<i>K</i>-Coverage Estimation for Irregular Targets in Wireless Visual Sensor Networks Deployed in Complex Region of Interest
Zhimin Liu, Guiyan Jiang, Youke Wu, Tian Wang, Shukun Liu, Zhangdong Ouyang
Abstract
In wireless visual sensor networks (WVSNs), coverage estimation is essential for optimizing network energy consumption and reducing deployment costs. In recent years, the majority of literature primarily focuses on small-scale networks, and typically assumes that visual sensors are either predetermined or randomly deployed in obstacle-free region of interest (RoI). However, in real-world scenarios, these sensors are frequently deployed in challenging-to-access environments that contain irregular obstacles. Moreover, the targets in these environments exhibit considerable diversity in shape and size. To address these challenges, this paper presents an issue of irregular targets K coverage with obstacles (K-ITCO) in WVSNs deployed in irregular RoI. In addition, we account for the probability that targets are unobstructed and consider the boundary effects. We derive two K-ITCO estimation models and their respective solving methods (K-ITCO-BE and K-ITCO-noBE) that predict a specified coverage ratio with a high probability. To validate the effectiveness and accuracy of our proposed models, multiple sets of simulations are designed to analyze the numerical and simulation results. Our findings demonstrate a high degree of consistency with the simulation outcomes, and indicate that the model can effectively estimate K-ITCO ratio.