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Healing Coverage Holes for Big Data Collection in Large-Scale Wireless Sensor Networks

机译:大型无线传感器网络中用于大数据收集的修复覆盖孔

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The quality of service is severely degraded by coverage holes in wireless sensor networks. This paper focuses on the coverage hole healing (CHH) problem for big data collection in a large-scale wireless sensor network (LS-WSN) where the LS-WSN containing both static sensors and mobile sensors with the topology control of LEACH algorithm. Meanwhile, the data volume transmitted by each sensor node may be inconsistent. Specifically, the target of the CHH problem is to find an optimal subset of mobile nodes from all mobile nodes while maximizing the transmission times (TT) that all dispatched mobile nodes can transmit in their lifetime. Hence, from the data-centric perspective, we propose a greedy healing algorithm (GHA) via the greedy-based heuristic strategy with low computational complexity to solve this CHH problem. Simulation results show that the proposed GHA can efficiently heal the coverage holes which significantly prolongs the network lifetime and observably enhances the quality of service (QoS) of WSNs while increasing the TT, transmitted data volume (TDV) and average residual energy of all dispatched mobile nodes.
机译:无线传感器网络中的覆盖漏洞严重降低了服务质量。本文重点研究大规模无线传感器网络(LS-WSN)中大数据收集的覆盖孔修复(CHH)问题,其中LS-WSN同时包含静态传感器和移动传感器,并具有LEACH算法的拓扑控制。同时,每个传感器节点发送的数据量可能不一致。具体地说,CHH问题的目标是从所有移动节点中找到最佳的移动节点子集,同时最大化所有已调度移动节点在其生命周期中可以发送的传输时间(TT)。因此,从以数据为中心的角度出发,我们通过基于贪婪的启发式策略提出了一种贪婪修复算法(GHA),该算法具有较低的计算复杂度来解决此CHH问题。仿真结果表明,所提出的GHA能够有效地修复覆盖孔,显着延长网络寿命,并显着提高WSN的服务质量(QoS),同时增加所有调度移动台的TT,传输数据量(TDV)和平均剩余能量节点。

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