Successful tracking of a mobile target with a sensor network requires effective answers to the challenges of uncertainty in the measured data, small latency in acquiring and reporting the tracking information, and compliance with the stringent constraints imposed by the scarce resources available on each sensor node: limited available power, restricted availability of the inter-node communication links, relatively moderate computational power. This thesis introduces the architecture of a hierarchical, self-organizing, two-tier, mission-specific sensor network, composed of sensors and routers, to track the trajectory and velocity of a single mobile target in a two-dimensional convex sensor field. A query-driven approach is proposed to input configuration parameters to the network, which allow sensors to self-configure into regions, and routers into tree-like structures, with the common goal of sensing and tracking the target in an energy-aware manner, and communicating this tracking data to a base station node incurring low-overhead responses, respectively. The proposed algorithms to define and organize the sensor regions, establish the data routing scheme, and create the data stream representing the real-time location/velocity of a target, are heuristic, distributed, and represent localized node collaborations. Node behaviours have been modeled using state diagrams and inter-node collaborations have been designed using straightforward messaging schemes. This work has attempted to establish that by using a query-driven approach to track a target, high-level knowledge can be injected to the sensor network self-organization processes and its following operation, which allows the implementation of an energy-efficient, low-overhead tracking scheme. The resulting system, although built upon simple components and interactions, is complex in extension, and not directly available for exact evaluation. However, it provides intuitively advantageous behaviours. / Thesis (Master, Computing) -- Queen's University, 2009-03-04 11:18:14.392