Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks
Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-con...
Saved in:
| Published in | Computer communications Vol. 192; pp. 234 - 244 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.08.2022
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0140-3664 1873-703X |
| DOI | 10.1016/j.comcom.2022.06.017 |
Cover
| Abstract | Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-connectivity. To this end, a joint task and energy offloading problem is studied under a UAV-aided and energy-constrained intelligent edge network, consisting of a high altitude platform (HAP), multiple UAVs, and on-ground fog computing nodes (FCNs). To guarantee the energy supply of UAVs and FCNs, both simultaneous wireless information and power transfer (SWIPT), as well as laser charging techniques are considered. Specifically, we investigate a scenario where each UAV needs to execute a computation-intensive task during each time slot and can be powered by the laser beam transmitted from the HAP. Due to the limited computation resources, each UAV can offload part of the task and energy to the FCNs for collaborative computing, to reduce local energy consumption and the overall task execution delay by adopting SWIPT. Considering the dynamics of the network, e.g., the time-varying locations of UAVs and available computation resources of FCNs, the problem is formulated as a cooperative multi-agent Markov game for UAVs, which aims to maximize the total system utility, by optimizing the task partitioning and power allocation strategies of each UAV, regarding task size, average delay and energy consumption of task execution. To tackle this problem, we propose a multi-agent soft actor–critic (MASAC)-based approach to resolve the problem. Numerical simulation results prove the superiority of our proposed approach as compared with benchmark methods. |
|---|---|
| AbstractList | Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-connectivity. To this end, a joint task and energy offloading problem is studied under a UAV-aided and energy-constrained intelligent edge network, consisting of a high altitude platform (HAP), multiple UAVs, and on-ground fog computing nodes (FCNs). To guarantee the energy supply of UAVs and FCNs, both simultaneous wireless information and power transfer (SWIPT), as well as laser charging techniques are considered. Specifically, we investigate a scenario where each UAV needs to execute a computation-intensive task during each time slot and can be powered by the laser beam transmitted from the HAP. Due to the limited computation resources, each UAV can offload part of the task and energy to the FCNs for collaborative computing, to reduce local energy consumption and the overall task execution delay by adopting SWIPT. Considering the dynamics of the network, e.g., the time-varying locations of UAVs and available computation resources of FCNs, the problem is formulated as a cooperative multi-agent Markov game for UAVs, which aims to maximize the total system utility, by optimizing the task partitioning and power allocation strategies of each UAV, regarding task size, average delay and energy consumption of task execution. To tackle this problem, we propose a multi-agent soft actor–critic (MASAC)-based approach to resolve the problem. Numerical simulation results prove the superiority of our proposed approach as compared with benchmark methods. |
| Author | Chen, Ning Huang, Lianfen Liwang, Minghui Guizani, Mohsen Du, Xiaojiang Cheng, Zhipeng |
