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Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm

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.