Research On On-Demand Charging And Data Collection Strategy For Minimizing Network Energy Consumption In WSN

Limited energy of wireless sensor nodes has always been one of the important reasons restricting the development of wireless sensor networks(WSN).The development of wireless charging technology makes the energy supplement of wireless sensor networks have a new way.By formulating a reasonable wireless charging strategy to charge the sensor nodes in WSN,the network lifetime of WSN can be greatly extended.However,because the task of sensor nodes in WSN is to collect and transmit data,only charging wireless sensor nodes does not reduce the energy consumption in WSN,it can only ensure that the energy consumed during the operation of sensor nodes can be supplemented,so how to formulate reasonable The wireless charging strategy of WSN reduces the network energy consumption of WSN as much as possible under the premise of ensuring the normal operation of sensor nodes,and has been a research hotspot of wireless sensor networks.Therefore,this thesis plans the energy supplement and data collection in WSN at the same time to achieve the goal of minimizing the energy consumption in the network while ensuring the normal operation of the network.In this thesis,the data collected by the sensor node is not directly transmitted back to the base station,but stored in the sensor memory and waiting for the mobile device to collect,which can greatly reduce the energy consumption caused by the data in the long-distance transmission process.Therefore,this thesis completes the tasks of energy supplement and data collection for sensor nodes in WSN by scheduling the mobile car(MC)loaded with wireless charging and data collection modules.This thesis first studies how to schedule a single MC to recharge and collect data for a part of demand nodes with insufficient residual energy or insufficient storage space in a two-dimensional plane scenario.When the remaining energy of the sensor node or the remaining storage space reaches the lower threshold,the sensor node sends information to the base station.After receiving the information,the base station informs all nodes to return the remaining energy and storage space information,and selects the remaining working time to be less than or equal to the remaining working time threshold the node with the demand threshold is regarded as the demand node.The base station calculates the MC moving path through the DEFW algorithm proposed in this thesis,and then MC starts from the base station to perform energy supplement and data collection operations for demand nodes.Experimental results show that using DEFW algorithm for MC scheduling has low energy consumption.Based on the energy supplement and data collection of a single MC for WSN,this thesis analyzes the insufficient charging and data collection capabilities of a single MC in a large-scale network,and then carries out a large-scale WSN energy supplement and data collection strategy research,using many MC performs energy supplement and data collection for large-scale WSN.On the scheduling problem of multi-MC,this thesis designs a large-scale network firework differential scheduling algorithm(MDEFW).First,K-means-based clustering method is used to determine the number of MCs and the required responsibility based on the remaining working time of nodes and the distance between nodes Number of nodes.Then the moving path of each MC is solved according to DEFW algorithm.Unlike the DEFW algorithm,which only solves a single MC moving path,MDEFW can determine the node to be visited by each MC,the dwell time of the MC at the base station,and the access path of each MC,thus achieving comprehensive scheduling.Experimental results show that the MDEFW algorithm can use lower power consumption to ensure the normal operation of the network.