| Author_xml | – sequence: 1 givenname: Zhipeng surname: Cheng fullname: Cheng, Zhipeng organization: Xiamen University, Xiamen 361005, China – sequence: 2 givenname: Minghui surname: Liwang fullname: Liwang, Minghui email: minghuilw@xmu.edu.cn organization: Xiamen University, Xiamen 361005, China – sequence: 3 givenname: Ning surname: Chen fullname: Chen, Ning organization: Xiamen University, Xiamen 361005, China – sequence: 4 givenname: Lianfen surname: Huang fullname: Huang, Lianfen organization: Xiamen University, Xiamen 361005, China – sequence: 5 givenname: Xiaojiang surname: Du fullname: Du, Xiaojiang organization: Stevens Institute of Technology, Hoboken, NJ, 07030, USA – sequence: 6 givenname: Mohsen surname: Guizani fullname: Guizani, Mohsen organization: Department of Computer Science and Engineering, Qatar University, Qatar |
| BookMark | eNqFkM1KQzEQhYMoWH_ewEVe4F6Tm9ykdSEUf6pQcGPFXUiTSUl7m5QkKL69KXXlQmFgmOF8B845Q8chBkDoipKWEiqu162J2zptR7quJaIlVB6hER1L1kjC3o_RiFBOGiYEP0VnOa8JIVxKNkLDPcAOJ_DBxWRgC6HgAXQKPqyapc5g8Tr6-iw6b7AOFkOAtPrC0bkhaltl2Ae8mL412tuqFrN6FxgGv9p7gV0BDlA-Y9rkC3Ti9JDh8mefo8Xjw-vdUzN_mT3fTeeNYUSUZjnurRAMJrabWM6Npt0SzMRRBq7vBdOcU2sl44SDc44bEExKKvueLGtkYOfo5uBrUsw5gVPGF118DCVpPyhK1L43tVaH3tS-N0WEqr1VmP-Cd8lvdfr6D7s9YFCDfXhIKhsPwYD1CUxRNvq_Db4BrxSNhQ |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3419904 crossref_primary_10_3390_electronics12122599 crossref_primary_10_3390_s23136095 crossref_primary_10_1109_TNSE_2024_3371434 crossref_primary_10_1016_j_iot_2024_101281 crossref_primary_10_1109_TMC_2024_3394568 crossref_primary_10_1016_j_adhoc_2023_103371 crossref_primary_10_1016_j_comnet_2024_110387 crossref_primary_10_1145_3712599 crossref_primary_10_3390_math11102376 crossref_primary_10_1016_j_compeleceng_2023_108581 crossref_primary_10_1109_TMC_2024_3384405 crossref_primary_10_1016_j_comnet_2024_110801 crossref_primary_10_1016_j_vehcom_2025_100900 crossref_primary_10_3390_rs15020429 crossref_primary_10_1109_ACCESS_2023_3241881 crossref_primary_10_3390_drones7040236 crossref_primary_10_1002_cav_2182 crossref_primary_10_1109_JIOT_2024_3441236 crossref_primary_10_3390_computers13030064 crossref_primary_10_1016_j_cosrev_2024_100656 crossref_primary_10_1016_j_comcom_2023_05_013 crossref_primary_10_1109_ACCESS_2023_3236801 crossref_primary_10_3390_su15097315 crossref_primary_10_1109_COMST_2023_3323344 crossref_primary_10_1016_j_comnet_2024_110575 crossref_primary_10_1145_3626566 |
| Cites_doi | 10.1016/j.adhoc.2004.03.004 10.1109/COMST.2015.2495297 10.1109/JIOT.2018.2880812 10.1109/TVT.2021.3074304 10.1109/TNSE.2021.3130251 10.1109/TGCN.2019.2926131 10.1109/JIOT.2019.2958975 10.1109/JIOT.2020.2964951 10.1109/TVT.2020.3014788 10.1109/TWC.2020.2964765 10.1109/LCOMM.2019.2947039 10.1109/TVT.2018.2890685 10.1109/TWC.2018.2794345 10.1109/ICCW.2018.8403572 10.1109/JIOT.2020.2968951 10.1109/COMST.2017.2745201 10.1109/TWC.2020.2970920 10.1109/TWC.2019.2935201 10.1109/JPROC.2011.2159690 10.1109/TVT.2020.3028011 10.1109/GLOBECOM42002.2020.9322269 10.1109/JPROC.2019.2918951 10.1109/JIOT.2020.3003398 10.1109/COMST.2017.2783901 10.1109/JSAC.2018.2864426 10.1109/TWC.2020.3019097 10.1109/MCOM.2008.4427231 10.1109/TNSE.2021.3066598 10.1109/JIOT.2020.3021006 10.1109/JIOT.2018.2851070 10.1109/TIE.2010.2046002 10.1109/TWC.2013.031813.120224 10.1109/MNET.011.2000651 10.1109/JSAC.2020.3018804 10.1109/COMST.2014.2368999 10.1109/GCWkshps50303.2020.9367493 10.1109/TGCN.2022.3157735 10.1109/ACCESS.2020.3036416 10.1109/TWC.2020.2971987 10.1109/TMC.2019.2928811 10.1109/JIOT.2021.3091508 10.1109/MNET.010.2100025 10.1002/wcm.344 10.1109/TCOMM.2020.2995373 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier B.V. |
| Copyright_xml | – notice: 2022 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.comcom.2022.06.017 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-703X |
