Abstract
One of the biggest challenges in Wireless Sensor Networks (WSNs) is to efficiently utilise the limited energy available in the network. In most cases, the energy units of sensors cannot be replaced or replenished. Therefore, the need for energy efficient and robust algorithms for load balancing in WSNs is ever present. This need is even more pronounced in the case of cluster-based WSNs, where the Cluster Head (CH) gathers data from its member nodes and transmits this data to the base station or sink. In this paper, we propose a location independent algorithm to cluster the sensor nodes under gateways, as CHs into well defined, load balanced clusters. The location-less aspect also avoids the energy loss in running GPS modules. Simulations of the proposed algorithm are performed and compared with a few existing algorithms. The results show that the proposed algorithm shows better performance under different evaluation metrics such as average energy consumed by sensor nodes vs number of rounds, number of active sensors vs number of rounds, first gateway die and half of the gateways die.
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References
Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient mac protocol for wireless sensor networks. In Proceedings of the 1st international conference on Embedded networked sensor systems (pp. 171–180). ACM.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4), 393–422.
Wang, N., Zhang, N., & Wang, M. (2006). Wireless sensors in agriculture and food industry-recent development and future perspective. Computers and electronics in agriculture, 50(1), 1–14.
George, Z. et al. (2010). Node deployment and mobile sinks for wireless sensor networks lifetime improvement. Sustainable Wireless Sensor Networks.
Zhang, J., & Yang, T. (2013). Clustering model based on node local density load balancing of wireless sensor network. Fourth International Conference on Emerging Intelligent Data and Web Technologies, Xi’an, 2013, 273–276.
Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.
Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for energy efficient clusters in wireless sensor networks. Fourth international conference on information technology (ITNG’07) (pp. 147–154). Las Vegas, NV.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Kumar, N., & Kaur, J. (2011). Improved LEACH Protocol for Wireless Sensor Networks. 7th International conference on wireless communications, networking and mobile computing (Vol. 2011, pp. 1–5). Wuhan.
Gattani, V. S., & Jafri, S. M. H. (2016). Data collection using score based load balancing algorithm in wireless sensor networks. 2016 International conference on computing technologies and intelligent data engineering (ICCTIDE’16) (pp. 1–3). Kovilpatti.
Nabajyoti, M., & Om, H. (2016). An energy efficient GA-based algorithm for clustering in wireless sensor networks. International conference on emerging trends in engineering, technology and science (ICETETS). IEEE.
Yarinezhad, R., & Hashemi, S. N. (2019). A routing algorithm for wireless sensor networks based on clustering and an fpt-approximation algorithm. J. Syst. Softw., 155, 145–161.
Azharuddin, M., Kuila, P., & Jana, P. K. (2013). A distributed fault-tolerant clustering algorithm for wireless sensor networks. 2013 International conference on advances in computing, communications and informatics (ICACCI) (pp. 997–1002). Mysore.
Kuila, P. & Jana, P. (2015). Heap and parameter-based load balanced clustering algorithms for wireless sensor networks. In International journal of communication Nntworks and distributed systems (Vol. 14).
Thumthawatworn, T., Yeophantong, T., & Daengdej, J. (2005). Energy Conservation Approach for Precision-Insensitive Wireless Sensor Applications, IEEE Aerospace Conference (pp. 1–9). MT: Big Sky.
John, A., Rajput, A., & Babu, K. V. (2017). Energy saving cluster head selection in wireless sensor networks for internet of things applications. International conference on communication and signal processing (ICCSP) (pp. 34–38). Chennai.
Khadivi, A., Shiva, M., & Yazdani, N. (2005). EPMPAC: an efficient power management protocol with adaptive clustering for wireless sensor networks, Proceedings. International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan, China, 2005, 1154–1157.
Kuila, P, & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.
Singh, S. K., Kumar, P., & Singh, J. P. (2017). A Survey on Successors of LEACH Protocol. IEEE Access, 5, 4298–4328.
Lipare, A., Edla, D. R., Kuppili, V. (2019). Energy efficient load balancing approach for avoiding energy hole problem in WSN using Grey Wolf Optimizer with novel fitness function. Applied Soft Computing.
Jannu, S., & Jana, P. K. (2014). Energy efficient grid based clustering and routing algorithms for wireless sensor networks. Fourth international conference on communication systems and network technologies (pp. 63–68). Bhopal.
Venkataraman, Revathi, Moeller, Scott., Krishnamachari, Bhaskar., & Rao, T. Rama. (Feb 2015). “Trust-based backpressure routing in wireless sensor networks,” Int. J. Sen. Netw. vol. 17, no. 1, pp. 27–39.
Fakhrosadat, F., & Marjan, K. R. (2018). Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm. Applied Soft Computing, 71.
Fei, X., & Boukerche, A. (2008). A performance evaluation of a coverage compensation based algorithm for wireless sensor networks. In Proceedings of the 11th international symposium on Modeling, analysis and simulation of wireless and mobile systems (MSWiM’ 08) (pp. 109–116). New York, NY, USA.
Shuai, F., Jianfeng, M., Hongtao, L., & Changguang, W. (2013). Energy-balanced separating algorithm for cluster-based data aggregation in wireless sensor networks. International Journal of Distributed Sensor Networks.
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Madhu, S., Prasad, R.K., Ramotra, P. et al. A Location-less Energy Efficient Algorithm for Load Balanced Clustering in Wireless Sensor Networks. Wireless Pers Commun 122, 1967–1985 (2022). https://doi.org/10.1007/s11277-021-08976-1
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DOI: https://doi.org/10.1007/s11277-021-08976-1