Deployment and power assignment problem in Wireless Sensor Networks for intruder detection application using MEA

R. Shaleni, S. R. Swaathiha, P. Karthikeyan
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引用次数: 3

Abstract

Wireless Sensor Network (WSN) design for intruder detection application requires the decision of deployment of nodes with respect to the lifetime of the network. Based on literature survey it is found that few works have been made on optimizing both decision variables for maximizing the network coverage and lifetime. But the above two objectives in the latter studies are considered individually without any application specific. In this work, it is defined as the multi-objective Deployment and Power Assignment Problem (DPAP) for intruder detection application is solved using Multi Objective Evolutionary Algorithm (MOEA) based on decomposition. The M-tour Selection (M-tourS), Adaptive crossover and Adaptive mutation are introduced to improve the MOEA/D algorithm. The DPAP decomposed into a set of sub problems that are classified based on the above proposed genetic operators into seven different combinations. The proposed operators adapt to the requirements and objective preferences of each combination dynamically during the evolution, resulting in significant improvements on the overall performance of MOEA/D. Simulation parameters are fixed by considering the above application specific. The results show that the proposed algorithm significantly better than the existing algorithms in different network instances.
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基于MEA的入侵检测无线传感器网络的部署和功率分配问题
针对入侵检测应用的无线传感器网络(WSN)设计要求根据网络的生命周期来决定节点的部署。通过文献调查发现,为实现网络覆盖和生命周期的最大化而同时优化决策变量的研究很少。但在后一种研究中,上述两个目标是单独考虑的,没有任何具体的应用。本文将入侵检测应用中的多目标部署和权力分配问题定义为基于分解的多目标进化算法(MOEA)。为了改进MOEA/D算法,引入了M-tourS选择、自适应交叉和自适应突变。DPAP分解为一组子问题,这些子问题基于上述遗传算子被分类为7种不同的组合。在演化过程中,所提出的操作方法可以动态地适应每种组合的要求和客观偏好,从而显著提高MOEA/D的整体性能。通过考虑上述特定应用,确定了仿真参数。结果表明,在不同的网络实例下,该算法明显优于现有算法。
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