Robust collaborative mesh networking with large-scale distributed wireless heterogeneous terminals in industrial cyber-physical systems
Author(s) -
Tao Wang,
Jun Liu,
Lianglun Cheng,
Hong Xiao
Publication year - 2017
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147717729640
Subject(s) - computer science , wireless mesh network , distributed computing , order one network protocol , computer network , robustness (evolution) , wireless network , mesh networking , failover , load balancing (electrical power) , wireless , wireless wan , key distribution in wireless sensor networks , telecommunications , biochemistry , chemistry , geometry , mathematics , gene , grid
Industrial cyber-physical system is defined as transformative technologies for upgrading the traditional industrial mode. Wireless mesh network becomes a typical technology in industrial cyber-physical system for the network communication with large-scale distributed wireless terminals. However, the robustness of wireless mesh networks in the industrial environment is seriously challenged by worst working reliability of network nodes, more vulnerable wireless communication links, and so on. In this article, in order to enhance network robustness and reliability, we propose a robust collaborative mesh networking method for interconnecting large-scale distributed wireless heterogeneous terminals in industrial cyber-physical systems. First, moderate redundancy of network deployment is introduced to guarantee two-connectivity for each mesh router and two-coverage for each wireless terminal, and an improved metric for evaluating the overall network robustness is presented. Second, the robustness-aware collaborative mesh networking problem is formulated with a multi-objective optimization model, and an improved multi-objective particle swarm optimization algorithm based on self-adaptive evolutionary learning is exploited to search out the Pareto optimal particles with better distribution and diversity. The experimental results show how the network robustness and load-balancing performance change along with the increasing number of deployed mesh router/mesh gateways, and our method is helpful for finding out a robust wireless mesh network deployment scheme in industrial cyber-physical systems when given a deployment cost.
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