## 2 Related Work  
Most of the work in WMSN is related to multimedia sensor hardware [3, 4]. [PERSON] _et al._, [6], analysed how coverage-based algorithms designed for scalar sensors do not behave well in a video sensor network in terms of environment monitoring. The reason is the mismatch between sensor location and sensing region (_i.e._, FoV). In [8], the authors propose an optimal polynomial time algorithm for computing the worst-case breach coverage in FoV sensor networks, where \"breach\" is defined as the closest distance of the moving target to any sensor. The algorithm uses FoV-Voronoi graphs where only FoV shared areas participate in the construction of the Voronoi graph. The algorithm is used to inspect the probability that a random deployment results in an unobserved path between initial and final observed regions. The authors of [7] present an algorithm that enables self configurable sensor orientation calculation. The goal is finding the camera orientation that minimizes occlusion viewpoints and overlapping areas. All of them assume random camera deployments. The main justification for random deployments, [6-8], is to obtain sensing redundancy and avoid the negative effect of occlusions. Other approaches consider the well-known \"art-gallery\" problem when the objective is that all points in a 2D-plane are covered by at least one camera. Clustering has been studied in great depth in the field of wireless scalar sensor networks. The work [5] surveys present clustering protocols and algorithms in this field and provides the main keys for designing such algorithms. Cluster formation and cluster-head selection manners are the key factors in clustering algorithms. Most of the clustering algorithms form clusters based on sensor neighbourhood (radio-coverage) or based on the distance from the cluster-head. The number of clusters and cluster-size are parameters that usually impact cluster formation procedures. [PERSON] _et al._, [9], state the fact that directional FoV should be the key parameter to form clusters in WMSN and also highlight examples in which video sensor spatial-based collaboration provides robust object detection by cross-validating information. On the other hand, some algebra and geometric calculations are related to our research. Works [10-13] present different methods for testing whether two triangles intersect each other. Their techniques solve the basic sets of linear equations associated with the problem and exploit the relations between these sets to distinguish the existing overlaps. Object detection is known as one of the main tasks in WMSNs. [PERSON] _et al._, [14], have employed their low power and inexpensive camera device, Cyclops, for this aim. There, Cyclops periodically captures images from the outside world and is able to construct the stationary background using the moving average of the series of the images. It further constructs a model of the foreground based on the absolute difference of the instantaneous image and constructed background. In their case, Cyclops periodically sleeps for intervals of 15 seconds and executes the algorithm after each wake-up. Hierarchical network architectures with multiple tiers are designed modularly with assigned tasks for each tier. As in SensEye [15], object detection can be achieved with lightweight low-cost cameras in a first tier. Cyclops [14] is an example of a sensor in this tier. Each sensor node is duty-cycled and awakened periodically to capture an image and detect the presence of new objects independently of other nodes. Object detection is performed via simple frame differencing. Object recognition can be achieved with more computationally capable cameras in a second tier. Finally, object tracking involves PTZ (Pan-Tilt-Zoom) cameras in a third tier. Several wireless platforms and cameras may be found in the literature applying these concepts: CITRIC, Panoptes, Meerkats [16-19].