One of the most tedious tasks is the achievement of energy efficiency in WSNs. The energy consumption limitation is the major problem and it‟s required to address based on reliable and effective solutions. Clustering is the best technique for reduction of energy consumption in WSNs. The CHs have been selected after formation of clusters in a network based on clustering methods. But, theyhave some limitations like increased network overhead, and higher execution time for larger scale networks. As improper node selection is resulted in the unnecessary energy usage among sensor nodes, it‟s essential to perform the optimal relay node selection for energy saving. Both clustering and optimal relay node selection procedures have been optimized withtheuse ofa combinedalgorithm.The ModifiedGravitationalgorithmfor optimalnodeselection, i.e. MGA-ONS, which is a new energy-efficient management technique has been introduced for CHs selection according to different parameters, such as probability value, residual energy, and theclosersensornodestotheSINK.Theunnecessaryenergyutilizationislimitedbyconsidering the sensor node distance to SINK. For selection of relaynodes, the proposed MGA is considered significant parameters, such as delay and residual energy in the links, and nodes‟ distance from their respective CHs. The improved energy consumption is achieved using the proposed algorithm in comparison with the existing methods, such as LEACH.

The modified gravitation search technique proposes using the multi-objective CH selection inthis paper to provide the selection of energy-aware CHs and optimal relay nodes. However, the main advantageofusingMGAis that it chooses theoptimal routewith theuseofmulti-objective parameters, such as energy, node distance to CHs, and delay. The optimized results are achieved with the CH selection based on parameters, such as probability, residual energy, and distance to SINK. The proposed multi-objective MGA-ONS technique is achieved the greater energy energy-efficiency results with lower overhead based on the evaluation of simulation results. Additionally, the improved throughput and data delivery rate have been reached for inter-cluster data aggregation because of the optimal selection of CHs with the distance between nodes and SINK. The higher efficiency results are proved by the MGA based on multi-objectiveparameters. The proposed technique MGA-ONS outperforms all other existing routing methods, such as MACHFL-FT and EN-LEACH based on energy consumption, PDR, throughput, and end-to-end delay.

[1]Fahmy, Hossam Mahmoud Ahmad. “Wsn applications.” In Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks, pp. 67-232. Springer, Cham, 2021. [2]Aldoseri, Hamad Fadhel, Lars Haering, and Andreas Czylwik. “A Reliable Industrial Wireless Sensor and Actor Network Based on CDMA-OQAM-OFDM.” In2020 IEEE 31st AnnualInternationalSymposiumonPersonal,IndoorandMobileRadioCommunications,pp.1- 5.IEEE, 2020. [3]Poongodi, T., Anu Rathee, R. Indrakumari, and P. Suresh. “IoT sensing capabilities: sensor deployment and node discovery, wearable sensors, wireless body area network (WBAN), data acquisition.”InPrinciplesofInternetofThings(IoT)Ecosystem:InsightParadigm,pp.127-151. Springer, Cham, 2020. [4]Elhoseny, Mohamed, and Aboul Ella Hassanien. “Extending homogeneous WSN lifetime in dynamic environments using the clustering model.” In Dynamic Wireless Sensor Networks, pp. 73-92. Springer, Cham, 2019. [5]Haseeb, Khalid, Naveed Islam, Ahmad Almogren, and Ikram Ud Din. “Intrusion prevention framework for secure routing in WSN-based mobile Internet of Things.”Ieee Access7 (2019): 185496-185505. [6]Jain, Khushboo,Arun Agarwal, and Anoop Kumar. “Anovel dataprediction techniquebased on correlation fordata reduction in sensor networks.” In Proceedings of international conference on artificial intelligence and applications, pp. 595-606. Springer, Singapore, 2021. [7]P. Neamatollahi, M. Naghibzadeh, and S. Abrishami, “Fuzzybased clustering-task scheduling for lifetime enhancement in wireless sensor networks,” IEEE Sensors Journal, vol. 17, no. 20, pp. 6837– 6844, 2017. [8]Q. Ni, Q. Pan, H. Du, C. Cao, and Y. Zhai, “A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 1, pp. 76–84, January 2017. [9]S. Arjunan and P. Sujatha, “Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol,” Applied Intelligence, vol. 48, no. 8, pp. 2229–2246, 2017. [10]N. A. Pantazis, S. A. Nikolidakis, and D. D. Vergados, “Energy-efficient routing protocols in wireless sensor networks: a survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 2, pp. 551– 591, 2013. [11]G. Jakllari, S. Eidenbenz, N. Hengartner, S. V. Krishnamurthy, and M. Faloutsos, “Link positions matter: a noncommutative routing metric for wireless mesh networks,” IEEE Transactions on Mobile Computing, vol. 11, no. 1, pp. 61–72, 2012. [12]R. R. Rout, S. K. Ghosh, and S. Chakrabarti, “Co-operative routing for wireless sensor networks using network coding,” IET Wireless Sensor Systems, vol. 2, no. 2, pp. 75–85, June 2012. [13]D. Zhang, G. Li, K. Zheng, X. Ming, and Z.-H. Pan, “An energy-balanced routing method based on forward-aware factorfor wireless sensor networks,” IEEE Transactions on IndustrialInformatics,vol. 10, no. 1, pp. 766–773, 2014. [14]J. So and H. Byun, “Load-balanced opportunistic routing for duty-cycled wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 16, no. 7, pp. 1940–1955, 2017. [15]Y.Sun,W.Dong,andY.Chen,“Animprovedroutingalgorithmbasedonant colonyoptimizationin wireless sensor networks,” IEEE Communications Letters, vol. 21, no. 6, pp. 1317–1320, 2017. [16]Sindhwani N, Vaid R (2013) V Leach: an energy-efficient communication protocol for WSN. Mechanica Confab 2.2, Vol. 2, No.2, ISSN: 2320–2491. [17]Liao Q, Zhu H (2013) an energy-balanced clustering algorithm based on LEACH protocol. Proceedings of the 2nd International Conference on Systems Engineering and Modeling (ICSEM-13) 5(3):90–95. [18]Bakaraniya P, Mehta S (2013) K-LEACH: an improved LEACH protocol for lifetime improvement inWSN. International Journal of Engineering Trends and Technology (IJETT) 4(5):2231–5381. [19]Mirzaie, M. and Mazinani, S.M., 2019. MACHFL-FT: a fuzzy logic based energy-efficient protocol to cluster heterogeneous nodes in wireless sensor networks. Wireless Networks, 25(8), pp.4597-4609. [20]Salem, A.O.A. and Shudifat, N., 2019. Enhanced LEACH protocol for increasing a lifetime of WSNs. Personal and Ubiquitous Computing, 23(5), pp.901-907.

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