## 9 Coverage and Connectivity  
Coverage problem becomes critical in WMSN because the deployed multimedia sensors do not have omni-directional (antenna) coverage as the case of the scalar sensors, but they have the feature of capturing direction-sensitive multimedia content with larger sensing radii when there is a line of sight (LOS). Therefore, many proposed algorithms try to exchange local information between neighboring multimedia nodes to determine the most beneficial orientations of their coverage taking into account minimizing the effects of occlusions and overlapping sensing regions and improving the cumulative quality of sensed information from the region of interest. Knowing the overlapping areas between cameras allows exploiting the redundancy in camera sensor coverage in the network and also can be used to track moving objects in the environment. In [85], a distributed algorithm is proposed to detect the coverage of multimedia nodes and determines their orientation. The algorithm assumes that each multimedia node knows its location information and the location of all the obstacles around it. It starts with broadcasting HELLO messages between the neighbors to exchange the location information of each other and current FoV of each multimedia sensor. By using these information, each multimedia sensor detects the best pose for its field of view using one of the three tests. In Perimeter Test, each multimedia node, with the ability of panning 360\({}^{\circ}\) to scan its FoV disk, determines whether a visible FoV (full coverage without occluded or overlapping regions) exists in its FoV disk otherwise the node runs Neighbor-Distance Test. The node can know the overlapping regions by using the received location information from the neighbors. Neighbor-Distance Test examines whether a node has a visible FoV that might be overlapped with other neighbor, but the node needs to find the smallest overlapping FoV by scanning the FoV disk. Finally, if a node could not find a visible FoV even overlapped with other neighbor, the node runs the Obstacle-Distance test to avoid the occlusions from closer obstacles and maximize the visible FoV. An automated calibration protocol, called Snapshot, is presented in [86]. Snapshot determines and calibrates the location, orientation, and range of camera sensor in WMSNs using the inherent imaging abilities of the cameras themselves and four reference points along with using the principles from optics and geometry in the calibration process. Snapshot uses low-fidelity camera sensors (such as CMUcam or Cyclops) connected with limited computational recourse processors (Crossbow motes or Intel Stargates) and some of the sensors are equipped with a Cricket ultrasound receiver as a wireless calibration device for the reference point. This technique assumes that the intrinsic parameters (focal length, lens distortion, principal point) are known from the manufacture or can be estimated offline prior to deployment, and it only tries to determine the extrinsic parameters (coordinates of the camera, camera orientation, and the FoV of each camera) in order to use as less reference points as possible and reduce the computational cost. In order to determine the location of a camera sensor, the four reference points should be in the visual range of the camera and should not three of them lie along a straight line. By using two reference points and the principles of geometry, the location of the camera lies in closed surface. And by using combination of two reference points each time with the rest of the four points and taking the intersection points of the resulting surface from each calculation with the other, the location of the camera can be estimated after eliminating the false solutions using the optics principles. The orientation of the camera in the three axes (pan, tilt, and rotate) is also determined by using three reference points and the camera location from the previous process.