Orientation Optimization for Full-View Coverage Using Rotatable Camera Sensors

Recently, full-view coverage has been introduced to capture intruders from multiple directions in the camera sensor networks. It is more efficient than traditional coverage in identifying the intruders. However, full-view coverage typically calls for a large number of camera sensors. Hence, we exploit limited mobility or orientation to improve the performance of full-view coverage since camera sensors typically can rotate to cover more areas without being relocated after installation. Observing that target points may not be full-view covered constantly due to the sensor rotation, we emphasize the importance of the fairness-based coverage maximization problem, i.e., how to schedule the orientations of camera sensors to maximize the minimum cumulative full-view coverage time of target points. To solve this issue, we first try to reduce the dimension space of orientations by dividing the orientation space into a set of discrete directions. We then study how to select the minimum number of sensing regions that camera sensors should rotate to cover in order to ensure the full-view coverage of all target points. Next, we unveil the relationship between the full-view coverage and target points, which are spatially correlated. Based on these results, we devise a centralized algorithm to solve the problem based on “largest demand first serve” principle, by which the target points with less cumulative full-view coverage time will be preferentially selected to be full-view covered with a higher probability. We further design a distributed solution as a counterpart of the centralized algorithm. Extensive simulations are presented to show the performances of the proposed algorithms. Results show that exploiting limited mobility of sensor rotation has good potential in promoting the efficiency and reducing the cost of ensuring full-view coverage.