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. These networks consist of camera nodes such that each camera node integrates image sensor, embedded processor and wireless transceiver. Each camera node has the ability to locally process image data, extract suitable data and collaborate with other nodes based on the network application. 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 thesis, we propose a geometrical model for target localization error. In this model, the conical intersection region formed by corresponding pixels of two cameras is investigated. Thus, to determine the amount of error in the target localization via two cameras, the intersection of the corresponding cones of each camera is utilized. For specifying the points of intersection, we propose three approaches. Since the field of view of each pixel is considered as a cone, the proposed approaches can easily be applied to any configuration of cameras. Another advantage of the proposed approach is that it can be generalized to a multi-camera system, where more than two cameras are used to have more accurate estimation of the target location. In such a system, each added camera leads to cone intersection formations and the results of these intersections determine the amount of target localization error. 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. We propose three selection metrics: cost, distance between the nodes and the overlapped region between camera nodes. The cost metric consist of remaining battery of each node, routing cost and essentiality of each node in the network. The proposed selection metrics are utilized such that the computational overhead is decreased and the lifetime of the network is increased. Simulation results show that the distance and overlap metrics decrease the computational overhead while the network lifetime is also decreased. This is because, these metrics are not considered the energy consumption of each node. On the other hand, by considering the cost metric, the lifetime of networks is increased. Consequantly, to balance the tradeoff between these metrics and also to improve the accuracy of the measurment, the combination of these metrics must be used. Keywords: Camera sensor network, height estimation, camera selection, network lifetime