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在无线传感网络中,将观测数据汇集至融合中心的过程会消耗大量能量.设计一种用于无线传感网络的离散拉普拉斯算子(DLO-WSN),提出基于该算子的数据选择算法(LDS).传感器在每个观测周期内通过与相邻传感器的互动评估自身数据的重要性,仅选择重要性较高的数据发送至融合中心,从而降低网络的能量消耗.为弥补数据选择带来的信息损失,提出了松弛的数据恢复算法(RDR)和增强的数据恢复算法(EDR),使融合中心可以结合网络结构信息,通过接收到的部分数据集恢复完整数据集.实验结果表明,在场重建应用中,LDS算法能够在系统性能和能耗之间取得折衷,结合RDR或EDR算法,系统性能接近单纯通过数据选择所能达到的最佳性能.
In wireless sensor networks, the process of pooling observational data to the fusion center consumes a lot of energy.Designing a discrete Laplacian (DLO-WSN) for wireless sensor networks, this paper proposes a novel algorithm based on this operator Data Selection Algorithm (LDS). Sensors evaluate the importance of their own data in each observation period by interacting with neighboring sensors, selecting only more significant data to be sent to the fusion center to reduce network energy consumption. Data loss, we propose a loose data recovery algorithm (RDR) and an enhanced data recovery algorithm (EDR), so that the fusion center can combine the network structure information and recover the complete data set through the received partial data set. The results show that LDS algorithm can make a compromise between system performance and energy consumption in field reconstruction. Combined with RDR or EDR algorithm, the system performance is close to the best performance that can be achieved by data selection alone.