计算机科学
软件部署
无线传感器网络
概率逻辑
启发式
趋同(经济学)
无线
功率(物理)
实时计算
电信
计算机网络
人工智能
量子力学
操作系统
物理
经济
经济增长
作者
Xunbo Li,Xunbo Li,Witold Pedrycz
出处
期刊:Intelligent Automation and Soft Computing
[Computers, Materials and Continua (Tech Science Press)]
日期:2023-01-01
卷期号:37 (2): 1531-1551
标识
DOI:10.32604/iasc.2023.039256
摘要
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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