Obstacle-Aware Wireless Video Sensor Network Deployment for 3D Indoor Monitoring

Abstract

Wireless Video Sensor Networks (WVSNs) have been widely proposed for monitoring spatial environments. Dissimilar from traditional Wireless Sensor Networks (WSNs) where nodes such as thermal and light sensors are often considered in 2D and with omni-directional sensing ranges, the sensing range of a video sensor in WVSNs is deemed as directional and usually cognized as a rectangular pyramid shape in 3D environments. Moreover, within indoor spaces, obstacles such as ceiling lights and furniture can easily block the line-of-sight of a video sensor. These new challenges render the traditional deployment solutions for WSNs or 2D modeling environments as impractical to solve the WVSN deployment problem for 3D indoor monitoring. In this paper, we strive to tackle the WVSN deployment problem for 3D indoor monitoring with the consideration of ensuring coverage, connectivity and obstacle awareness. We first model the general problem in a continuous 3D space to minimize the total number of required video sensors. We then convert it into a discrete version problem by incorporating 3D grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. We develop a series of mechanisms to handle the obstacles in the 3D environment and propose an efficient greedy heuristic algorithm yielding high quality solutions. Based on this, we also propose an enhanced Depth First Search (DFS) algorithm that can not only further improve the solution quality, but also return optimal solutions if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced DFS solutions.