Toward Optimal Deployment And Orientation Scheduling For Full-view Coverage In Camera Sensor Networks

Recently,camera sensor networks(CSNs)have been increasingly employed in many applica-tion domains to provide security surveillance,which is the so-called coverage problem.A point(or an intruder)is said to be covered if it is within the sensor's sensing region in traditional CSNs.Due to the growing awareness of the security in both public places and private properties,new demands for capturing clear profile of intruders have risen beyond traditional coverage of simply detecting intruders.Therefore,full-view coverage has been introduced recently to capture intruders from multiple directions in CSNs.It is more efficient than traditional coverage in identifying intruders.However,full-view coverage typically calls for a large number of camera sensors,on the other hand,camera sensors need to work collaboratively to full-view cover the target.Then how to make full use of camera sensors with less cost to improve the full-view coverage in CSNs is an urgent problem.Based on the motivations,my research work mainly focuses on the following aspects:deployment for full-view area coverage and orientation scheduling for full-view point coverage in CSNs.Firstly,we study a problem on how to select a subset of candidate locations to deploy camera sensors over,and choosing an orientation direction for each camera sensor,to guarantee the full-view coverage of a given region.We first transfer the full-view area problem into the full-view point problem by the intrinsic geometric relationship between them,which leads to a significant dimension reduction.Next,we prove that the minimum number full-view point coverage is NP-hard and design Iterative-Screening algorithms with performance guarantee to solve the problem.Based on insight into the aforementioned deployment problem,it is unavoidable that full-view coverage in static CSNs calls for a large number of camera sensors.Clearly,it's costly especially for the scenarios where the number of target points is large.To tackle this problem,we first exploit limited mobility of orientation to improve the full-view coverage in CSNs since camera sensors usually can rotate to cover more areas.Observing that target points may not be full-view covered all the time due to the sensor rotation,we focus on the fairness-based coverage maximization problem,i.e.,how to schedule the orientations of camera sensors to maximize the minimum accumulated full-view coverage time of target points.To solve this issue,we first try to reduce the dimension space of orientations by dividing the orientation space into a set of discrete directions.We then study how to select the minimum number of sensing regions that camera sensors should rotate to cover in order to ensure the full-view coverage of all target points.Next,we unveil the relationship between full-view coverage and target points,which are spatially correlated.Based on these results,we devise a centralized algorithm to solve the problem based on "largest demand first serve" principle,by which target points with less accumulated full-view coverage time will be preferentially selected to be full-view covered with a higher probability.We further design a distributed counterpart to solve the problem,by which each camera sensor could choose the orientation independently with communicating with only a few camera sensors.We provide extensive simulation results to demonstrate the desired performance of the proposed algorithms.