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Obstacle and Connectivity Aware Wireless Video Sensor Deployment for 3D Indoor Monitoring: Poster 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 omnidirectional 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 poster, 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 and enhanced DFS algorithms yielding high quality solutions.