| Wireless sensor networks are widely used i n environmental monitoring,smart home,military reconnaissance,medical care and other fields,but the energy shortage limits its development and application.With the gradual maturity of wir eless charging technology,using wireless mobile charger(WMC)t o supplement energy for sensor nodes is an effective method to solve the problem of energy shortage in sensor networks,which brings a new opportunity to prolong the lif etime of the network.At this time,how to reasonably schedule the charging trajectory of WMC to prolong the network lifetime more effectively is the research hotspot of sensor networks using wireless charging technology.Currently,researchers have propose d many charging schemes with different objectives to plan the charging path of WMC,bu t most of them did not consider some practical factors,such as unbalanced node energy consumption,limited WMC energy and ex pensive WMC cost.Consequently,these charging schemes will face more obstacles in practical application.In order to maximize the network lifetime,con sidering the above factors,this thesis proposes corresponding charging strategies for different WMC capacit ies,which improves the charging efficiency of WMC and effectively improves the network lifetime.The specific research content s of this thesis are as follows:Limited by space distance and battery capacity,some sensor nodes may have died due to insufficient residual energy before WMC arrives,resulting in reduced network lifetime.The energy consumption of the node mainly comes from data sensing,sending and receiving.When the node enters sleep mode(only sensing data),it can effectively prolong t he remaining lifetime of the n ode,so as to improve the overall performance of the network.Therefore,this thesis first proposes a novel node sleep strategy,that is,scheduling nodes with insufficient remaining lifetime to reach a certain threshold to ent er sleep,so as to reduce the related energy consumption.However,the sleep of nodes will have an impact on the connectivity of t he network.Hence,it is necessary to minimize the sleep time on the basis of ensuring the survival of nodes.To sum up,this thesis constructs an optimization problem with the goal of minimizing the sleep time and the variables of node sleep time and WMC charging path,and proposes an improved particle swarm optimization(NS-PSO)based on neighborhood search.By i ntroducing 2opt operator for neighborhood search,the algorithm can jump out of local optimization and impr ove the accuracy of the algorithm.Finally,the performance of NS-PSO algorithm is verified by the MATLAB in different network simulation scenarios.The simulation results show that the proposed algorithm can effectively prolong the ne twork lifetime while reducing the sleep time.Considering the limited energy of WMC and the energy consumption differ ence between nodes,in practical application,WMC with limited capacity may not be able to supplement the energy of a large number of nodes to be charged in one charging cycle,resulting in the decline of network performance.At this time,if the WMC supplements partial required energy for the nodes,it can ensure the survival of more nodes in one charging cycle and improve the network lifeti me.Therefore,this thesis first proposes a new ‘partial charging’ strategy,where WMC could charge more nodes under energy constraints.Then,the unified lifetime of nodes is defined as the consistent remaining lifetime of the nodes to be charged in the n etwork.Based on the proposed strategy,an optimization problem is formulated with the goal of maximizing the unified lifetime of nodes and the variables of node charging capacity and WMC charging path.Then,an improved ant colony algorithm(F-ACO)based on fuzzy evaluation i s proposed.Fuzzy eva luation is introduced when the basic ant colony algorithm updates the pheromone,so that a better convergence and higher solution accuracy could be achieved.Finally,MATLAB is used to verify the performance of proposed algorithms in different network scenarios.The results show that the proposed algorithm can effectively prolong the network lifetime. |
