摘要:在无线可充电传感器网络（wireless rechargeable sensor network，简称WRSN）中，所面临的一项重要挑战是如何在高效收集传感器节点数据的同时，降低网络整体能量消耗.大多数现有数据收集策略或是不能适应大规模的充电传感器网络，或是没有充分考虑到传感器节点能量补充的问题，这将严重降低网络的通信量和生命周期.为此，针对WRSN中数据收集和网络能耗的问题，提出使用数据收集小车（data collection vehicle，简称DCV）和无线充电小车（wireless charging vehicle，简称WCV）分别负责数据收集和节点充电，从而在优化数据收集的同时，保证网络的持续性.首先，为了提高数据收集和充电效率，根据传感器节点的邻域相似度以及节点之间的距离，将网络自适应划分为多个子区域；随后，根据传感器节点k跳路由之内的电池能量和节点社交性，选择各个区域内数据收集锚点；接着，通过分析传感器节点自身能量消耗与网络系统能耗之间的关系，设计了网络能耗优化函数，通过对偶分解和次梯度的方法求得优化函数的最佳节点感知率和物理链路传输率；最后，实验验证了该网络不仅能有效降低网络整体能耗，而且具有较低的节点死亡数目.



Abstract:In wireless rechargeable sensor network (WRSN), how to efficiently collect data from sensor nodes and reduce the system energy cost is very challenging. However, most recent data collection works either cannot adapt to the large-scale rechargeable sensor network or do not take into account the sensors' energy recharging problem. They will lead to the decrease of network traffic and lifetime. Thus, aiming at the problem of data collection and network cost in WRSN, this study proposes to use the data collection vehicle (DCV) and wireless charging vehicle (WCV) to be responsible for data collection and wireless charging respectively. It can optimize data collection and ensure network continuity at the same time. Firstly, in order to improve the data collection and charging efficiency to divide the large network into several parts, this study proposes a network partition scheme based on the neighborhood similarity of sensor nodes and the distance between nodes. Then, to each part, an anchor selection scheme based on tradeoff between neighbor amount and residual energy within k hops is proposed. Next, a network cost optimization function is designed by analyzing the relationship between sensor energy consumption and network cost. The optimal sensor nodes sensing data rate and link rate are obtained by dual decomposition and sub-gradient the cost function. The results demonstrate the network can not only reduce the overall network cost but also reduce the amount of dead sensor nodes.



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