Citation: HAN MINGDING (2010-03-31). Resource management for target tracking in wireless sensor networks. ScholarBank@NUS Repository.
Abstract: Target tracking applications are popular in wireless sensor networks, in which distributed low-power devices perform sensing, processing and wireless communication tasks, for applications such as indoor localization with ambient sensors. Being resource-constrained in nature, wireless sensor networks require efficient resource management to select the most suitable nodes for sensing, in-network data fusion, and multi-hop data routing to a base-station, in order to fulfill multiple, possibly conflicting, performance objectives. For example, in target tracking applications, reducing sensing and update intervals to conserve energy could lead to a decline in application performance, in the form of tracking accuracy. In this thesis, we study resource management approaches to address such challenges, through simulations and test-bed implementations. There are two main components of this thesis. We first address indoor target tracking using a state estimation algorithm and an information-driven sensor selection scheme. An information-utility metric is used to characterize application performance for adaptive sensor selection. We address the system design choices such as the system architecture and models, hardware, software and algorithms. We also describe the system implementation in a test-bed, which incorporates mobile devices such as smart-phones, for control and monitoring of the wireless sensor network, querying of sensors, and visualization interfaces. The second component is a simulation study of a distributed sensor election and routing scheme for target tracking in a multi-hop wireless sensor network. An objective function, which trades-o ff information-quality with remaining energy of nodes, is used for sensor election. Subsequently, energy-efficient multi-hop routing is performed back to the sink node. In our non-myopic approach, we convert the remaining energy of nodes into an additive cost-based metric, and next-hop nodes are selected based on the expected sum of costs to the base station. A decision-theoretic framework is formulated to capture the non-myopic decision-making problem, and a reinforcement learning approach is used to incrementally learn which nodes to forward packets to, so as to increase the delivery ratio at the sink node.