A Distributed Barrier Coverage Mechanism for Supporting Full View in Wireless Visual Sensor Networks

Abstract

Barrier coverage is one of the most important issues in intruder detection applications. Visual sensors can give more accurate information of intruder but also raise the problem of large data volume for information exchange and processing. Constructing a disjoint full-view barrier using minimal number of visual sensors has been the main goal for handling the barrier coverage problem. This paper presents a decentralized FBCA mechanism which consists of Region Partitioning Phase, Grid Excluding Phase, Grid Verification Phase and Full-View Barrier Construction Phase. In Region Partitioning Phase, the given R is partitioned into a set of equal-sized grids, aiming to simplify the construction problem of the full-view barrier. In Grid Excluding Phase, a certain amount of grids is removed, aiming to reduce the computational complexity. In Grid Verification Phase, each visual sensor aims to check if its neighboring grids satisfy the full-view coverage criteria. Finally, the Full-View Barrier Construction Phase aims to construct as many as possible full-view barriers. Experimental study shows that the proposed FBCA outperforms existing work SP, and likely approaches to the optimal solution (MCSPS) in terms of the average success ratio for constructing a full-view barrier.

Coverage Enhance in Boundary Deployed Camera Sensor Networks for Airport Surface Surveillance

“…Many studies proposed different approaches to discover the barrier gaps and then mend them. Xu et al propose a decentralized mechanism to construct a disjoint full-view barrier by using visual sensors as few as possible [18]. In their method, the region is partitioned into a number of grids, which can reduce the computation cost and communication cost.…”

Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless Sensor Networks

“…However, if regular cameras are employed on an outdoor scenario, they may become useless during the night and any employed optimization approach might not accomplish the expected results. In a different scenario but with similar outcome, visual sensors may be occluded by obstacles, which will reduce their effectively viewed areas [20]. Therefore, any performed optimization or quality assessment approach in wireless visual sensor networks should only consider visual sensors that are not under any coverage failure, at the risk of having their results compromised.…”