Abstract
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.
Highlights
Industrial cyber-physical system (ICPS)[1] is considered to be a core technology system for creating an advanced industrial mode by integrating the emerging information and network technologies with the industrial process.[2]
With the improved binary multi-objective particle swarm optimization (MOPSO) algorithm based on self-adaptive evolutionary learning, we are able to obtain the Pareto front of the multi-objective optimization model for robust collaborative mesh networking presented in section ‘‘The problem.’’ The pseudo code of our algorithm is shown in Algorithm 1
With different numbers of mesh routers (MRs)/mesh gateways (MGs) (35 and 40) including 7 MGs, we evaluate the performance of the improved MOPSO algorithm by comparing with the original MOPSO algorithm, which is shown in Figures 9 and 10 The experimental results showed that (1) the particles in the Pareto front of the original MOPSO algorithm have gathered together with higher density, and the distribution performance of the Pareto front is worse than the improved MOPSO algorithm because the original MOPSO algorithm is easy to fall into local optimum
Summary
Industrial cyber-physical system (ICPS)[1] is considered to be a core technology system for creating an advanced industrial mode by integrating the emerging information and network technologies (e.g. data sensing, industrial network, high-performance computing, intelligent decision-making, and controlling) with the industrial process.[2]. In order to enhance the network error resilience, it is very necessary to improve the network robustness by introducing appropriate redundancy for the deployment of MRs/MGs. In this article, we proposed a robust collaborative mesh networking method with large-scale distributed wireless heterogeneous terminals in ICPSs, and the main contributions of this study are as follows: 1. First we introduced appropriate redundancy for the network deployment and proposed an improved evaluation metric of network robustness performance with the probability of network link failure, while taking into account the network connection robustness and fairness of both WTs and MRs/MGs. 2. DT represents the distance matrix, and Dij denotes the actual distance between WTi and WTj. Let VS = {S1, ..., Sm} denote the set of candidate locations (CLs) where to install MRs or MGs, which form a connected ad hoc network GS = (VS, ES, DS).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Distributed Sensor Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
