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An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks

机译：基于改进的自适应粒子群算法的定向传感器网络区域覆盖与能耗优化方法

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Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.
机译：覆盖率是反映方向性传感器网络（DSN）性能的重要指标。定向传感器节点的随机部署将导致许多覆盖盲区和重叠区域。此外，由于能量有限，节点的过早死亡也将直接影响网络的服务质量。为了解决这些问题，本文提出了一种基于改进的自适应粒子群算法（IAPSO）的新的区域覆盖和能耗优化方法。对于区域覆盖问题，我们建立了一个多目标优化模型，以通过感知方向旋转来提高覆盖率并减少冗余率。为了优化能耗，我们通过群集网络使能耗平均分布在每个传感器节点上。我们建立了一个簇头选择优化模型，该模型考虑了候选簇头的总剩余能量比和能耗平衡度。我们还提出了一种簇形成算法，其中成员节点通过权重函数选择其簇头。接下来，我们使用IAPSO解决两个优化模型，以实现高覆盖率，低冗余率和能耗平衡。大量的仿真结果表明，我们提出的方法比其他方法具有更好的性能。

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