Adaptive Bandwidth Allocation for Dynamic Event Region Detection in Wireless Sensor Networks

Detecting and reconstructing critical dynamic event regions at a control center is an important application of bandwidth-limited wireless sensor networks (WSNs). In this paper, the problem of adaptive bandwidth allocation for sensor data collection is studied. The spatiotemporal relationship of the evolving field is assumed and modeled by dynamic Markov random fields. Observations are collected from a network of sensors distributed in the field. To meet the stringent bandwidth and energy constraints in WSNs, only a few selected sensors are allowed to transmit compressed data to a control center. To reconstruct and track the field state map at each time step, a processing framework including sensor selection and local and central processing is proposed. Specifically, adaptive bandwidth allocation is obtained by solving a conditional entropy-based optimization problem. Mean-field approximation with incomplete quantized sensor observations is carried out at the center for dynamic event region detection. The overall communication costs in terms of the bandwidth and energy consumption of the proposed framework are evaluated by considering all possible overheads in a practical communication protocol. The performance is analyzed through simulations, and the effectiveness and efficiency are demonstrated by comparing with other methods.