## 4 Visual Data Capture in WVSNs  
Because of the limited view angle of the camera on the sensor node, it often needs several cameras in different orientations to fully cover the object. Given that the sensor nodes are randomly deployed in the field, to identify a minimum set of sensors needed to fully cover the object becomes a challenging issue. One trivial solution would be letting the sensor nodes whose FoV cover the object to transmit the visual data to the sink node. With this approach, there would be a lot of redundant visual information transmitted back to the sink. This comes with significant extra energy consumption and transmission bandwidth. Another possible solution is to identify the minimum number of sensors that can cooperatively cover the object from different orientations. Camera correlation models are an active research topic in the WVSN field. Camera sensor nodes are correlated if their FoVs are overlapped. Depending on the relative angle between the correlated camera sensors nodes, the captured images between these correlated sensors could be redundant or supportive. Intuitively, when the relative angle is \(0\,\lx@math@degree\), the correlated sensors catch the image with the same angle, and this implies that the images are redundant and only one copy of the image should be transmitted back to the sink node. When the relative angle is \(180\,\lx@math@degree\), the correlated sensors catch the image from opposite directions, implying that the images are highly supportive (especially for security surveillance) and all the images should be transmitted back to the sink node. However, in most of the cases, the relative angle is not exactly \(0\,\lx@math@degree\) or \(180\,\lx@math@degree\), so sophisticated methods should be devised to determine if the captured images are redundant or supportive. In [26], a spatial correlation function is proposed to describe the degree of correlation for the images observed by cameras with overlapped FoVs. The correlation entropy function is defined to measure the amount of information provided by these cameras. Based on the correlation-based entropy function, the correlation-based camera selection algorithm is proposed to select the cameras to transmit the captured images back to the sink node. Hence, instead of choosing all the cameras whose FoV covers the object to transmit the visual data, the correlation-based selection algorithm in [26] could select fewer cameras with a sufficient amount of visual information. In [27], they compute the intersection area from the FoVs of multiple camera sensor nodes with a 3D directional sensing model. With this model, the computation of intersection areas could be reduced to calculating the relative position between the camera sensor nodes and it could be done distributedly. The camera sensor node activation problem in two-tier WVSNs is considered in [28]. The first tier is the scalar sensor node that is used to detect the object and sent a signal to activate the camera sensor node in the second tier. Because of the restrictions on the camera lens, an object that is too close or too far from the camera will not be in focus. By setting the near line and far line in the FoV, only the camera sensor nodes whose good FoV (between the near line and far line in FoV) that can cover the object in focus will be activated. A similar idea of good FoV is also shown in [29]. It is the idea of generating look-up tables to identify the camera nodes with overlapping FoVs. However, the construction of look-up table is based on the distance between the object and the center of the FoV, and this information is difficult to get since the location of the object and the center of the FoV are difficult to determine accurately. In [30], an intelligent camera sensor actuation mechanism is proposed to actuate (turn on) the least number of camera sensor nodes to reduce the redundancy in the multimedia data while still providing the necessary event coverage.