Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm

Yujiang Li; Jinghua Cao

doi:10.00000/TIOT.2023.100001

Submit Manuscript Edit a Special Issue

Academic Editor

Jinchao Chen

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

Article Metrics

Views

2285

PDF Downloads

Popular articles

Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm The Public Health Events on the Audit Behavior and the Audit Quality: An Empirical Study A Biomimetic Covering Learning Method Based on Principle of Homology Continuity The Optimization of Cold Chain Logistics Delivery Routes for Company B Based on Service Priority Visitors' Preferences at Chinese Historical Sites An Improved YOLOv3-Based Method for Immature Apple Detection Design and implementation of private college enrollment Management System based on B/S mode Design and Implementation of 3D Library Application System-Taking The Library of Henan University of Technology as an Example YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Consumer Satisfaction Based on Online Reviews of Fresh Food E-commerce Platforms: Take YuanShengXian Store As an Example

2023, Volume 1, Issue 1: 1-8. DOI: 10.00000/TIOT.2023.100001

Research Article | Feature Paper | 17 April 2023

Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm

Yujiang Li 1 *

Jinghua Cao 1

1 School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China

* Corresponding Author

Received: 01 April 2023, Accepted: 17 April 2023, Published: 17 April 2023

Abstract

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.

Graphical Abstract

Keywords

Uniform coverage

discrete binary particle swarm optimization algorithm

wireless sensor network

optimal deployment

Cite This Article

Li, Y., & Cao, J. (2023). Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm. IECE Transactions on Internet of Things, 1(1), 1-8. https://doi.org/10.00000/TIOT.2023.100001

References

[1] Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619-632, doi: 10.1016/j.jnca.2011.11.016.

[2] Renold, A. P., & Chandrakala, S. (2016). Survey on state scheduling-based topology control in unattended wireless sensor networks. Computers & Electrical Engineering, 56, 334-349, doi: 10.1016/j.compeleceng.2015.12.024.

[3] Zhang, B., Tong, E., Hao, J., Niu, W., & Li, G. (2016). Energy efficient sleep schedule with service coverage guarantee in wireless sensor networks. Journal of Network and Systems Management, 24, 834-858, doi: 10.1007/s10922-015-9361-9.

[4] Singh, B., & Lobiyal, D. K. (2013). Traffic-aware density-based sleep scheduling and energy modeling for two dimensional Gaussian distributed wireless sensor network. Wireless personal communications, 70, 1373-1396, doi: 10.1007/s11277-012-0754-8.

[5] Li, J., Chen, Z., Cheng, L., & Liu, X. (2022). Energy data generation with wasserstein deep convolutional generative adversarial networks. Energy, 257, 124694, doi: 10.1016/j.energy.2022.124694.

[6] Wang, A., Liu, Y., Zhang, J., & Liu, Y. (2016). Coverage algorithm for finding the minimum 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 wireless sensor network based on parallelized cuckoo search algorithm. Application Research of Computers, 35(7), 2063-2065.

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

[9] Zhou, L., Yang, K., Zhou, P. (2010). Optimal coverage configuration based on artificial 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. Chinese Journal of Sensors and Actuators, 28(4),578-584.

[11] Liu, Q., Cheng, L., Jia, A. L., & Liu, C. (2021). Deep reinforcement learning for communication flow control in wireless mesh networks. IEEE Network, 35(2), 112-119, doi: 10.1109/MNET.011.2000303.

[12] Huang, Y., Cheng, L., Xue, L., Liu, C., Li, Y., Li, J., & Ward, T. (2021). Deep adversarial imitation reinforcement learning for QoS-aware cloud job scheduling. IEEE Systems Journal, 16(3), 4232-4242, doi: 10.1109/JSYST.2021.3122126.

[13] Cheng, L., Wang, Y., Liu, Q., Epema, D. H., Liu, C., Mao, Y., & Murphy, J. (2021). Network-aware locality scheduling for distributed data operators in data centers. IEEE Transactions on Parallel and Distributed Systems, 32(6), 1494-1510, doi: 10.1109/TPDS.2021.3053241.

[14] Cheng, F., Huang, Y., Tanpure, B., Sawalani, P., Cheng, L., & Liu, C. (2022). Cost-aware job scheduling for cloud instances using deep reinforcement learning. Cluster Computing, 1-13, doi: 10.1109/MNET.011.2000303.

[15] Li, J., Tong, X., Liu, J., & Cheng, L. (2023). An Efficient Federated Learning System for Network Intrusion Detection. IEEE Systems Journal, doi: 10.1109/JSYST.2023.3236995.

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

[17] Wang, Y., Qiu, F. Y., & Guo, H. D. (2019). Adaptive inertia weight binary particle swarm optimization algorithm with mutation operator. Journal of Chinese Computer Systems, 40(04), 733-737.

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

[19] Li, Y., & Pan, B. (2018). Research of WSN Regional Coverage Based on Adaptive Mutation Binary Particle Swarm Optimization. Journal of Sichuan University of Science & Engineering (Natural Science Edition), 31(01), 20-24.

Publisher's Note

IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.