Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm

Computer Science and Information Systems

Wei Ye¹  Ling Wang² 

[1] Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida;Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University

关键词: industrial wireless sensor networks;    node placement;    binary particle swarm optimization;    adaptive mutation;   

DOI  :  10.2298/CSIS120117058W

学科分类：社会科学、人文和艺术（综合）

来源: Computer Science and Information Systems

【 摘 要 】

Industrial Wireless Sensor Networks (IWSNs), a novel technique in industry control, can greatly reduce the cost of measurement and control and improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial applications, the communication reliability of IWSNs has to be guaranteed as the real-time field data need to be transmitted to the control system through IWSNs. Obviously, the network architecture has a significant influence on the performance of IWSNs, and therefore this paper investigates the optimal node placement problem of IWSNs to ensure the network reliability and reduce the cost. To solve this problem, a node placement model of IWSNs is developed and formulized in which the reliability, the setup cost, the maintenance cost and the scalability of the system are taken into account. Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme. After the verification of the model and optimization algorithm on the benchmark problem, the presented AMPBPSO and the optimization model are used to solve various large-scale optimal sensor placement problems. The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (GA) in terms of search accuracy and the convergence speed with the guaranteed network reliability.

【 授权许可】

CC BY-NC-ND