| EndPage | 244 |
| ExternalDocumentID | 10_1016_j_comcom_2022_06_017 S0140366422002195 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 77K 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABFNM ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG9 M41 MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ RXW SDF SDG SDP SES SPC SPCBC SST SSV SSZ T5K WH7 ZMT ~G- 07C 29F AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ABXDB ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AFXIZ AGCQF AGQPQ AGRNS AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN BNPGV CITATION EJD F0J FEDTE FGOYB HLZ HVGLF HZ~ R2- RIG SBC SEW SSH TAE UHS VH1 VOH WUQ XPP ZY4 |
| ID | FETCH-LOGICAL-c306t-b85d663e9d29d44ca12bec9f13ef5563a441dd73404efff4ce637717550b873e3 |
| IEDL.DBID | AIKHN |
| ISSN | 0140-3664 |
| IngestDate | Tue Jul 01 02:43:08 EDT 2025 Thu Apr 24 23:03:57 EDT 2025 Fri Feb 23 02:40:33 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | MASAC SWIPT UAV Multi-agent Markov game Task offloading Laser |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-b85d663e9d29d44ca12bec9f13ef5563a441dd73404efff4ce637717550b873e3 |
| PageCount | 11 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_comcom_2022_06_017 crossref_primary_10_1016_j_comcom_2022_06_017 elsevier_sciencedirect_doi_10_1016_j_comcom_2022_06_017 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-08-01 2022-08-00 |
| PublicationDateYYYYMMDD | 2022-08-01 |
| PublicationDate_xml | – month: 08 year: 2022 text: 2022-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Computer communications |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Mao, You, Zhang, Huang, Letaief (b4) 2017; 19 Gupta, Jain, Vaszkun (b9) 2016; 18 Min, Xiao, Chen, Cheng, Wu, Zhuang (b26) 2019; 68 Senadhira, Durrani, Zhou, Yang, Ding (b10) 2020; 68 Yin, Zhao, Li, Yu (b40) 2019; 3 Lahmeri, Kishk, Alouini (b21) 2020; 24 Lu, Gu, Luo, Ding, Zheng, Shen (b42) 2020; 8 Hu, Wong, Zhang (b35) 2020; 19 Zhou, Wu, Hu, Qian (b32) 2018; 36 Sample, Meyer, Smith (b11) 2011; 58 Zhang, Fang, Liu, Wu, Xia, Yang (b20) 2018; 5 Lu, Wang, Niyato, Kim, Han (b14) 2015; 17 Zhao, Shi, Zhao (b22) 2020; 19 Y. Wang, L. Zhang, M. Min, C. Guo, V. Sharma, Z. Han, Privacy-aware wireless power transfer for aerial computation offloading via colonel blotto game, in: Proc. 2020 IEEE Globecom Workshops, 2020, pp. 1–6. Cheng, Min, Liwang, Huang, Gao (b13) 2022; 9 Zhang, Mou, Gao, Jiang, Ding, Han (b44) 2020; 69 Sacco, Esposito, Marchetto, Montuschi (b12) 2021; 70 Ponnimbaduge Perera, Jayakody, Sharma, Chatzinotas, Li (b17) 2018; 20 Dong (b6) 2021; 35 H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, T. Yuan, Edge intelligence empowered UAVs for automated wind farm monitoring in smart grids, in: Proc. 2020 IEEE Globecom, 2020, pp. 1–6. Du (b3) 2004; 2 Wang, Xu, Cui (b15) 2020; 19 Hu, Wong, Yang (b29) 2018; 17 Haarnoja, Zhou, Hartikainen, Tucker, Ha, Tan, Kumar, Zhu, Gupta, Abbeel (b41) 2018 Cheng, Gao, Liwang, Huang, Du, Guizani (b23) 2021; 35 K. Xiong, et al., Joint Optimization of Trajectory, Task Offloading and CPU Control in UAV-assisted Wireless Powered Fog Computing Networks, IEEE Trans. Green Commun. Zhang, Ho (b16) 2013; 12 Zhang, Hanzo (b39) 2020; 69 Cao, Zhou, Li, Huang, Wu (b45) 2020; 7 Mohammed, Mehmood, Pavlidou, Mohorcic (b47) 2011; 99 Cai, Yang, Wang, Wu, Yang, Luo (b31) 2020; 7 . Xiao, Leung, Pan, Du (b2) 2005; 5 Liu, Zhao, Qin, Geng, Meng (b37) 2022; 9 Jiang, Ma, Liu, Hu, Chen, Humar (b36) 2022; 9 Khairy, Balaprakash, Cai, Cheng (b8) 2021; 39 Liu, Nie, Li, Ahmed, Lim, Miao (b38) 2021; 8 