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Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Hang Yang1,2,*, Xunbo Li1, Witold Pedrycz2

1 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China
2 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

* Corresponding Author: Hang Yang. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 1531-1551. https://doi.org/10.32604/iasc.2023.039256

Abstract

Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system. The model improves the power supply to a more considerable extent with the least number of power bank deployments. The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm. An overall probabilistic coverage rate analysis of every point on the FOI is provided, not limiting the scope to target points or areas. Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement. A dynamic search strategy (DSS) is proposed to modify the artificial bee colony (ABC) and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems. Further, the cellular automata (CA) is utilized to enhance the convergence speed. The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process. Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method. The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC (GABC) algorithms. The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms (i.e., ABC, GABC). The proposed model is, therefore, effective and efficient for optimization in the IWSN.

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Cite This Article

APA Style
Yang, H., Li, X., Pedrycz, W. (2023). Hybrid power bank deployment model for energy supply coverage optimization in industrial wireless sensor network. Intelligent Automation & Soft Computing, 37(2), 1531-1551. https://doi.org/10.32604/iasc.2023.039256
Vancouver Style
Yang H, Li X, Pedrycz W. Hybrid power bank deployment model for energy supply coverage optimization in industrial wireless sensor network. Intell Automat Soft Comput . 2023;37(2):1531-1551 https://doi.org/10.32604/iasc.2023.039256
IEEE Style
H. Yang, X. Li, and W. Pedrycz "Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network," Intell. Automat. Soft Comput. , vol. 37, no. 2, pp. 1531-1551. 2023. https://doi.org/10.32604/iasc.2023.039256



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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