| As a new type of sensor network,mobile sensor networks(MSN)can realize rapid topology change in practical application.Because of its high mobility,MSN has been widely used on many occasions,such as modern battlefields,disaster relief,medical detection and so on.When the sensor network is deployed randomly,the nodes will be unevenly distributed,resulting in coverage gaps in the target area.In this case,it is necessary to develop a movement strategy for nodes to complete the effective coverage of the target region.This paper focuses on how to maximize the coverage of target area in mobile sensor networks considering energy consumption.After the initial random deployment of nodes,there will be coverage gaps in the target area,so it is necessary to move the mobile sensor nodes reasonably to ensure the quality of service of the sensor network.Most of the current research on the optimization of MSN target area coverage is based on the ideal Boolean disk model and some derived or improved models,These methods do not fully consider the spatial correlation of environmental variables in the sensing field,and do not consider the balance between the coverage of target area and energy consumption,so it will lead to the waste of sensor network resources.Based on the confident information coverage(CIC)model,this paper proposes an energy consumption and coverage equalization algorithm,and uses the game theory method to model the target area coverage optimization problem into a state-based potential game.The whole cyberspace is divided into several unit grids,each grid gives a constant coverage value,and the target area represents the grid with high coverage value.In the potential game,a reasonable utility function is designed for each sensor node,and the binary log-linear reinforcement learning method is used to solve the potential game.In this paper,a series of simulations are carried out on the energy consumption and coverage equalization algorithm,the energy consumption and coverage equalization algorithm under the disk model and the random algorithm.The experiment results show that the performance of this algorithm is better.When the algorithm converges,the global coverage value is improved compared with the comparison algorithm,and when the energy consumption is the same,the global coverage value obtained by this algorithm converges is much higher,not only can it satisfy the requirements of coverage,but also reduces the energy consumption. |