Huang, Bi, Zhang (b30) 2020; 19 Zhou, Chen, Li, Zeng, Luo, Zhang (b5) 2019; 107 Zhou, Hu (b27) 2020; 19 Cui, Liu, Nallanathan (b46) 2020; 19 T. Haarnoja, et al. Soft actor-critic: off policy maximum entropy deep reinforcement learning with a stochastic actor, in Proc. ICML, 2018, 80 pp. 1861–1870. Liu, Xiong, Ni, Fan, Letaief (b33) 2020; 7 J. Ouyang, Y. Che, J. Xu, K. Wu, Throughput maximization for laser-powered UAV wireless communication systems, in: Proc. 2018 ICC Workshops, 2018, pp. 1–6. Yang, Ou, Chen (b1) 2008; 46 R. Lowe, Y. Wu, A. Tamar, J. Harb, O.P. Abbeel, I. Mordatch, Multi-agent actor-critic for mixed cooperative-competitive environments, in: Proc. 31st Int. Conf. Neural Inf. Process. Syst. 2017, pp. 6379–6390. Fu, Kang, Zhang, Yu, Wu (b43) 2021; 8 Ji, Guo (b28) 2019; 6 Xiao (10.1016/j.comcom.2022.06.017_b2) 2005; 5 Dong (10.1016/j.comcom.2022.06.017_b6) 2021; 35 Sample (10.1016/j.comcom.2022.06.017_b11) 2011; 58 Cao (10.1016/j.comcom.2022.06.017_b45) 2020; 7 Yang (10.1016/j.comcom.2022.06.017_b1) 2008; 46 Min (10.1016/j.comcom.2022.06.017_b26) 2019; 68 Zhou (10.1016/j.comcom.2022.06.017_b5) 2019; 107 Ji (10.1016/j.comcom.2022.06.017_b28) 2019; 6 Hu (10.1016/j.comcom.2022.06.017_b29) 2018; 17 Lu (10.1016/j.comcom.2022.06.017_b14) 2015; 17 Liu (10.1016/j.comcom.2022.06.017_b33) 2020; 7 10.1016/j.comcom.2022.06.017_b25 Mohammed (10.1016/j.comcom.2022.06.017_b47) 2011; 99 10.1016/j.comcom.2022.06.017_b24 Cui (10.1016/j.comcom.2022.06.017_b46) 2020; 19 Zhao (10.1016/j.comcom.2022.06.017_b22) 2020; 19 Mao (10.1016/j.comcom.2022.06.017_b4) 2017; 19 Lahmeri (10.1016/j.comcom.2022.06.017_b21) 2020; 24 Zhang (10.1016/j.comcom.2022.06.017_b20) 2018; 5 Cai (10.1016/j.comcom.2022.06.017_b31) 2020; 7 Zhang (10.1016/j.comcom.2022.06.017_b44) 2020; 69 Sacco (10.1016/j.comcom.2022.06.017_b12) 2021; 70 Zhou (10.1016/j.comcom.2022.06.017_b27) 2020; 19 Haarnoja (10.1016/j.comcom.2022.06.017_b41) 2018 Liu (10.1016/j.comcom.2022.06.017_b37) 2022; 9 Yin (10.1016/j.comcom.2022.06.017_b40) 2019; 3 Zhang (10.1016/j.comcom.2022.06.017_b16) 2013; 12 Ponnimbaduge Perera (10.1016/j.comcom.2022.06.017_b17) 2018; 20 Senadhira (10.1016/j.comcom.2022.06.017_b10) 2020; 68 Zhou (10.1016/j.comcom.2022.06.017_b32) 2018; 36 Du (10.1016/j.comcom.2022.06.017_b3) 2004; 2 Gupta (10.1016/j.comcom.2022.06.017_b9) 2016; 18 Cheng (10.1016/j.comcom.2022.06.017_b13) 2022; 9 10.1016/j.comcom.2022.06.017_b7 10.1016/j.comcom.2022.06.017_b34 Hu (10.1016/j.comcom.2022.06.017_b35) 2020; 19 Wang (10.1016/j.comcom.2022.06.017_b15) 2020; 19 Jiang (10.1016/j.comcom.2022.06.017_b36) 2022; 9 Liu (10.1016/j.comcom.2022.06.017_b38) 2021; 8 Huang (10.1016/j.comcom.2022.06.017_b30) 2020; 19 Zhang (10.1016/j.comcom.2022.06.017_b39) 2020; 69 Lu (10.1016/j.comcom.2022.06.017_b42) 2020; 8 Fu (10.1016/j.comcom.2022.06.017_b43) 2021; 8 Khairy (10.1016/j.comcom.2022.06.017_b8) 2021; 39 Cheng (10.1016/j.comcom.2022.06.017_b23) 2021; 35 10.1016/j.comcom.2022.06.017_b18 10.1016/j.comcom.2022.06.017_b19 |
| References_xml | – volume: 46 start-page: 56 year: 2008 end-page: 63 ident: b1 article-title: On effective offloading services for resource-constrained mobile devices running heavier mobile internet applications publication-title: IEEE Commun. Mag. – volume: 19 start-page: 3170 year: 2020 end-page: 3184 ident: b27 article-title: Computation efficiency maximization in wireless-powered mobile edge computing networks publication-title: IEEE Trans. Wireless Commun. – volume: 68 start-page: 1930 year: 2019 end-page: 1941 ident: b26 article-title: Learning-based computation offloading for IoT devices with energy harvesting publication-title: IEEE Trans. Veh. Technol. – volume: 