Abstract
Wireless sensor networks (WSN) react to events in specified circumstances by sensing, computing and communicating with thousands of sensors arranged at different locations operating in different modes. Typical applications include, but are not limited to, data collection, military operations, surveillance, and medical telemetry. The sensors are battery powered devices and hence their lifetime is very limited. It may not be possible to recharge or replace the battery depending upon the application environment. Communication overhead has to be reduced because energy is a very valuable resource for these sensor nodes. Long distance communication among sensors will cause large amount of energy drain which may reduce the lifetime of the network. In this work we propose Genetic Algorithm (GA) and Gravitational Search based methods to address sensor network optimization problem. The GA, GSA and PSO based clustering of WSN can greatly minimize the total communication distance, thus lengthening the network lifespan. Kerala, on the west coast of India, holds a vital role in India’s fishing industry that gives a sustainable steady income. A new technique based on WSN technology using GA, GSA and PSO methods has been presented in this paper which helps to provide protection to the fishermen while they are in the deep sea. The results obtained from the proposed work shows that the network optimization based on cluster head using GSA has better performance and less energy consumption than the network without clustering.
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References
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless micro-sensor networks. In: Proceedings of the Hawaii International Conference on System Science, Maui, Hawaii (2000)
Cheng, X., Xu, J., Pei, J., Liu, J.: Hierarchical distributed data classification in wireless sensor networks. In: Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) (2009)
Lai, C.-C., Ting, C.-K., Ko, R.-S.: An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In: IEEE Congress on Evolutionary Computation (CEC 2007) (2007)
Hussain, S., Matin, A.W., Islam, O.: Genetic algorithm for energy efficient clusters in wireless sensor networks. J. Netw. 2(5) (2007)
Mansouri, M., Nounou, H., Nounou, M.: Genetic algorithm-based adaptive optimization for target tracking inwireless sensor networks. J. Sig. Process. Syst. 74, 189–202 (2014)
Chagas, S.H., Martins, J.B., de Oliveira, L.L.: Genetic algorithms and simulated annealing optimization methods in wireless sensor networks localization using artificial neural networks, pp. 928–931. IEEE (2012)
Jourdan, D.B., de Weck, O.L.: Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. Published in: 2004 IEEE 59th Vehicular Technology Conference, VTC 2004-Spring (2004)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multi objective optimization: formulation, discussion and generalization. In: Genetic Algorithms: Proceedings of Fifth International Conference, pp. 416–423. Morgan Kaufmann (1993)
Sara, G.S., Devi, S.P., Sridharan, D.: A genetic-algorithm-based optimized clustering for energy-efficient routing in MWSN. ETRI J. 34(6), 922–931 (2012)
Misra, S., et al. (eds.): Guide to Wireless Sensor Networks, Computer Communications and Networks. Springer, London (2009). https://doi.org/10.1007/978-1-84882-218-4
Singh, S.K., Singh, M.P., Singh, D.K.: Routing protocols in wireless sensor networks – a survey. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 1(2) (2010). https://doi.org/10.5121/ijcses.2010.1206
Singh, P., Bhatia, M., Kaur, R.: Energy-efficient cluster based routing techniques: a review. Int. J. Eng. Trends Technol. 4(3) (2013). ISSN 2231-5381
Delavar, A.G., Shamsi, S., Mirkazemi, N., Artin, J.: SLGC: a new cluster routing algorithm in wireless sensor network for decrease energy consumption. Int. J. Comput. Sci. Eng. Appl. (IJCSEA) 2(3) (2012). https://doi.org/10.5121/ijcsea.2012.2304
Goyal, R.: A review on energy efficient clustering routing protocol in wireless sensor network. IJRET: Int. J. Res. Eng. Technol. 03(06) (2014). eISSN: 2319-1163, pISSN: 2321-7308
Kumar, S., Prateek, M., Ahuja, N.J., Bhushan, B.: MEECDA: multihop energy efficient clustering and data aggregation protocol for HWSN. Int. J. Comput. Appl. (0975 – 8887) 88(9) (2014)
Kaur, A., Buttar, A.S.: Energy efficient clustering techniques using genetic algorithm in wireless sensor network: a survey. IJIRST –Int. J. Innov. Res. Sci. Technol. 2(09) (2016). ISSN (online): 2349-6010
Hussain, S., Matin, A.W., Islam, O.: Genetic algorithm for hierarchical wireless sensor networks. J. Netw. 2(5) 87–97 (2007)
Singh, V.K., Sharma, V.: Elitist genetic algorithm based energy efficient routing scheme for wireless sensor networks. Int. J. Adv. Smart Sensor Netw. Syst. (IJASSN) 2(2) (2012). https://doi.org/10.5121/ijassn.2012.2202
Zahmatkesh, A., Yaghmaee, M.H.: A genetic algorithm-based approach for energy- efficient clustering of wireless sensor networks. Int. J. Inf. Electron. Eng. 2(2), 165 (2012)
Peiravi, A., Mashhadi, H.R., Hamed Javadi, S.: An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. Int. J. Commun. Syst. 26, 114–126 (2013). Published online 24 August 2011 in Wiley Online Library (wileyonlinelibrary.com). https://doi.org/10.1002/dac.1336
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. dissertation, Swiss Federal Institute of Technology Zurich, November 1999
Khalil, E.A., Attea, B.A.: Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evol. Comput. 1, 195–203 (2011). https://doi.org/10.1016/j.swevo.2011.06.004
Chagas, S.H., Martins, J.B., de Oliveira, L.L.: An approach to localization scheme of wireless sensor networks based on artificial neural networks and genetic algorithms. IEEE (2012). 978-1-4673-0859-5
He, S., Dai, Y., Zhou, R., Zhao, S.: A clustering routing protocol for energy balance of WSN based on genetic clustering algorithm. IERI Procedia 2 788–793 (2012). Elsevier
Peng, B., Li, L.: An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cogn. Neurodyn. 9, 249–256 (2015). https://doi.org/10.1007/s11571-014-9324-y
Nicolescu, D., Nath, B.: DV based positioning in ad hoc networks. J. Telecommun. (2003)
Rostami, A.S., Bernetty, H.M., Hosseinabadi, A.R.: A novel and optimized algorithm to select monitoring sensors by GSA. In: 2nd International Conference on Control, Automation and Instrumentation. IEEE (2011). 978-1-4673-1690-3
Rafsanjani, M.K., Dowlatshahi, M.B.: Using gravitational search algorithm for finding near-optimal base station location in two-tiered WSNs. Int. J. Mach. Learn. Comput. 2(4), 377 (2012)
Huynh, T.T., Dinh-Duc, A.-V., Tran, C.-H., Le, T.-A.: Balance particle swarm optimization and gravitational search algorithm for energy efficient in heterogeneous wireless sensor networks. In: 2015 IEEE International Conference on Computing and Communication Technologies Research, Innovation, and Vision for Future (RIVF (2015). 978-1-4799-8044-4/15
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Sobhana, N.V., Rahul Raj, M., Gayatri Menon, B., Sherly, E. (2019). Optimized Path Selection in Oceanographic Environment. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_50
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