基于无人机的边缘智能计算研究综述

doi:10.11959/j.issn.2096-6652.202025

Abstract

Abstract:

Edge intelligent computing refers to the offloading of computationally intensive tasks generated by user nodes to edge servers with stronger computing capabilities for processing.Unmanned aerial vehicle (UAV)-based edge intelligent computing combines intelligent drone platforms on this basis and utilizes them with the advantages of strong mobility and easy deployment,it can provide edge computing services for ground user equipment more quickly and flexibly.At the same time,drones can also be used as user nodes to off load their computationally intensive tasks to the ground edge server for execution.Aiming at two different scenarios of UAV as a user node or an edge server,the current research on edge intelligent computing based on UAV is classified and summarized according to different optimization goals such as minimizing energy consumption,minimizing delay and maximizing utility,and the next research direction is considered and prospected.

Key words: edge intelligent computing, unmanned aerial vehicle, task off loading

CLC Number:

TP391

Chao DONG,Yun SHEN,Yuben QU. A survey of UAV-based edge intelligent computing[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 227-239.

Figures/Tables 5

References 71

[1]	YANG Y , KARIMADINI M , XIANG C ,et al. Wide area surveillance of urban environments using multiple mini-VTOL UAVs[C]// IECON 2015 41st Annual Conference of the IEEE Industrial Electronics Society. Piscataway:IEEE Press, 2015: 795-800.
[2]	XIAO P , WANG L , CHUAN J ,et al. Implementation for UAVs aided edge sensing system in wireless emergency communications[C]// 2019 11th International Conference on Wireless Communications and Signal Processing. Piscataway:IEEE Press, 2019: 1-5.
[3]	KATSIGIANNIS P , MISOPOLINOS L , LIAKOPOULOS V ,et al. An autonomous multi-sensor UAV system for reduced-input precision agriculture applications[C]// 2016 24th Mediterranean Conference on Control and Automation.[S.l.:s.n]. 2016: 60-64.
[4]	ZENG Y , ZHANG R , LIM T.J . Wireless communications with unmanned aerial vehicles:opportunities and challenges[J]. IEEE Communications Magazine, 2016,54(5): 36-42.
[5]	LIU Y , ZHU C , DENG X ,et al. UAV-aided urban target tracking system based on edge computing[J]. arXiv preprint, 2020,arXiv:1902.00837.
[6]	CAO X , XU J , ZHANG R . Mobile edge computing for cellular-connected UAV:computation offloading and trajectory optimization[C]// 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications. Piscataway:IEEE Press, 2018: 1-5.
[7]	WEI L , HU R Q Y , QIAN Y ,et al. Enable device-to-device communications underlaying cellular networks:challenges and research aspects[J]. IEEE Communications Magazine, 2014,52(6): 90-96.
[8]	HU R Q Y , QIAN Y . An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems[J]. IEEE Communications Magazine, 2014,52(5): 94-101.
[9]	WANG L , WANG K , PAN C ,et al. Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing[J]. arXiv preprint, 2019,arXiv:1911.03887.
[10]	ZHANG Z , NJILLA L , YU S ,et al. edge-assisted learning for real-time UAV imagery via predictive offloading[C]// 2019 IEEE Global Communications Conference.[S.l.:s.n]. 2019: 1-6.
[11]	PENG K , LEUNG V , XU X ,et al. A survey on mobile edge computing:focusing on service adoption and provision[J]. Wireless Communications and Mobile Computing, 2018
[12]	WANG S , XU J , ZHANG N ,et al. A survey on service migration in mobile edge computing[J]. IEEE Access, 2018,6: 23511-23528.
[13]	MACH P , BECVAR Z . Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Communications Surveys ＆Tutorials, 2017,19(3): 1628-1656.
[14]	MAO Y , YOU C , ZHANG J ,et al. A survey on mobile edge computing:the communication perspective[J]. IEEE Communications Surveys ＆ Tutorials, 2017,19(4): 2322-2358.
[15]	WANG S , ZHANG X , ZHANG Y ,et al. A survey on mobile edge networks:convergence of computing,caching and communications[J]. IEEE Access, 2017,5: 2169-3536.
[16]	ABBAS N , ZHANG Y , TAHERKORDI A ,et al. Mobile edge computing:a survey[J]. IEEE Internet of Things Journal, 2017,5(1): 450-465.
[17]	董思岐, 李海龙, 屈毓锛 ,等. 移动边缘计算中的计算卸载策略研究综述[J]. 计算机科学, 2019,46(11): 32-40.
	DONG S Q , LI H L , QU Y B ,et al. Survey of research on computation unloading strategy in mobile edge computing[J]. Computer Science, 2019,46(11): 32-40.
[18]	CHEN W , LIU B , HUANG H ,et al. When UAV swarm meets edge-cloud computing:the QoS perspective[J]. IEEE Network, 2019,33(2): 36-43.
[19]	KALATZIS N , AVGERIS M , DECHOUNIOTIS D ,et al. Edge computing in IoT ecosystems for UAV-enabled early fire detection[C]// 2018 IEEE International Conference on Smart Computing. Piscataway:IEEE Press, 2018: 106-114.
[20]	CHENG N , XU W , SHI W ,et al. Air-ground integrated mobile edge networks:architecture,challenges,and opportunities[J]. IEEE Communications Magazine, 2018,56(8): 26-32.
