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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.

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Abstract:The combination of remote sensing and sensor network technologies can provide unprecedented earth observation capabilities, and has attracted high R&D interest in recent years. However, the procedures and tools used for deployment, geo-referenciation and collection of logged measurements in the case of traditional environmental monitoring stations are not suitable when dealing with hundreds or thousands of sensor nodes deployed in an environment of tenths of hectares. This paper presents a scheme based on Unmanned Aerial Systems that intends to give a step forward in the use of sensor networks for environment observation. The presented scheme includes methods, tools and technologies to solve sensor node deployment, localization and collection of measurements. The presented scheme is scalable-it is suitable for medium-large environments with a high number of sensor nodes-and highly autonomous-it is operated with very low human intervention. This paper presents the scheme including its main components, techniques and technologies, and describes its implementation and evaluation in field experiments.

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.