| The ever-growing Wireless Sensor Networks(WSNs)provides a powerful means for complex and changeable environmental monitoring.Traditionally,WSNs are composed of omni-directional sensors,which,however,are still limited to unadjustable sensing angle and superfluous energy consumption.Fortunately,these limitations can be overcome by deploying directional sensors in WSNs,thus forming Directional Sensor Networks(DSNs).As a typical architecture of WSNs,DSNs can efficiently facilitate various digital and intelligent WSNs applications.In the DSNs,the network performance such as coverage,connectivity,energy efficiency,and lifetime are directly determined by the deployment schemes of the sensors.Unreasonable sensor deployment schemes may lead to a large number of coverage holes,thus reducing the utilization efficiency of the sensors and the monitoring performance of the networks.Most of the existing coverage optimization schemes in DSNs are based on Voronoi and virtual force,which are greatly affected by the network topology.However,existing coverage optimization schemes based on the Minimum Exposure Path(MEP)usually deploy additional sensors directly along the MEP,the frequent replacement or addition of new sensors to the networks may cause unnecessary waste of node resources,energy consumption,and coverage redundancy.In addition,the existing schemes do not consider the limited initial energy of sensors and moving energy consumption during the coverage optimization process.Therefore,how to achieve coverage optimization by adjusting the deployment of a limited number of sensors with limited initial energy becomes a research hotspot in both industry and academia.In response to the above challenges,this thesis proposes a novel sensor redeployment scheme based on the Minimum Exposure Path(MEP)to achieve coverage optimization in DSNs,to overcome the problems such as coverage redundancy and waste of sensor resources caused by additional sensor deployment,as well as new coverage holes caused by a large number of sensor movements.Specifically,we first propose a MEP-PSO algorithm with the target of establishing the MEP.With this algorithm,the traditional MEP problem can be analyzed and simplified by conducting grid discretization and building weighted undirected graph.Then,a MEP-based coverage optimization algorithm is proposed to determine the optimal deployment locations and the dispatch sensors so that the sensors can be dynamically redeployed to achieve the coverage optimization.After that,we derive the formula for the coverage upper bound and develop a coverage upper bound algorithm to provide a benchmark for evaluating the effectiveness of different coverage optimization algorithms.Simulation results demonstrate that the proposed coverage optimization algorithm can significantly promote the Minimum Exposure Value(MEV)and coverage ratio of the monitoring area compared with the existing algorithms.Furthermore,this thesis considers the limited energy of directional sensors and the energy consumption of sensor movements,as well as the actual scenario,a coverage optimization strategy based on Obstacle Avoidance Minimum Exposure Path(OAMEP)was proposed.Firstly,the OAMEP algorithm is proposed,in which the finite difference method and genetic algorithm are used to simplify and solve OAMEP problems.Then,a coverage optimization algorithm based on the OAMEP is proposed to adjust the positions of the deployed sensors considering the energy consumption of sensor movements.The simulation results show that with the increase of optimization times,the proposed scheme can effectively avoid the obstacle area while significantly improving the coverage performance,which is conducive to the comprehensive information collection of deployment area. |