[21]	ZHOU Y , PAN C , YEOH P L ,et al. Secure communications for UAV-enabled mobile edge computing systems[J]. IEEE Transactions on Communications, 2020,68(1): 376-388.
[22]	YANG L , YAO H , WANG J ,et al. Multi-UAV enabled load-balance mobile edge computing for IoT networks[J]. IEEE Internet of Things Journal, 2020(99):1.
[23]	HUA M , HUANG Y , WANG Y ,et al. Energy optimization for cellular-connected UAV mobile edge computing systems[C]// 2018 IEEE International Conference on Communication Systems. Piscataway:IEEE Press, 2018: 1-6.
[24]	HUA M , HUANG Y , WANG Y ,et al. Energy optimization for cellular-connected multi-UAV mobile edge computing systems with multi-access schemes[J]. Journal of Communications and Information Networks, 2018,3(4): 33-44.
[25]	ZHU S , GUI L , CHEN J ,et al. Cooperative computation offloading for UAVs:a joint radio and computing resource allocation approach[C]// 2018 IEEE International Conference on Edge Computing. Piscataway:IEEE Press, 2018: 74-79.
[26]	ZHANG Q , CHEN J , JI L ,et al. Response delay optimization in mobile edge computing enabled UAV swarm[J]. IEEE Transactions on Vehicular Technology, 2020,69(3): 3280-3295.
[27]	MESSOUS M , SEDJELMACI H , HOUARI N ,et al. Computation offloading game for an UAV network in mobile edge computing[C]// 2017 IEEE International Conference on Communications. Piscataway:IEEE Press, 2017: 1-6.
[28]	彭维平, 王明坤, 宋成 ,等. 多无人机协同直播场景下自适应任务卸载决策研究[J]. 控制与决策, 2019
	PENG W P , WANG M K , SONG C ,et al. Research on adaptive task offloading decision of multi-UAVs cooperation in live broadcasting scenario[J]. Control and Decision, 2019
[29]	KIM K , HONG C S . Optimal task-UAV-edge matching for computation offloading in UAV assisted mobile edge computing[C]// 2019 20th Asia-Pacific Network Operations and Management Symposium. Piscataway:IEEE Press, 2019: 1-4.
[30]	KIM K , PARK Y M , HONG S C . Machine learning based edge-assisted UAV computation offloading for data analyzing[C]// 2020 International Conference on Information Networking. Piscataway:IEEE Press, 2020: 117-120.
[31]	JEONG S , SIMEONE O , KANG J . Mobile edge computing via a UAV-mounted cloudlet:optimization of bit allocation and path planning[J]. IEEE Transactions on Vehicular Technology, 2018,67(3): 2049-2063.
[32]	GUO H , LIU J . UAV-enhanced intelligent offloading for internet of things at the edge[J]. IEEE Transactions on Industrial Informatics, 2020,16(4): 2737-2746.
[33]	DIAO X , ZHENG J , WU Y ,et al. Joint trajectory design,task data,and computing resource allocations for NOMA-based and UAV-assisted mobile edge computing[J]. IEEE Access, 2019,7: 117448-117459.
[34]	DIAO X , ZHENG J , CAI Y ,et al. Fair data allocation and trajectory optimization for UAV-assisted mobile edge computing[J]. IEEE Communications Letters, 2019,23(12): 2357-2361.
[35]	XIONG J , GUO H , LIU J . Task offloading in UAV-aided edge computing:bit allocation and trajectory optimization[J]. IEEE Communications Letters, 2019,23(3): 538-541.
[36]	HUA M , WANG Y , LI C ,et al. UAV-aided mobile edge computing systems with one by one access scheme[J]. IEEE Transactions on Green Communications and Networking, 2019,3(3): 664-678.
[37]	HU J , JIANG M , ZHANG Q ,et al. Joint optimization of UAV position,time slot allocation,and computation task partition in multiuser aerial mobile-edge computing systems[J]. IEEE Transactions on Vehicular Technology, 2019,68(7): 7231-7235.
[38]	HUA M , WANG Y , ZHANG Z ,et al. Optimal Resource Partitioning and Bit Allocation for UAV-enabled mobile edge computing[C]// 2018 IEEE 88th Vehicular Technology Conference. Piscataway:IEEE Press, 2018: 1-6.
[39]	WANG L , HUANG P , WANG K ,et al. RL-based user association and resource allocation for multi-UAV enabled MEC[C]// 2019 15th International Wireless Communications ＆ Mobile Computing Conference. Piscataway:IEEE Press, 2019: 741-746.
[40]	ZHOU F , WU Y , SUN H ,et al. UAV-enabled mobile edge computing:offloading optimization and trajectory design[C]// 2018 IEEE International Conference on Communications. Piscataway:IEEE Press, 2018: 1-6.
[41]	MEI H , YANG K , LIU Q ,et al. Joint trajectory-resource optimization in UAV-enabled edge-cloud system with virtualized mobile clone[J]. IEEE Internet of Things Journal, 2019(99):1.
[42]	WU G , MIAO T , ZHANG Y ,et al. Energy efficient for UAV-enabled mobile edge computing networks:intelligent task prediction and offloading[J]. Computer Communications, 2019,150: 556-562.
[43]	ALSENWI M , TUN Y K , PANDEY S R ,et al. UAV-assisted multi-access edge computing system:an energy-efficient resource management framework[C]// 2020 International Conference on Information Networking. Piscataway:IEEE Press, 2020: 214-219.
[44]	ZHANG J , ZHOU L , TANG Q ,et al. Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing[J]. IEEE Internet of Things Journal, 2019,6(2): 3688-3699.
[45]	XIONG J , GUO H , LIU J ,et al. Collaborative computation offloading at UAV-enhanced edge[C]// 2019 IEEE Global Communications Conference. Piscataway:IEEE Press, 2019: 1-6.
