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An Elite Hybrid Particle Swarm Optimization for Solving Minimal Exposure Path Problem in Mobile Wireless Sensor Networks

机译：解决移动无线传感器网络最小暴露路径问题的精英混合粒子群算法

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Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms.
机译：移动无线传感器网络（MWSN）是无线传感器网络（WSN）的子类，最近在学术界引起了越来越多的关注。 MWSN可以提高网络覆盖质量，这反映了传感器对目标区域的监视或跟踪情况。为了评估WSN的覆盖质量，我们经常在感测领域中使用最小暴露路径（MEP）作为有效度量。 MEP是指入侵者可以通过的最差覆盖路径，而其被检测到的可能性最低。对于网络设计师来说，认识WSN的漏洞并进行必要的改进非常有价值。先前的大多数研究都集中在静态传感器网络下的此问题上，该网络可能会遇到一些缺点。即，传感器位置的故障会导致网络中出现覆盖漏洞。本文研究在MWSN（以下称为MMEP）中寻找最小暴露路径的问题。首先，我们提出MMEP问题。然后，将MMEP问题转换为具有高维，无微分和非线性的数值函数极端问题。为了有效地应对这些特征，我们提出了HPSO-MMEP算法，该算法是遗传算法与粒子群算法的集成。此外，我们还为实验仿真创建了各种定制的MWSN拓扑。实验结果表明，HPSO-MMEP适用于转换后的MMEP问题，并且比现有算法具有更好的性能。

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