Research On Energy Starvation Avoidance Strategy Of Sensor Network Based On Minimizing Charging Cost

A series of strategies of efficiently using energy have been designed in traditional Wireless Sensor Networks(WSNs).These strategies aim at reducing energy consumption of sensor node for the purpose of solving the “Energy Starvation” problem.“Energy Starvation” was caused by unbalanced energy consumption and battery capacity shortage.Although this problem has been alleviated somehow,the utilization rate for network energy is kind of low and unable to keep networks running for a long time.In the field of sensor network study,it has been a hot issue on how to use an energy-efficient solution to send collected information to the base station in a short time.In recent years,the development of the wireless energy transfer technology and the high energy density lithium battery technology have alleviated the bottleneck for Wireless Rechargeable Sensor Network(WRSNs)energy,which has attracted widespread attention from scholars.This thesis proposes a method for dividing virtual data collection points based on different network shapes so as to improve the usability of WRSNs,balance energy consumption of all of the network nodes and prolong lifetime of networks.This method defines several Virtual Traverse Points(VTPs)which are uniformly distributed in the network.During each round of data collection,a Data Gathering Vehicle(DGV)only needs to move to each VTP for data collection.It is not necessary to select a “cluster” in the network,nor is it necessary for DGV to cover each node.Meanwhile,this thesis proposes a periodic data collection scheme.This scheme uses a method of combining distributed multi-hop data uploading and regional mobile data collection,which realizes data collection in a highly efficient way.Afterwards,this thesis describes a method for sleeping scheduling in nodes under deterministic deployment,based on which a “two-stage” charging method of combining “periodic” and “responsive” is proposed.Simulation results show that compared with existing algorithms,this method performs well and has certain scalability in terms of different charging power,Wireless Charging Vehicle(WCV)energy consumption per unit distance and node energy consumption rate.