[46]	WANG Y , RU Z , WANG K ,et al. Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing[J]. IEEE Transactions on Cybernetics, 2019(99): 1-14.
[47]	HU X , WONG K , YANG K ,et al. UAV-assisted relaying and edge computing:scheduling and trajectory optimization[J]. IEEE Transactions on Wireless Communications, 2019,18(10): 4738-4752.
[48]	HU X , WONG K , YANG K ,et al. Task and bandwidth allocation for UAV-assisted mobile edge computing with trajectory design[C]// 2019 IEEE Global Communications Conference. Piscataway:IEEE Press, 2019: 1-6.
[49]	ZHANG T , XU Y , LOO J ,et al. Joint computation and communication design for UAV-assisted mobile edge computing in IoT[J]. IEEE Transactions on Industrial Informatics, 2019,16(8): 5505-5516.
[50]	YANG Z , PAN C , WANG K ,et al. Energy efficient resource allocation in UAV-enabled mobile edge computing networks[J]. IEEE Transactions on Wireless Communications, 2019,18(9): 4576-4589.
[51]	ZHANG X , ZHANG J , XIONG J ,et al. Energy-efficient multi-UAV-enabled multi-access edge computing incorporating NOMA[J]. IEEE Internet of Things Journal, 2020,7(6): 5613-5627.
[52]	CHEN B , ZHOU H , YAO J ,et al. RESERVE:an energy-efficient edge cloud architecture for intelligent multi-UAV[J]. IEEE Transactions on Services Computing, 2019(99):1.
[53]	HU Q , CAI Y , YU G ,et al. Joint offloading and trajectory design for UAV-enabled mobile edge computing systems[J]. IEEE Internet of Things Journal, 2019,6(2): 1879-1892.
[54]	YU Y , BU X , YANG K ,et al. UAV-aided low latency mobile edge computing with mmWave backhaul[C]// 2019 IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-7.
[55]	LIU Y , QIU M , HU J ,et al. Incentive UAV-enabled mobile edge computing based on microwave power transmission[J]. IEEE Access, 2020,8: 28584-28593.
[56]	QIAN Y , WANG F , LI J ,et al. User association and path planning for UAV-aided mobile edge computing with energy restriction[J]. IEEE Wireless Communications Letters, 2019,8(5): 1312-1315.
[57]	HU X , WONG K , ZHENG Z . Wireless-powered mobile edge computing with cooperated UAV[C]// 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications. Piscataway:IEEE Press, 2019: 1-5.
[58]	ZHANG L , ZHAO Z , WU Q ,et al. Energy-aware dynamic resource allocation in UAV assisted mobile edge computing over social internet of vehicles[J]. IEEE Access, 2018,6: 56700-56715.
[59]	ZHOU F , WU Y , HU R Q ,et al. Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2018,36(9): 1927-1941.
[60]	ZHOU Y , PAN C , YEOH P L ,et al. Secure Communications for UAV-enabled mobile edge computing systems[J]. IEEE Transactions on Communications, 2020,68(1): 376-388.
[61]	CHENG F , ZHANG S , CHEN Y ,et al. UAV trajectory optimization for data offloading at the edge of multiple cells[J]. IEEE Transactions on Vehicular Technology, 2018,67(7): 6732-6736.
[62]	LI J , LIU Q , WU P ,et al. Task Offloading for UAV-based mobile edge computing via deep reinforcement learning[C]// 2018 IEEE/CIC International Conference on Communications in China. Piscataway:IEEE Press, 2018: 798-802.
[63]	WANG J , LIU K , PAN J . Online UAV-mounted edge server dispatching for mobile-to-mobile edge computing[J]. IEEE Internet of Things Journal, 2020,7(2): 1375-1386.
[64]	YU Z , GONG Y , GONG S ,et al. Joint task offloading and resource allocation in UAV-enabled mobile edge computing[J]. IEEE Internet of Things Journal, 2020,7(4): 3147-3159.
[65]	WANG R , CAO Y , NOOR A ,et al. Agent-enabled task offloading in UAV-aided mobile edge computing[J]. Computer Communications, 2020,149: 324-331.
[66]	LIU Q , SHI L , SUN L . Path planning for UAV-mounted mobile edge computing with deep reinforcement learning[J]. arXiv preprint, 2020,arXiv:2001.10268.
[67]	ZHANG X , ZHONG Y , LIU P ,et al. Resource allocation for a UAV-enabled mobile-edge computing system:computation efficiency maximization[J]. IEEE Access, 2019,7: 113345-113354.
[68]	ZHANG J , ZHOU L , ZHOU F ,et al. Computation-efficient offloading and trajectory scheduling for multi-UAV assisted mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2019,(69)2: 2114-2125.
[69]	LIU B , HUANG H , GUO S ,et al. Joint computation offloading and routing optimization for UAV-edge-cloud computing environments[C]// 2018 IEEE SmartWorld,Ubiquitous Intelligence ＆ Computing,Advanced ＆ Trusted Computing,Scalable Computing ＆Communications,Cloud ＆ Big Data Computing,Internet of People and Smart City Innovation. Piscataway:IEEE Press, 2018: 1745-1752.
[70]	LIU J , LI L , YANG F ,et al. Minimization of offloading delay for two-tier UAV with mobile edge computing[C]// 2019 15th International Wireless Communications ＆ Mobile Computing Conference. Piscataway:IEEE Press, 2019: 1534-1538.
[71]	WAN S , LU J , FAN P . Towards big data processing in IoT--path planning and resource management of UAV base stations in mobile-edge computing system[J]. arXiv preprint, 2019,arXiv:1906.05023.10268.

