Sensor Parameter Estimation for Full-View Coverage of Camera Sensor Networks Based on Bounded Convex Region Deployment

Abstract

Recently, full-view coverage was introduced to capture intruders from any direction in camera sensor networks (CSNs), it can identify intruders more effectively than traditional coverage. However, the full-view coverage usually requires a large number of camera sensors so that its estimation problem becomes more complicated. In addition, in practical application scenario, the camera sensors are often randomly deployed in an irregular bounded region. In this paper, we assume that many heterogeneous camera sensors are deployed in a bounded convex region. In order to predict the sensor parameter needed to achieve any given full-view coverage probability (ratio) of the heterogeneous CSNs, we deduce the sensor parameter estimation model to estimate the sensor scale, sensing radius and other parameters so as to guide the engineer to design a better CSNs. Finally, To evaluate the accuracy of the proposed model, a series of simulation experiments are conducted to verify the results. By analysis of the results, we can conclude that the mean absolute coverage error defined in this paper is not greater than 6.5%