Optimal data collection of multi‐radio multi‐channel multi‐power wireless sensor networks for structural monitoring applications: A simulation study
Optimal data collection of multi‐radio multi‐channel multi‐power wireless sensor networks for structural monitoring applications: A simulation study
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DOI：
10.1002/stc.2328
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发表时间：
2019-02
影响因子：
5.4
通讯作者：
Zhicong Chen;Qinghua Li;Lijun Wu;Shuying Cheng;P. Lin
中科院分区：
文献类型：
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作者：
Zhicong Chen;Qinghua Li;Lijun Wu;Shuying Cheng;P. Lin
结构健康监测（SHM）是无线传感器网络（WSN）的一种数据密集型应用，通常需要较高的网络容量。然而，传统的单无线电单信道（SR-SC）无线传感器网络的带宽是相当有限的。为了满足结构监测的需求，本文研究了多无线电多信道多功率（MR-MC-MP）通信技术，以提高面向结构监测的无线传感器网络的数据采集性能。首先，在MR-MC-MP无线传感器网络的数据收集问题建模为一个优化问题下的可用时隙，无线电，信道和功率电平的约束。然后，结合粒子群优化（PSO）算法的快速收敛性和花授粉优化（FPA）算法的高搜索性能，提出了一种新的二元混合Meta启发式算法BFPA-PSO。为了验证所提出的BFPA-PSO的优势，一些其他的Meta启发式算法进行了测试，以及该问题。最后，通过仿真实验对不同算法的性能进行了测试和比较。实验结果表明，BFPA-PSO算法在网络容量和能耗方面具有上级性能.
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.