12 start-page: 1989 year: 2013 end-page: 2001 ident: b16 article-title: MIMO broadcasting for simultaneous wireless information and power transfer publication-title: IEEE Trans. Wireless Commun. – volume: 69 start-page: 11599 year: 2020 end-page: 11611 ident: b44 article-title: Uav-enabled secure communications by multi-agent deep reinforcement learning publication-title: IEEE Trans. Veh. Technol. – volume: 99 start-page: 1939 year: 2011 end-page: 1953 ident: b47 article-title: The role of high-altitude platforms (HAPs) in the global wireless connectivity publication-title: Proc. IEEE – reference: Y. Wang, L. Zhang, M. Min, C. Guo, V. Sharma, Z. Han, Privacy-aware wireless power transfer for aerial computation offloading via colonel blotto game, in: Proc. 2020 IEEE Globecom Workshops, 2020, pp. 1–6. – volume: 36 start-page: 1927 year: 2018 end-page: 1941 ident: b32 article-title: Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems publication-title: IEEE J. Sel. Areas Commun. – year: 2018 ident: b41 article-title: Soft actor-critic algorithms and applications – volume: 39 start-page: 1101 year: 2021 end-page: 1115 ident: b8 article-title: Constrained deep reinforcement learning for energy sustainable multi-UAV based random access IoT networks with NOMA publication-title: IEEE J. Sel. Areas Commun. – volume: 68 start-page: 5242 year: 2020 end-page: 5258 ident: b10 article-title: Uplink NOMA for cellular-connected UAV: Impact of UAV trajectories and altitude publication-title: IEEE Trans. Commun. – volume: 9 start-page: 104 year: 2022 end-page: 116 ident: b13 article-title: Multiagent DDPG-based joint task partitioning and power control in fog computing networks publication-title: IEEE Internet Things J. – volume: 58 start-page: 544 year: 2011 end-page: 554 ident: b11 article-title: Analysis, experimental results, and range adaptation of magnetically coupled resonators for wireless power transfer publication-title: IEEE Trans. Ind. Electron. – volume: 19 start-page: 2322 year: 2017 end-page: 2358 ident: b4 article-title: A survey on mobile edge computing: The communication perspective publication-title: IEEE Commun. Surv. Tutorials – reference: K. Xiong, et al., Joint Optimization of Trajectory, Task Offloading and CPU Control in UAV-assisted Wireless Powered Fog Computing Networks, IEEE Trans. Green Commun. – volume: 70 start-page: 5003 year: 2021 end-page: 5015 ident: b12 article-title: Sustainable task offloading in UAV networks via multi-agent reinforcement learning publication-title: IEEE Trans. Veh. Technol. – reference: H.-M. Chung, S. Maharjan, Y. Zhang, F. Eliassen, T. Yuan, Edge intelligence empowered UAVs for automated wind farm monitoring in smart grids, in: Proc. 2020 IEEE Globecom, 2020, pp. 1–6. – volume: 8 start-page: 1308 year: 2021 end-page: 1321 ident: b43 article-title: Soft actor-critic DRL for live transcoding and streaming in vehicular fog computing-enabled IoV publication-title: IEEE Internet Things J. – volume: 5 start-page: 3853 year: 2018 end-page: 3864 ident: b20 article-title: Distributed laser charging: A wireless power transfer approach publication-title: IEEE Internet Things J. – volume: 7 start-page: 2777 year: 2020 end-page: 2790 ident: b33 article-title: Uav-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization publication-title: IEEE Internet Things J. – volume: 35 start-page: 42 year: 2021 end-page: 49 ident: b23 article-title: Intelligent task offloading and energy allocation in the UAV-aided mobile edge-cloud continuum publication-title: IEEE Netw. – volume: 17 start-page: 757 year: 2015 end-page: 789 ident: b14 article-title: Wireless