Efficient integration of RSSI for tracking using Wireless Camera Networks

This paper proposes a scheme that efficiently exploits synergies between RSSI and camera measurements in cluster-based target tracking using Wireless Camera Networks (WCNs). The scheme is based on the combination of two main components: a training method that accurately trains RSSI-range models adapted to the conditions of the particular local environment; and a sensor activation/deactivation method that decides on the individual activation of sensors balancing the different information contributions and energy consumptions of camera and RSSI measurements involved in sensing. The scheme also includes a distributed Extended Information Filter that integrates all available measurements. The combination of these components originates self-regulated behaviors that drastically reduce power consumption and computational effort with no significant tracking degradation w.r.t. existing schemes based exclusively on cameras. Besides, it shows better robustness to target occlusions. The proposed scheme has been implemented and validated in real experiments.