013)
Wang, H., Wang, J.: An effective image representation method using kernel classification. In: Proceedings of the 26th International Conference on Tools with Artificial Intelligence (ICTAI), 853–858 (2014)
Bi, C., Wang, H., Bao, R.: SAR image change detection using regularized dictionary learning and fuzzy clustering. In: Proceedings of the 3rd International Conference on Cloud Computing and Intelligent Systems (CCIS), 327–330 (2014)
Zhang, S., Wang, H., Huang, W.: Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust. Comput. 20(2), 1517–1525 (2017)
Nguyen, T.T., Dao, T.K., Horng, M.F., Shieh, C.S.: An energy-based cluster head selection algorithm to support long-lifetime in wireless sensor networks. J. Netw. Intell. Taiwan Ubiquitous Inf. 1(1), 23–37 (2016)
Zhou, Y., Wang, N., Xiang, W.: Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access 5, 2241–2253 (2017)
Math Works Documentation Center, Available at: http://www.mathworks.in/help/matlab/. Accessed 18 October 2015
Pitchai, R., Jayashri, S., Raja, J.: Searchable encrypted data file sharing method using public cloud service for secure storage in cloud computing. J. Wirel. Pers. Commun. 90(2), 947–960 (2016)
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Sundaran, K., Ganapathy, V. & Sudhakara, P. Energy minimization in wireless sensor networks by incorporating unequal clusters in multi-sector environment. Cluster Comput 22 (Suppl 4), 9599–9613 (2019). https://doi.org/10.1007/s10586-017-1279-4
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DOI: https://doi.org/10.1007/s10586-017-1279-4