Research On Performance Optimization Of Wireless Sensor Networks Based On Mobile Charging Device With Limited Energy

As an important part of the Internet of things,the Wireless Sensor Network(WSN)plays a very important role in the development of human society.However,its development is limited by the limited energy.Wireless rechargeable sensor networks(WRSNs)use Mobile Charging Device(MCD)to replenish energy for sensor nodes,eliminates the energy constraint caused by battery power supply mode,and provides an effective solution to solve the energy bottleneck of the WSN.At present,the existing charging scheduling algorithms have achieved some achievements.However,these studies often ignore some realistic factors,such as the limited energy of the MCD,the complex terrain environment of actual deployment,etc.This neglect makes the scheme unable to achieve the desired effect in practice.In addition,the charging scheduling in these studies did not consider network performance requirements,such as network energy consumption,network connectivity and coverage,and service quality of heterogeneous networks.To maximize the network lifetime and improve energy efficiency,in different network scenarios,considering the above factors,this paper designs the corresponding optimization algorithm and scheduling scheme.The specific research content of this paper is summarized as follows:Firstly,the lifetime of the WSN is not only related to energy supply,but also closely related to node energy consumption.Aiming at the phenomenon that there are a large number of redundant sensor nodes in the network,by analyzing the relationship between network connectivity coverage and sensor node energy consumption,this paper proposes a joint strategy of energy management and efficient charging scheduling based on energy-limited mobile charging devices to maximize the lifetime of the network.Through the analysis of the network model and performance,it is proved that finding the minimum sensor nodes and the minimum energy requirement to meet the basic coverage and connectivity requirements of the network is a key step to maximize the network lifetime.Under the constraint of maintaining coverage and connectivity,an algorithm based on particle swarm optimization(PSO)is designed to find the minimum node set,and the minimum energy requirement is calculated.On this basis,according to the relationship between the node energy demand and the MCD battery capacity,under the premise of maximizing the network lifetime,the charging scheduling algorithms with different objectives are designed.When the supply is less than the demand,the charging demand of sensor nodes within the node set with minimum energy demand is given priority,and partial charging is adopted to extend the network lifetime as far as possible.When the supply exceeds the demand,the energy of MCDS is sufficient,and the sensor nodes within the node set with minimum energy demand are charged first to ensure the normal operation of the network.Then,the remaining energy and location of sensor nodes are comprehensively considered to charge the nodes outside the set to maximize the energy efficiency.The comparison results show that the proposed algorithm can effectively prolong the network lifetime and improve the charging efficiency.Secondly,in the face of complex application fields and diversified Quality of Service(Qo S)requirements,only one type of mobile device and a single data transmission mode cannot meet the charging request of sensor nodes and Qo S requirements of applications.In order to improve energy efficiency and Qo S of the network,a new strategy for joint data collection and charging scheduling in rechargeable sensor networks is proposed.Aiming at the complex environment of the actual deployment of sensor networks,a joint charging scheduling method of Moving Vehicle(MV)and Unmanned Aerial Vehicle(UAV)is designed to improve the charging efficiency of the network by using the characteristics of MV and UAV.In addition,according to the different Qo S of services in the network,MV and UAV relay and multihop transmission are adopted.While planning the charging path,the data transmission strategy is designed to prolong the network lifetime and guarantee the Qo S of heterogeneous services.As the differences in moving speed,charging rate and the amount of responsible data,there is some spare time of the MV or the UAV a service cycle.To address this,a novel data collection and relay strategy is proposed.Under this strategy,considering various kinds of traffic requirements and limitations of sensor nodes,we propose a jointly scheduling of moving vehicles(MVs)and unmanned aerial vehicles(UAVs)to adapt to the complex environment and improve network performance.Through planning charging path and designing data transmission policy,the proposed algorithm prolongs the network lifetime while guarantees the Qo S of heterogeneous traffics.As the differences in moving speed,charging rate and the amount of responsible data,there is some spare time of the MV or the UAV in a cycle and a global charging.A collecting algorithm is proposed to further charge and collect data from the cluster out of the original path.Finally,the results verify that the proposed algorithm can effectively reduce the dead time of the network and improve the energy efficiency simultaneously.Further utilizing this idle time,a global charging and data collection algorithm is proposed to charge and collect data from sensor nodes outside the original path and further improve the network performance.Finally,experimental results show that the proposed algorithm can effectively reduce the network death time and improve the energy efficiency.