networks with RF energy harvesting: A contemporary survey publication-title: IEEE Commun. Surv. Tutorials – volume: 9 start-page: 299 year: 2022 end-page: 309 ident: b36 article-title: MER-WearNet: Medical-emergency response wearable networking powered by UAV-assisted computing offloading and WPT publication-title: IEEE Trans. Network Sci. Eng. – volume: 19 start-page: 729 year: 2020 end-page: 743 ident: b46 article-title: Multi-agent reinforcement learning-based resource allocation for UAV networks publication-title: IEEE Trans. Wirel. Commun. – reference: R. Lowe, Y. Wu, A. Tamar, J. Harb, O.P. Abbeel, I. Mordatch, Multi-agent actor-critic for mixed cooperative-competitive environments, in: Proc. 31st Int. Conf. Neural Inf. Process. Syst. 2017, pp. 6379–6390. – volume: 69 start-page: 14104 year: 2020 end-page: 14109 ident: b39 article-title: Federated learning assisted multi-UAV networks publication-title: IEEE Trans. Veh. Technol. – volume: 20 start-page: 264 year: 2018 end-page: 302 ident: b17 article-title: Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges publication-title: IEEE Commun. Surv. Tutorials – volume: 2 start-page: 241 year: 2004 end-page: 254 ident: b3 article-title: Qos routing based on multi-class nodes for mobile ad hoc networks publication-title: Ad Hoc Netw. – volume: 19 start-page: 3257 year: 2020 end-page: 3272 ident: b22 article-title: Efficiency maximization for UAV-enabled mobile relaying systems with laser charging publication-title: IEEE Trans. Wireless Commun. – volume: 107 start-page: 1738 year: 2019 end-page: 1762 ident: b5 article-title: Edge intelligence: Paving the last mile of artificial intelligence with edge computing publication-title: Proc. IEEE – volume: 9 start-page: 660 year: 2022 end-page: 677 ident: b37 article-title: Joint dynamic task offloading and resource scheduling for WPT enabled space-air-ground power internet of things publication-title: IEEE Trans. Network Sci. Eng. – volume: 7 start-page: 6201 year: 2020 end-page: 6213 ident: b45 article-title: Multiagent deep reinforcement learning for joint multichannel access and task offloading of mobile-edge computing in industry 4.0 publication-title: IEEE Internet Things J. – reference: J. Ouyang, Y. Che, J. Xu, K. Wu, Throughput maximization for laser-powered UAV wireless communication systems, in: Proc. 2018 ICC Workshops, 2018, pp. 1–6. – volume: 19 start-page: 2443 year: 2020 end-page: 2459 ident: b15 article-title: Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems publication-title: IEEE Trans. Wireless Commun. – reference: . – volume: 8 start-page: 9827 year: 2021 end-page: 9837 ident: b38 article-title: Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms publication-title: IEEE Internet Things J. – volume: 6 start-page: 4744 year: 2019 end-page: 4754 ident: b28 article-title: Energy-efficient cooperative resource allocation in wireless powered mobile edge computing publication-title: IEEE Internet Things J. – volume: 17 start-page: 2375 year: 2018 end-page: 2388 ident: b29 article-title: Wireless powered cooperation-assisted mobile edge computing publication-title: IEEE Trans. Wireless Commun. – volume: 18 start-page: 1123 year: 2016 end-page: 1152 ident: b9 article-title: Survey of important issues in UAV communication networks publication-title: IEEE Commun. Surv. Tutorials – volume: 7 start-page: 3067 year: 2020 end-page: 3082 ident: b31 article-title: JOTE: Joint offloading of tasks and energy in fog-enabled IoT networks publication-title: IEEE Internet Things J. – volume: 24 start-page: 173 year: 2020 end-page: 177 ident: b21 article-title: Stochastic geometry-based analysis