Prediction-Based Proactive Cluster Target Tracking Protocol for Binary Sensor Networks

An efficient, economical and robust strategy for target tracking in binary sensor network is proposed in this paper. By adopting the binary variational filtering algorithm, considerable tracking quality is ensured, while decreasing communication between sensors compared to a particle filtering algorithm. Based on the proactive clustering, the entire sensor network is subdivided into several clusters. Only cluster heads are configured with more available energy and high processing capability, reducing thus the hardware expenditure. Furthermore, precise prediction of the target position and the cluster activation protocol ensure that the most potential cluster is activated to perform target tracking, reducing consumed energy during the hand-off operation. Employing of the binary variational filtering algorithm and the exception handle scheme ensure robustness in coping with the case of highly non-linear and non-Gaussian environments.