Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks
Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor Networks
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DOI：
10.1109/tim.2008.2005822
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发表时间：
2009-06-01
影响因子：
5.6
通讯作者：
Xie, Lihua
中科院分区：
文献类型：
--
作者：
Lin, Jianyong;Xiao, Wendong;Xie, Lihua
由于目标运动的不确定性和有限的传感器感知区域，无线传感器网络（WSNs）中基于单传感器的协同目标跟踪，如在许多以前的方法中所解决的，遭受低跟踪精度和缺乏可靠性时，目标不能被检测到的预定传感器。通常，激励多个传感器可以实现更好的跟踪性能，但具有高能耗。跟踪精度、可靠性和能量消耗受两个连续时间步长之间的采样间隔的影响。针对无线传感器网络中的协同目标跟踪问题，提出了一种自适应的节能多传感器调度方案。它计算最佳采样间隔，以满足预测跟踪精度的规格，选择集群的任务传感器根据其联合检测概率，并指定一个任务传感器作为簇头估计更新和传感器调度根据簇头能量测量（CHEM）功能。仿真结果表明，与现有的单传感器调度和具有均匀采样间隔的多传感器调度相比，所提出的自适应多传感器调度方案能够获得上级的能量效率和跟踪可靠性.同时满足跟踪精度要求。它还对过程噪声的不确定性具有鲁棒性。
Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability. while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.