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| Industrial Wireless Sensor Networks (IWSNs), a novel technique in the field of industrial control, can greatly reduce the cost of measurement and control, as well as improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial areas, IWSNs has high requirements for reliability, especially for large-scale industry application. As the network architecture has great influences on the performance of IWSNs, this paper discusses the node placement problem in IWSNs. Considering the reliability requirements, the setup cost and energy balance in IWSNs, the node placement problem in large-scale IWSNs is a non-linear constrained multi-objective optimization problem. In this paper, the node placement model of IWSNs is built and an adaptive mutation probability binary Particle Swarm Optimization algorithm (AMPBPSO) is proposed to solve this model. Performance comparisons show that the proposed AMPBPSO outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (SGA) and AMPBPSO is more effective for the optimal node placement in IWSNs with various kinds of field scales and different node densities in terms of network reliability, load uniformity, total cost and convergence speed. |
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| Keywords:Industrial Wireless Sensor Networks;Node Placement;Particle Swarm Optimization;Adaptive Mutation |
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