An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks
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Abstract
The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others.
Received 27/09/2023
Revised 10/02/2024
Accepted 12/02/2024
Published 25/02/2024
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Elhabyan R, Shi W, St-Hilaire M. Coverage protocols for wireless sensor networks: Review and future directions. J.Commn.Net. 2019 Feb;21(1):45-60.https://doi.org/10.1169/JCN.2019.000005
Saeedi ID, Al-Qurabat AK. Perceptually important points-based data aggregation method for wireless sensor networks. Baghdad Sci. J. 2022 Aug 1;19(4):0875.https://doi.org/10.21123/bsj.2022.19.4.0875
Maheshwari A, Chand N. A survey on wireless sensor networks coverage problems. InProceedings of 2nd International Conference on Communication, Computing and Networking: ICCCN 2018, NITTTR Chandigarh, India .2019; (pp. 153-164). Springer Singapore. https://doi.org/10.1007/978-981-13-1217-5_16
Yasear SA, Ku-Mahamud KR. Taxonomy of memory usage in swarm intelligence-based metaheuristics. Baghdad Sci. J. 2019;16(2):0445.http://dx.doi.org/10.21123/bsj.2019.16.2(SI).0445
Singh A, Sharma S, Singh J. Nature-inspired algorithms for wireless sensor networks: A comprehensive survey. Comput. Sci. Rev. . 2021 Feb 1;39:100342.https://doi.org/10.1016/j.cosrev.2020.100342
Duan J, Yao AN, Wang ZT, Yu L. An Improved Sparrow Search Algorithm Optimizes Coverage in Wireless Sensor Networks. Eng. Technol. Ed. . [cited 2023 Feb 21];1–11. http://kns.cnki.net/kcms/detail/22.1341.T.20220726.1034.003.html.
Mottaki NA, Motameni H, Mohamadi H. An effective hybrid genetic algorithm and tabu search for maximizing network lifetime using coverage sets scheduling in wireless sensor networks. J. Supercomput. 2023 Feb;79(3):3277-97.https://doi.org/10.1007/s11227-022-04710-1
He Q, Lan Z, Zhang D, Yang L, Luo S. Improved marine predator algorithm for wireless sensor network coverage optimization problem. Sustainability. 2022 Aug 11;14(16):9944.https://doi.org/10.3390/su14169944
Musa JA, Romli R, Yusoff N. An analysis on the applicability of meta-heuristic searching techniques for automated test data generation in automatic programming assessment. Open Access Baghdad Sci J. . 2019;16(SI):515-33.https://doi.org/10.21123/bsj.2019.16.2(SI).0515
Yang XS, Deb S. Engineering optimisation by cuckoo search. IJMMNO. 2010 Jan 1;1(4):330-43.https://doi.org/10.1504/IJMMNO.2010.03543
Mohamad AB, Zain AM, Nazira Bazin NE. Cuckoo search algorithm for optimization problems—a literature review and its applications. Appl. Artif. Intell. . 2014 May 28;28(5):419-48.https://doi.org/10.1080/08839514.2014.904599
Ye SQ, Wang FL, Ou Y, Zhang CX, Zhou KQ. An improved cuckoo search combing artificial bee colony operator with opposition-based learning. In2021 China Automation Congress (CAC).2021 Oct 22; pp. 1199-1204. IEEE.https://doi.org/10.1109/CAC53003.2021.9727912
Zhang CX, Zhou KQ, Ye SQ, Zain AM. An improved cuckoo search algorithm utilizing nonlinear inertia weight and differential evolution for function optimization problem. IEEE Access. 2021 Nov 25;9:161352-73.https://doi.org/10.1109/ACCESS.2021.3130640
Li PC, Zhang XY, Zain AM, Zhou KQ. An Improved Cuckoo Search Algorithm Using Elite Opposition-Based Learning and Golden Sine Operator. InInternational Conference on Adaptive and Intelligent Systems. Cham: Springer International Publishing. 2022 Jul 4 ;(pp. 276-288).https://doi.org/10.1007/978-3-031-06794-5_23
Yang XS, Deb S. Cuckoo search via Lévy flights.NaBIC.2009 Dec 9; pp. 210-214. Ieee.https://doi.org/10.1109/NABIC.2009.5393690
Iqbal Z, Ilyas R, Chan HY, Ahmed N. Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach. Baghdad Sci. J. . 2021 Dec 20;18(4 (Suppl.)):1465-.https://doi.org/10.21123/bsj.2021.18.4(Suppl.).1465
Tizhoosh HR. Opposition-based learning: a new scheme for machine intelligence. InInternational conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC'06). 2005 Nov 28; Vol. 1: pp. 695-701). IEEE.https://doi.org/10.1109/CIMCA.2005.1631345
Kamaruzaman AF, Zain AM, Yusuf SM, Udin A. Levy flight algorithm for optimization problems-a literature review. Appl. Mech. Mater. . 2013 Dec 12;421:496-501.https://doi.org/10.4028/www.scientific.net/AMM.421.496
Zhang ZZ, He XS, Yu QL, Yang XS. Cuckoo Algorithm for Multi-stage Dynamic Disturbance and Dynamic Inertia Weight. Comput. Appl. Eng. Educ. . 2021 Apr 29;58(01):79–88.https://doi.org/10.3778/j.issn.1002-8331.2012-0281
Sun M, Wei H. An improved adaptive inertial weight Cuckoo algorithm. Journal of Yangtze University(Natural Science Edition). 2019 Jul 17;16(07):81-87.https://doi.org/10.16772/j.cnki.1673-1409.2019.07.016
Zheng HQ, Feng WJ. An improved Cuckoo Search algorithm for Constrained Optimization Problems. Chinese Journal of Engineering Mathematics. 2023 Feb 15;40(01);135-146.https://doi.org/10.3969/j.issn.1005-3085.2023.01.010