Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search

The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability an...

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Bibliographic Details
Published in:Advances in manufacturing 2016-03, Vol.4 (1), p.66-78
Main Authors: Wang, Ling, An, Lu, Ni, Hao-Qi, Ye, Wei, Pardalos, Panos M., Fei, Min-Rui
Format: Article
Language:English
Subjects:
Control
Design engineering
Engineering
Evolution
Machines
Manufacturing
Mechatronics
Nanotechnology and Microengineering
Optimization
Placement
Processes
Real time
Remote sensors
Robotics
Searching
Wireless networks
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Summary:The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliability, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolution harmony search (MOBDEHS) is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deployment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOBDEHS is an effective tool for multi-objective node placement problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II (NSGA-II) and modified NSGA-II.
ISSN:2095-3127
2195-3597
DOI:10.1007/s40436-016-0135-8