Abstract
Wireless Sensor Networks are usually the application specific networks that consists of nodes which gathers some data from its surrounding environment continuously or intermittently based on user specifications. In practical use case scenarios, WSNs will consist of densely populated sensor nodes where each node will have a greater number of neighboring nodes. Establishing direct communication with more neighboring nodes results high energy consumption because large transmission power is required. To overcome the most critical issue of poor network lifetime, efficient use of energy resources is required. The goal of the proposed study is to create an efficient cluster head selection model based on Ant Colony Optimization technique that helps to save energy and extend network lifetime by selecting cluster heads wisely.
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References
Yetgin, H., et al.: A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun. Surv. Tutor. 19(2), 828–854 (2017)
Xu, L., Collier, R., O’Hare, G.M.P.: A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios. IEEE Internet Things J. 4(5), 1229–1249 (2017). https://doi.org/10.1109/JIOT.2017.2726014
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, vol. 2, p. 10 (2000). https://doi.org/10.1109/HICSS.2000.926982
Verma, A., Kumar, S., Gautam, P.R., Rashid, T., Kumar, A.: Fuzzy logic based effective clustering of homogeneous wireless sensor networks for mobile sink. IEEE Sens. J. 20(10), 5615–5623 (2020). https://doi.org/10.1109/JSEN.2020.2969697
Behera, T.M., Nanda, S., Mohapatra, S.K., Samal, U.C., Khan, M.S., Gandomi, A.H.: CH selection via adaptive threshold design aligned on network energy. IEEE Sens. J. 21(6), 8491–8500 (2021). https://doi.org/10.1109/JSEN.2021.3051451
Ali, H., Tariq, U.U., Hussain, M., Lu, L., Panneerselvam, J., Zhai, X.: ARSH-FATI: a novel metaheuristic for cluster head selection in wireless sensor networks. IEEE Syst. J. 15(2), 2386–2397 (2021). https://doi.org/10.1109/JSYST.2020.2986811
El Alami, H., Najid, A.: ECH: an enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access 7, 107142–107153 (2019). https://doi.org/10.1109/ACCESS.2019.2933052
Gupta, P., Sharma, A.K.: Clustering-based optimized HEED protocols for WSNs using bacterial foraging optimization and fuzzy logic system. Soft. Comput. 23(2), 507–526 (2017). https://doi.org/10.1007/s00500-017-2837-7
Hassan, A.A.H., Shah, W.M., Habeb, A.-H.H., Othman, M.F.I., Al-Mhiqani, M.N.: An improved energy-efficient clustering protocol to prolong the lifetime of the WSN-based IoT. IEEE Access 8, 200500–200517 (2020). https://doi.org/10.1109/ACCESS.2020.3035624
Elshrkawey, M., Elsherif, S.M., Wahed, M.E., An enhancement approach for reducing the energy consumption in wireless sensor networks. J. King Saud Univ.-Comput. Inf. Sci. 30(2), 259 (2018). ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2017.04.002
Srinivasa Rao, P.C., Banka, H.: Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wirel. Netw. 23(2), 433–452 (2015). https://doi.org/10.1007/s11276-015-1156-0
Qin, W., Chen, S., Peng, M.: Recent advances in industrial internet: insights and challenges. Digit. Commun. Netw. 6(1), 1–13 (2020)
Joseph, J., et al.: A survey on wireless networks: classifications, applications and research challenges. Perspect. Commun. Embed. Syst. Sig. Process. PiCES2(9), 200–209 (2018)
Dorigo, M., Stützle, T., The ant colony optimization metaheuristic. In: Ant Colony Optimization. MIT Press, Cambridge, pp. 25–64 (2004)
Venkateswarao, T., Sreevidya, B.: An energy-efficient wireless sensor deployment for lifetime maximization by optimizing through improved particle swarm optimization. In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds.) Information and Communication Technology for Competitive Strategies (ICTCS 2020). LNNS, vol. 190, pp. 49–63. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0882-7_3
Bhavani, K.D., Radhika, N.: K-means clustering using nature-inspired optimization algorithms - a comparative survey. Int. J. Adv. Sci. Technol. 29(Special Issue 6), 2466–2472 (2020)
Vidhya, S.S., Mathi, S.: Investigations on Power-aware solutions in low power sensor networks. In: Ranganathan, G., Fernando, X., Shi, F. (eds) Inventive Communication and Computational Technologies. LNNS, vol. 311, pp. 911–925. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-5529-6_69
Gokuldev, S., Jathin, R.: Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters. Int. J. Eng. Technol. 7(2), 837–841 (2018)
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Krishna, M.P.N., Abirami, K. (2022). A Meta-heuristic Based Clustering Mechanism for Wireless Sensor Networks. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_27
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