Camera Selection Algorithm to Increase Height Estimation Precision and Lifetime of the Network

Research activities in wireless sensor networks have been growing in recent years. In such a network, sensor nodes collect scalar data such as temperature, pressure, humidity and etc. Scalar data are not sufficient for some applications like automatic surveillance and environmental monitoring. With recent advances in the technology of image sensors and embedded processors, most of attentions have been concentrated on camera sensor networks. Therefore, these networks are being utilized in many applications such as environmental monitoring and target tracking. Target detection, localization and tracking in a specific region are the most important issues in these applications. Due to quantization in CCD cameras, the obtained information from these nodes is not very accurate.
In this paper, we present a geometrical model to analyze the quantization error. The proposed model can be generalized to a multi-camera system, where more than two cameras are used to have more accurate estimation of the target location. This error can be decreased by selecting cameras in the network with appropriate positions. Camera selection problem in camera sensor networks is essential not only to improve the accuracy of the network but also to compensate for the processing, energy and bandwidth limitation of each sensor node. Hence, for more accurate estimation of the target height and to prolong the lifetime of the network, we propose the priority as well as a genetic search algorithm. In these algorithms, the precision of height estimation and also the resource constraint of the network are considered. Therefore, the accuracy of the measurements and also the lifetime of the network are increased. Simulation results show that the proposed metrics decrease the computational overhead and energy consumption of the network.