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Jinchao Chen
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
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1 School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China
Received 2022-10-09; Accepted 2022-10-17; Issue published 2022-10-17
In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introduced to obtain the optimal deployment algorithm of wireless sensor network nodes. The experimental results show that the algorithm can reduce the number of active nodes efficiently, improve coverage uniformity, reduce network energy consumption and prolong network lifetime under the premise that the coverage rate is greater than 90%, and compared with an algorithm called coverage configuration protocol, an algorithm called finding the minimum working sets in wireless sensor networks, and an algorithm called binary particle swarm optimization-g in literature, the number of active nodes in this algorithm is reduced by about 36%, 30% and 23% respectively.
Uniform coverage
discrete binary particle swarm optimization algorithm
wireless sensor network
optimal deployment
Cite This Article
L. Yujiang, C. Jinghua, and C. Jinchao, “Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm”, Vol. 1, No.1, vol. 1, no. 1, pp. 1–11, 2022, doi: 10.00000/TIOT.2023.100001.

[1]Zhu, C., Zheng, C., Lei, S., Han, G. (2012). A survey on coverage and con-nectivity issues in wireless sensor networks. Journal of Network and ComputerApplications, 35(2), 619-632.;

[2]Renold, A. P., Chandrakala, S. (2016). Survey on state scheduling-based topol-ogy control in unattended wireless sensor networks. Computers and ElectricalEngineering, 56, 334-349.;

[3]Bo, Z., Tong, E., Jie, H., Niu, W., Gang, L. (2016). Energy efficient sleep schedulewith service coverage guarantee in wireless sensor networks. Journal of Network and Systems Management, 24(4), 834-858.;

[4]Singh, B., Lobiyal, D. K. (2013). Traffic-aware density-based sleep schedulingand energy modeling for two dimensional gaussian distributed wireless sensornetwork. Wireless Personal Communications, 70(4), 1373-1396.;

[5]Xu, Y., Peng, Y., zheng, Ch., Liao,Y. (2020). Node neergy balanced coveragestrategy in WSNs based on improved PSO algorithm. Transducer and MicrosystemTechnologies, 39(02), 29-32.;

[6]Wang, A., Liu, Y., Zhang, J., Liu, Y. (2016). Coverage algorithm for finding theminimum working sets in WSNs. Journal of Xidian University, 43(04), 141-146.;

[7]Liu, X., Zhang, X., Hu, T., Zhu, Q. (2018). Deployment optimization of wire-less sensor network based on parallelized cuckoo search algorithm. ApplicationResearch of Computers, 35(7), 2063-2065.;

[8]Yu, W., Li, X., Yang, H., Huang, B. (2017). Extrapolation artificial bee colonyalgorithm research on deployment optimization in wireless sensor network. In-strument Technique and Sensor, 6,158-160.;

[9]Zhou, L., Yang, K., Zhou, P. (2010). Optimal coverage configuration based onartificial fish swarm algorithm in WSNs. Application Research of Computers, 6,2276-2279.;

[10]Qin, N., Chen, J., Ding, Z. (2015). Balanced rate area coverage algorithm. ChineseJournal of Sensors and Actuators, 28(4),578-584.;

[11]Elbes, M., Alzubi, S., Kanan, T., Al-Fuqaha, A., Hawashin, B. (2019). A sur-vey on particle swarm optimization with emphasis on engineering and networkapplications. Evolutionary Intelligence, 12(2), 113-129.;

[12]Ahmed, K., Al-Khateeb, B., Mahmood, M. (2019). Application of chaos discreteparticle swarm optimization algorithm on pavement maintenance schedulingproblem. Cluster Computing, 22 (2), 4647-4657.;

[13]Liu, J., Yang, R. Sun, S. (2011).The analysis of binary particle swarm optimization.Journal of Nanjing University (Natural Science),47(5), 504-514.;

[14]Wang, Y., Qiu, F., Guo, H. (2019). Adaptive inertia weight binary particle swarmoptimization algorithm with mutation operator. Journal of Chinese ComputerSystems, 40(04), 733-737.;

[15]Wu, X., Zhang, C., Zhang, R., Sun, Y. (2019). Clustering routing protocol based onimproved PSO algorithm in WSN. Journal on Communications, 40(12), 114-123.;

[16]Li, Y., Pan, B. (2018). Research of WSN regional coverage based on adaptivemutation binary particle swarm optimization. Journal of Sichuan University ofScience & Engineering (Natural Science Edition), 31(01), 20-24.;

[17]Chen, X., Cheng, L., Liu, C., Liu, Q., Liu, J., Mao, Y., Murphy, J. (2020). AWOA-based optimization approach for task scheduling in cloud computing sys-tems. IEEE Systems journal, 14(3), 3117-3128.;

[18]Liu, Q., Cheng, L., Alves, R., Ozcelebi, T., Kuipers, F., Xu, G., ... Chen, S. (2021).Cluster-based flow control in hybrid software-defined wireless sensor networks.Computer Networks, 187, 107788.;

[19]Liu, Q., Cheng, L., Alves, R., Ozcelebi, T., Kuipers, F., Xu, G., ... Chen, S.(2021). Cluster-based flow control in hybrid software-defined wireless sensornetworks. Computer Networks, 187, 107788.;

[20]Cheng, L., Wang, Y., Pei, Y., Epema, D. (2017, August). A coflow-based co-optimization framework for high-performance data analytics. In 2017 46th Inter-national Conference on Parallel Processing (ICPP) (pp. 392-401).;