Research On Charging Station Scheduling For UAV Sensing Networks

In recent years,Unmanned Aerial Vehicle(UAV)sensing networks have been widely applied in fields such as agriculture,meteorology and transportation.Due to the limitation of battery capacity,the energy replenishment scheduling of UAVs has attracted more and more attention.Compared with traditional sensors,UAVs not only require energy forsensing and communication but also additional energy to maintain takeoff,landing,flight,and hovering.Therefore,short battery life has become a bottleneck problem for the widespread applications of UAV sensing networks,and an effective way to extend battery life is through charging scheduling of UAVs.However,the existing scheduling schemes only focus on improving service quality or reducing charging costs,and there are few researches that comprehensively consider the scheduling for both aspects.In the UAV sensing network,if only the sensing value of the UAV is considered,it may result in high charging expenses,on the other hand,if only the charging cost is considered,it may lead to low sensing value.Therefore,an efficient charging scheduling scheme is the key to extending the availability of UAVs.This thesis first introduces the concept of sensing utility from the perspectives of sensing value and charging cost.Then,a utility-driven charging model for sustainable UAV sensing networks is designed,which can maximize the total sensing utility of the network under a given sensing period.This thesis first proves that the problem has the characteristic of optimal substructure,that is,the optimal solution of the original problem contains the optimal solution of its subproblem.Then,the original problem is decomposed into smaller subproblems,and the state transition equation is proposed.An optional scheduling algorithm based on dynamic programming is purposed.The results of simulations demonstrate that the proposed optimal scheduling algorithm under limited sensing period can increase at most 16.59% total sensing utility comparing with the benchmark algorithms.In addition,considering that the energy consumption rate of UAVs is time-varying and unpredictable under non-ideal conditions,a charging scheduling long-term stochastic optimization model for sustainable UAV sensing networks is designed,which can maximize the time-averaged sensing value with the constraint of time-averaged charging cost.The Lyapunov optimization technique is used to decompose the long-term stochastic optimization problem into short-term optimization subproblems in each time slot.This thesis proves that the subproblem in each time slot is equivalent to the general assignment problem(GAP),and then solve the subproblem through a 2-approximation algorithm.Finally,the effectiveness of the proposed algorithm is verified through theoretical analysis and simulation experiments.The solution proposed in this thesis is superior to other feasible solutions and can increase at most 19.64% total sensing value comparing with the benchmark algorithms.