Research On Energy-efficient Optimization Strategies Of Rechargeable Wireless Sensor Network

In recent years,as a technological achievement in the combination of sensors and wireless networks,wireless sensor networks(WSN)have been widely used in various fields.WSN are of high efficiency of data acquisition and great flexibility.However,energy supply has been the bottleneck of the development of this technology.The wireless rechargeable sensor network(WRSN)is an evolving technology to solve this problem.In the meanwhile,the emerging technology in radio frequency,UAVs,and edge cloud computing provides WRSN with new opportunities.This thesis aims at solving problems about long charging cycle and poor practicability of mobile charging carrier in WRSN according to energy consumption and supply in WRSN and focuses on how to integrate simultaneously wireless information and energy transfer(SWIPT),offloading balance and unmanned mobile charging technology to solve one of the core problems in WRSN,that is,the optimization of overall energy efficiency.The topics studied in this thesis are of theoretical and practical value.The main contributions include the following three aspects:1.This thesis applies SWIPT into WRSN.With the help of directional antennas,array antennas,and beam-forming,energy contained in wireless data signals can be effectively collected and utilized.This thesis adopts the optimization analysis method and considers three basic adjustable parameters including data transmission rate among nodes,transmit power and the splitting ratio of received energy to optimize the energy efficiency of the overall WRSN,and also solve energy distribution balance problem in WRSNs.The convergence and feasibility of the proposed algorithm has been verified through simulation.In addition,this thesis also analyzes the impact of network capacity and signal-to-noise ratio on efficiency and lay a theoretical basis for the construction of a new energy-efficient WRSN.2.This thesis studies the feasibility of improving the data processing capability of the WRSN by offloading tasks between the data center and the ordinary data gathering node.At the same time,balancing the energy consumption among nodes by task offloading is also studied.This thesis first uses game theory to model the offloading demand of each node in the network and proposes a multi-node offloading game problem(MUOG).Then,it proposes an offloading game mechanism(OGM),including the beneficial offloading threshold(BOT)algorithm for each node and the beneficial offloading group(BOG)algorithm.To be specific,the BOT algorithm can obtain the optimal threshold for task offloading at each node.OGM can locally evaluate the maximum number of nodes that they can tolerate to share offloading together while meeting their own needs.And,the BOG algorithm can find out the nodes to participate in offloading.It can be proved that OGM can achieve the Nash equilibrium of the MUOG problem,thereby obtaining the maximum number of beneficial offloading node sets.Simulation results verify energy consumption of the beneficial offloading group under different strategies.Compared with other offloading strategies,the results show that OGM can save energy through offloading without increasing overhead,thereby can benefit more nodes.3.This thesis researches on the performance improvement in WRSN and charging strategies with the introduction of mobile charging by UAVs and regarding super capacitors as charging buffers.Based on the characteristics of the power distribution and physical locations of nodes in WRSN,a node weighting mechanism is formulated.After collecting the dynamical charging requirements of each node,the optimized charging path planning is used to complete a charging task under the condition that the cost is close to the minimum.The weighted charging path planning considers the fairness and timeliness,and the implementation of the charging task guarantees the integrity of topology and the data integrity in WRSNs.Finally,simulation results verify that the proposed algorithm in this thesis has significantly improved the performance of mobile charging technology in WRSN.