A novel target movement model and energy efficient target tracking in sensor networks

Energy awareness is a crucial component in the design of wireless sensor networks at all layers. This paper looks into efficient energy utilization of a target tracking sensor network by predicting a target's trajectory through experience. Whilst this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors and yet assuming minimal locality information. We present a novel target trajectory model adapted from the Gauss-Markov mobility model, formulate the tracking problem as a hierarchical Markov decision process (HMDP) and solve it through neuro-dynamic programming (NDP). Our HMTT (hierarchical MDP for target tracking) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains acceptable tracking accuracy through trajectory prediction (spatial management). Analysis and simulation results demonstrate its effectiveness in energy conservation and tracking accuracy against other known target tracking algorithms.