Applied Research On Coverage Algorithm For Mobile Directional Sensor Networks

Wireless sensor networks are composed of a set of sensor nodes, with limited capacity of computation and communication. These sensor nodes are interconnected through wireless networks. Sensor nodes cooperatively sense, collect and process the information of target over a physical area which is to be monitored. To detect intruders who penetrate the regions of surveillance, we need to deploy a set of sensor nodes that can provide coverage of the regions of surveillance, a problem that is often refereed to as barrier coverage. Directional sensors are becoming more advantages than omni-directional scalar sensors for the extra dimensional information they provide. As sensors are randomly deployed, barrier gaps may occur due to the limitation of number and sensing angle of nodes. With recent wireless sensor networks technical advances, practical mobile sensors have been developed, which provides us a way to improve barrier coverage performance after sensor networks have been deployed.In this thesis, we study how to efficiently achieve barrier coverage in hybrid directional sensor networks by moving mobile sensors to fill in gaps and form a barrier with stationary sensors. In particular, the research content about barrier coverage includes two aspects. In the first aspect: after stationary sensors are deployed, their barrier gaps are identified. In this part, the strong-connected-cluster identification algorithm has been proposed to identify the strong-connected-cluster formed by stationary sensor nodes. The algorithm uses boundary intersecting to judge if sensing regions are overlapped or not. In the second aspect: we calculated the number of needed mobile sensors, and the corresponding quantity of mobile sensors are deployed and move to desired locations to fill in these gap to form barrier. In this part, we formulated the problem of minimizing total cost and max-lifetime problem. The first problem can be solved by Hungarian algorithm and the second problem can be solved by Backtracking algorithm. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithm.