Optimal data collection of multi‐radio multi‐channel multi‐power wireless sensor networks for structural monitoring applications: A simulation study. Issue 4 (11th February 2019)

• Record Type: Journal Article
• Authors: Chen, Zhicong; Li, Qinghua; Wu, Lijun; Cheng, Shuying; Lin, Peijie
• Is Part Of: Structural control and health monitoring. Volume 26:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-02-11
• Subjects: BFPA‐PSO -- meta‐heuristic optimization -- multi‐radio multi‐channel multi‐power -- optimal data collection -- structural health monitoring -- wireless sensors networks; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2328
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: Structural health monitoring (SHM) is a kind of data‐intensive applications for wireless sensors network (WSN), which usually requires a high network capacity. However, the bandwidth of traditional single‐radio single‐channel (SR‐SC) WSN is quite limited. In order to meet the requirement of structural monitoring, we investigate the multi‐radio multi‐channel multi‐power (MR‐MC‐MP) communication to improve the data collection performance of SHM‐oriented WSNs in terms of network capacity and power consumption. First, the data collection problem in MR‐MC‐MP WSNs is modeled as an optimization problem under the constraint of available time slots, radios, channels, and power levels. And then, combining the fast convergence of the particle swarm optimization (PSO) algorithm and high exploration performance of flower pollination optimization (FPA) algorithm, we propose a novel binary hybrid meta‐heuristic algorithm named BFPA‐PSO to solve the problem. In order to verify the advantage of the proposed BFPA‐PSO, some other meta‐heuristic algorithms are tested for the problem as well. Finally, several simulation experiments are carried out to test and compare the performance of different algorithms. Experiment results demonstrate that the proposed BFPA‐PSO algorithm has superior performance in terms of network capacity and energy consumption.