Simultaneous State Estimation of Cluster-Based Wireless Sensor Networks

Abstract

There is rich literature on the state estimation of wireless sensor networks (WSNs). Most of these works are based on the dynamic state-space model via Kalman filter or similar Markov models. In any network, information exchange is inevitable, and in this paper, we propose a Bayesian non-parametric approach for addressing this issue in the context of state estimation of WSNs. We consider a cluster-based WSN and consider a discrete-time linear Markov model for estimating the state values of the sensor nodes over time. For measuring the amount of information shared by the model parameters across different clusters, we consider non-parametric matrix stick-breaking priors for the cluster-specific model parameters. We demonstrate the usefulness of our proposed model in locating an immobile anomalous node in the network. We compute the time to locate the anomalous object and the false positive rate of our proposed approach. Simulation studies are performed to assess the operating characteristics of the proposed model. The proposed approach will be useful in emergency monitoring, medical genetics, geosciences, and many other disciplines where WSNs are frequently used for decision making.