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

Ling Wang1, 2, Wei Ye1, Haikuan Wang1 and Muhammad Ilyas Menhas1

  1. Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University
    200072 Shanghai, China
    {wangling, yeweiysh, hkwang, fuxp, mrfei, Ilyasmenhas}@shu.edu.cn
  2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida
    32611 Florida, USA

Abstract

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.

Key words

industrial wireless sensor networks, node placement, binary particle swarm optimization, adaptive mutation

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS120117058W

Publication information

Volume 9, Issue 4 (December 2012)
Special Issue on Recent Advances in Systems and Informatics
Year of Publication: 2012
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wang, L., Ye, W., Wang, H., Menhas, M. I.: Optimal Node Placement of Industrial Wireless Sensor Networks Based on Adaptive Mutation Probability Binary Particle Swarm Optimization Algorithm. Computer Science and Information Systems, Vol. 9, No. 4, 1553-1576. (2012)