Optimal sensor density for remote estimation over Wireless Sensor Networks

Wireless Sensor Networks (WSNs) enable a wealth of new applications where remote estimation is essential. Individual sensors simultaneously sense, process and transmit measured information over a lossy wireless network to a central base station, which processes the data and produces an optimal estimate of the state. In order to provide a quantitative design principles on the density of sensor required, we investigate the tradeoff between the estimation performance and the number of communicating nodes with respect to the major MAC protocols used in WSNs. The correlation between packet reception probability and the number of communicating nodes can be studied by selecting a Markov model for the communication protocol. A multi-sensor measurement fusion model is then used to feed a multi-sensor Kalman filtering algorithm to assess the impact of MAC protocols on estimation performance. The proposed approach is placed in the framework of convex optimization problems. A target tracking example illustrates the proposed approach.