Spatio-temporal estimation with Bayesian maximum entropy and compressive sensing in communication constrained networks

Large scale monitoring applications require large numbers of sensors deployed in a region for accurate and high resolution spatio-temporal measurements and estimation. This can be achieved practically by deploying a mix of high capacity, high precision, expensive and low capacity, low precision, cheap sensors in the monitored region. However, the resource constrained nature of low-capacity sensors, and the availability of limited numbers of high-capacity sensors are a challenge to achieving highly accurate estimations. In this paper we propose a framework combining Bayesian compressive sensing and a robust Bayesian maximum entropy based spatio-temporal estimation technique to address this important problem. Evaluation on real wireless sensor network data reveals the trade-off between the spatio-temporal estimation accuracy and the communication overhead incurred in the network, and provides a mechanism to choose the right compressive ratios, such that a given estimation accuracy is achieved for a known communication overhead in the network.