典型文献
An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection
文献摘要：
With the developing demands of massive-data services,the applications that rely on big geographic data play crucial roles in academic and industrial communities.Unmanned aerial vehicles(UAVs),combining with terrestrial wireless sensor networks(WSN),can provide sustainable solutions for data harvesting.The rising demands for efficient data collection in a larger open area have been posed in the literature,which requires efficient UAV trajectory planning with lower energy consumption methods.Currently,there are amounts of inextricable solutions of UAV planning for a larger open area,and one of the most practical techniques in previous studies is deep reinforcement learning(DRL).However,the overestimated problem in limited-experience DRL quickly throws the UAV path planning process into a locally optimized condition.Moreover,using the central nodes of the sub-WSNs as the sink nodes or navigation points for UAVs to visit may lead to extra collection costs.This paper develops a data-driven DRL-based game framework with two partners to fulfill the above demands.A cluster head processor(CHP)is employed to determine the sink nodes,and a navigation order processor(NOP)is established to plan the path.CHP and NOP receive information from each other and provide optimized solutions after the Nash equilibrium.The numerical results show that the proposed game framework could offer UAVs low-cost data collection trajectories,which can save at least 17.58％of energy consumption compared with the baseline methods.
文献关键词：
中图分类号：
作者姓名：
Tong Ding;Ning Liu;Zhong-Min Yan;Lei Liu;Li-Zhen Cui
作者机构：
School of Software,Shandong University,Jinan 250101,China
文献出处：
引用格式：
[1]Tong Ding;Ning Liu;Zhong-Min Yan;Lei Liu;Li-Zhen Cui-.An Efficient Reinforcement Learning Game Framework for UAV-Enabled Wireless Sensor Network Data Collection)[J].计算机科学技术学报（英文版）,2022(06):1356-1368
A类：
inextricable
B类：
An,Efficient,Reinforcement,Learning,Game,Framework,Enabled,Wireless,Sensor,Network,Data,Collection,With,developing,demands,massive,data,services,applications,that,rely,big,geographic,play,crucial,roles,academic,industrial,communities,Unmanned,aerial,vehicles,UAVs,combining,terrestrial,wireless,sensor,networks,can,provide,sustainable,solutions,harvesting,rising,efficient,collection,larger,open,area,have,been,literature,which,requires,trajectory,planning,lower,energy,consumption,methods,Currently,there,amounts,one,most,practical,techniques,previous,studies,deep,reinforcement,learning,DRL,However,overestimated,problem,limited,experience,quickly,throws,path,into,locally,optimized,condition,Moreover,using,central,nodes,sub,WSNs,sink,navigation,points,visit,may,lead,extra,costs,This,paper,develops,driven,game,framework,partners,fulfill,above,cluster,head,processor,CHP,employed,determine,order,NOP,established,receive,information,from,each,other,after,Nash,equilibrium,numerical,results,show,proposed,could,offer,trajectories,save,least,compared,baseline
AB值：
0.630971
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
