在资源受限的无线传感器网络中,低质量的无线链路严重限制了其大规模应用。基于WSN监测信号普遍具有的稀疏特性,提出了基于双层压缩感知(double process of compressive sensing)的有损无线链路稀疏信号传输架构,探索低质量无线链路下实时、高精度和高能效的稀疏信号传输方法。首先,将稀疏信号传输过程中的随机分组丢失现象建模为压缩感知的线性降维观测过程(被动CS过程)。然后,针对WSN为提高传输效率而采用的长数据分组,数据发送前在发送端引入线性随机降维投影——简易的信源编码操作(主动CS过程),避免块状数据丢失的发生。最后,接收端根据收到的有损数据利用压缩感知的方法重构原始信号。进一步根据压缩感知重构和无线通信的相关原理,推导出允许使用的发送端最小压缩率和最大分组长度。大量仿真实验表明,所提方法不仅可以保证数据的可靠准确传输,还能减小发送数据量,降低传输时延和节点能耗。
Target tracking is a typical and important application of wireless sensor networks(WSNs).Existing target tracking protocols focus mainly on energy efficiency,and little effort has been put into network management and real-time data routing,which are also very important issues for target tracking.In this paper,we propose a scalable cluster-based target tracking framework,namely the hierarchical prediction strategy(HPS),for energyefficient and real-time target tracking in large-scale WSNs.HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing.As a target moves in the network,cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target.The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads.A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another.Under the framework of HPS,we design and implement an energy-efficient target tracking system,HierTrack,which consists of 36 sensor motes,a sink node,and a base station.Both simulation and experimental results show the efficiency of our system.
