This paper proposes mechanisms to efficiently address critical tasks in the operation of cluster-based target tracking, namely: (1) measurement integration, (2) inclusion/exclusion in the cluster, and (3) cluster head rotation. They all employ distributed probabilistic tools designed to take into account wireless camera networks (WCNs) capabilities and constraints. They use efficient and distribution-friendly representations and metrics in which each node contributes to the computation in each mechanism without requiring any prior knowledge of the rest of the nodes. These mechanisms are integrated in two different distributed schemes so that they can be implemented in constant time regardless of the cluster size. Their experimental validation showed that the proposed mechanisms and schemes significantly reduce energy consumption (>55 percent) and computational burden with respect to existing methods.

This paper proposes a scheme that exploits synergies between RSSI and camera measurements in object localization and tracking using Wireless Camera Networks (WCN). It is based on three main mechanisms: a training method that accurately adapts RSSI-range models to the particular environment; a sensor activation/deactivation method that balances the different information contribution and energy consumptions of camera and RSSI measurements; and a distributed Information Filter to integrate the available measurements. The joint use of these mechanisms drastically reduces energy consumption -40% with no significant degradation w.r.t. existing schemes based on only cameras and shows better robustness to target occlusions. The scheme has been implemented and validated in the indoor CONET Integrated Testbed.

The paper presents entropy-based mechanisms to improve energy efficiency and robustness to transmission errors in camera-based object tracking systems. The main mechanisms are: an entropy-based algorithm that dynamically activates/deactivates nodes; a method that dynamically selects the cluster head using entropies and transmission error rates between the cluster nodes; and a distributed Extended Information Filter (EIF) that integrates measurements gathered within the cluster. These mechanisms have been integrated and implemented in a camera-based Wireless Sensor Network, being each camera node comprised of a TelosB WSN node connected to a CMUcam3 module. The proposed mechanisms and tracking system have been experimented and validated in the CONET Robot-WSN Integrated Testbed.