## 4 Visual Data Capture in WVSNs
Two-tier sensor node (scalar sensor and camera sensor) architecture is considered, where the camera sensors can utilize the information from the scalar sensor to determine the priority of actuation. Basically, a camera sensor node hearing a higher number of messages from the scalar sensors indicates that the event has a higher chance to be in the FoV of this camera sensor node and it should be given higher priority to be actuated. By exchanging the number of the scalar sensors and the FoV, neighboring camera sensor nodes could collaborate to determine who should be actuated to capture the image and transmit. However, occlusion is not considered in [30]. In WVSNs, when the deployed sensors are running out of batteries or the wind changes the camera orientation, the network topology will change. Then an interesting question is how to identify the collaborative camera sensors to capture the images of the interests efficiently in a changing WVSN topology. In image/vision processing technology, \"camera calibration\" could determine each camera's position, orientation, and focal length automatically. With these camera parameters, each camera sensor could locate its collaborative sensors to perform high level vision processing tasks (e.g., multiple object tracking) more efficiently. [PERSON] _et al_. [31; 32; 33; 34] proposed distributed camera calibration algorithms to estimate the camera's position, orientation, and focal length. Then a vision graph is constructed to identify the overlapped FoV relations for the camera sensors. An edge on the vision graph represents two cameras that image a sufficiently large part of the same environment. By using the vision graph, when an event occurs, a set of collaborative camera sensors of FoV covering the event could be identified efficiently to perform high level image/vision processing. A similar vision graph idea is also shown in [35], where a mathematical model is proposed to analyze the overlapped FoVs among the camera sensor nodes. For any sensor node, the probability of having a vision graph neighbor (i.e., the neighboring with the overlapped FoV) with respect to the distance between these two sensors is determined. With this vision graph neighbor information, it could identify the set of sensors that should capture the visual data cooperatively. In WVSNs, the deployed camera sensors might not be able to cover the images of interest because of occlusion. In [36], virtual view image generation in WVSNs is studied. The virtual view image is generated when a user wants to see a particular object which is not within the FoV of any deployed camera sensor. Two visual sensors with opposite location and with smallest disparity criteria from the virtual view are selected. However, the generated virtual image quality is not examined carefully to justify this camera selection method. In contrast to the image capture in WVSNs, the video capture in WVSNs consumes more resources in terms of processing power, memory and power consumption. In addition, the stringent delay constraints incurred from real-time video applications (e.g., security surveillance) makes video capturs and processing more challenging than that of images. In recent works, to meet the real-time constraint, centralized algorithms are developed to control the camera sensors to capture the video stream. In [37], a real-time video surveillance application where the captured video is sent back to the centralized Remote Control Unit (RCU) to send the pan or tilt command to control the cameras for target tracking is described. In order to meet the real-time requirement, instead of identifying a minimum number of cameras for FoV coverage, the goal is to identify the delay-constrained camera control mechanism on the RCU to control cameras for target tracking. In [38], an application-independent task mapping and scheduling mechanism in multi-hop WVSNs is studied to meet the real-time guarantees.