Research On Multiple Vehicles Charging Scheduling Based On Game Theory

Though wireless sensor networks have broad application prospects,they still suffer from energy constraints,resulting in limited lifetime of sensors.To solve this problem,researchers have proposed the concept of wireless rechargeable sensor networks.Using wireless charging vehicles or robots to wirelessly charge sensors can effectively prolong the lifetime of sensors.In this paper,the multiple mobile chargers charging scheduling problem in large scale network is investigated,so that the energy usage efficiency is maximized.Moreover,the energy consumption for traveling of all mobile chargers is the least.We first study the problem that multiple mobile chargers periodically visit sensors to charge them,such that all sensors do not run out of energy.To solve this problem,we regard the charging behavior as a trading activity.When mobile chargers replenish energy for sensors,they will ask for some money to compensate for the movement loss.Thus,sensors bear the cost of mobile chargers.In order to reduce the cost,sensors will cooperative with each other.Then,we convert the multiple mobile charger scheduling problem into cooperative game among sensors and solve it by searching the optimal coalition structure.In order to reduce searching space for the optimal coalition structure,we restrict the coalition size and remove the infeasible coalition.Besides,an adaptively coalition structure updating algorithm is proposed to adapt to the changing network topology.We investigate collaborative charging to extend the networks coverage.In collaborative charging scheme,the chargers can not only charge sensors,but also charge other chargers.We model the multiple chargers collaborative charging scheduling problem as a game played among chargers.Then,by carefully designing payoff function and rules,the charging behavior is effectively restricted.Furthermore,our scheme achieves the optimal result of social welfare.We conduct simulations to evaluate the performance of our scheme.The simulation results show that our scheme is able to achieve less traveling energy consumption comparing to other multiple chargers charging scheduling algorithms.Besides,we conduct simulation to evaluate the performance of collaborative charging algorithm.The results show that our scheme has obvious advantages in energy usage efficiency and the number of dead sensors.