Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks
Mobile Scheduling for Spatiotemporal Detection in Wireless Sensor Networks
复制标题
DOI：
10.1109/tpds.2010.41
复制
发表时间：
2010-12
影响因子：
5.3
通讯作者：
G. Xing;Jianping Wang;Zhaohui Yuan;R. Tan;Limin Sun;Qingfeng Huang;X. Jia;H. So
中科院分区：
文献类型：
--
作者：
G. Xing;Jianping Wang;Zhaohui Yuan;R. Tan;Limin Sun;Qingfeng Huang;X. Jia;H. So
为任务关键型应用部署的无线传感器网络(WSN)面临着一个根本挑战，即如何使用有限的感知能力来满足严格的时空性能要求。尽管高级网络规划和密集节点部署最初可能会达到所需的性能，但它们往往无法适应物理现实的不可预测性和多变性。为了解决静态无线传感器网络在目标检测方面的局限性，本文探讨了移动传感器的有效使用。我们提出了一种基于数据融合的检测模型，使静态和移动传感器能够有效地协作进行目标检测。提出了一种优化的传感器移动调度算法，在满足高检测概率、低系统虚警率和有限检测时延的时空性能要求的同时，最小化传感器的总移动距离。基于23个传感器节点采集的真实数据轨迹进行了大量的仿真，验证了该方法的有效性。
Wireless sensor networks (WSNs) deployed for mission-critical applications face the fundamental challenge of meeting stringent spatiotemporal performance requirements using nodes with limited sensing capacity. Although advance network planning and dense node deployment may initially achieve the required performance, they often fail to adapt to the unpredictability and variability of physical reality. This paper explores efficient use of mobile sensors to address limitations of static WSNs for target detection. We propose a data-fusion-based detection model that enables static and mobile sensors to effectively collaborate in target detection. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low system false alarm rate, and bounded detection delay. The effectiveness of our approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.