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优化目标	参考文献序号	特点	适用对象
最小化无人机能耗	[23][24]	在满足时延条件的约束下，最小化无人机的能耗	能量消耗型任务
最小化时延	[6][25][26]	最小化完成协同计算任务过程中产生的时延	时延敏感型任务
最大化效用	[27][28][29][30]	综合考虑无人机能耗和任务时延，根据不同任务的具体需求，赋予能耗和时延不同的权值，通过优化最小化能耗和时延的加权和，实现效用最大化	综合考虑能耗和时延影响的任务

涉及方法	单无人机	多无人机
非智能方法	参考文献[6][23][25]	参考文献[24][26][27][28][29]
智能方法	无	参考文献[30]

优化目标	参考文献序号	特点	适用对象
最小化用户设备能耗	[9][31][32][33][34][35][36][37][38][39]	在满足时延条件的约束下，最小化用户设备的能耗	能量消耗型任务
最小化无人机能耗	[40][41][42]	在满足时延条件的约束下，最小化无人机的能耗	能量消耗型任务
最小化用户设备和无人机加权总能耗	[43][44][45][46][47][48][49][50][51][52]	在满足时延条件的约束下，最小化用户设备和无人机的加权总能耗	能量消耗型任务
最小化时延	[53][54]	最小化完成协同计算任务过程中产生的时延	时延敏感型任务
最大化任务量	[55][56][57][58][59][60][61][62][63]	最大化所有用户的任务卸载量或任务处理量（包括卸载量和本地计算量）	任务密集型任务
最大化效用	[64][65][66][67][68][69][70][71]	综合考虑无人机或设备的能耗、任务量和时延等各方面的因素，根据不同任务的具体需求制定不同的效用表达式，通过优化实现收益最大化	综合考虑能耗、任务量和时延等影响的任务

涉及方法	单无人机	多无人机
非智能方法	参考文献[31][32][33][34][35][36][37][38][40][41][43][44][45][47][48][49][51][53][54][55][56][57][58][59][60][61][64][67][68][69]	参考文献[46][50][52][63][70]
智能方法	参考文献[42][62][65][66]	参考文献[9][39][71]

A survey of UAV-based edge intelligent computing

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