of airborne base stations with laser-powered UAVs publication-title: IEEE Commun. Lett. – reference: T. Haarnoja, et al. Soft actor-critic: off policy maximum entropy deep reinforcement learning with a stochastic actor, in Proc. ICML, 2018, 80 pp. 1861–1870. – volume: 19 start-page: 8083 year: 2020 end-page: 8098 ident: b35 article-title: Wireless-powered edge computing with cooperative UAV: Task, time scheduling and trajectory design publication-title: IEEE Trans. Wirel. Commun. – volume: 5 start-page: 805 year: 2005 end-page: 823 ident: b2 article-title: Architecture, mobility management and quality of service for integrated 3G and WLAN networks publication-title: Wirel. Commun. Mob. Comput. – volume: 19 start-page: 2581 year: 2020 end-page: 2593 ident: b30 article-title: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks publication-title: IEEE Trans. Mob. Comput. – volume: 8 year: 2020 ident: b42 article-title: Optimization of task offloading strategy for mobile edge computing based on multi-agent deep reinforcement learning publication-title: IEEE Access – volume: 3 start-page: 1044 year: 2019 end-page: 1057 ident: b40 article-title: UAV-assisted cooperative communications with power-splitting information and power transfer publication-title: IEEE Trans. Green Commun. Netw. – volume: 35 start-page: 167 year: 2021 end-page: 175 ident: b6 article-title: UAVs as an intelligent service: Boosting edge intelligence for air-ground integrated networks publication-title: IEEE Netw. – volume: 2 start-page: 241 issue: 3 year: 2004 ident: 10.1016/j.comcom.2022.06.017_b3 article-title: Qos routing based on multi-class nodes for mobile ad hoc networks publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2004.03.004 – ident: 10.1016/j.comcom.2022.06.017_b24 – volume: 18 start-page: 1123 issue: 2 year: 2016 ident: 10.1016/j.comcom.2022.06.017_b9 article-title: Survey of important issues in UAV communication networks publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2015.2495297 – volume: 6 start-page: 4744 issue: 3 year: 2019 ident: 10.1016/j.comcom.2022.06.017_b28 article-title: Energy-efficient cooperative resource allocation in wireless powered mobile edge computing publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2880812 – volume: 70 start-page: 5003 issue: 5 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b12 article-title: Sustainable task offloading in UAV networks via multi-agent reinforcement learning publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3074304 – volume: 9 start-page: 660 issue: 2 year: 2022 ident: 10.1016/j.comcom.2022.06.017_b37 article-title: Joint dynamic task offloading and resource scheduling for WPT enabled space-air-ground power internet of things publication-title: IEEE Trans. Network Sci. Eng. doi: 10.1109/TNSE.2021.3130251 – volume: 3 start-page: 1044 issue: 4 year: 2019 ident: 10.1016/j.comcom.2022.06.017_b40 article-title: UAV-assisted cooperative communications with power-splitting information and power transfer publication-title: IEEE Trans. Green Commun. Netw. doi: 10.1109/TGCN.2019.2926131 – volume: 7 start-page: 2777 issue: 4 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b33 article-title: Uav-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2958975 – volume: 7 start-page: 3067 issue: 4 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b31 article-title: JOTE: Joint offloading of tasks and energy in fog-enabled IoT networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2964951 – volume: 69 start-page: 11599 issue: 10 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b44 article-title: Uav-enabled secure communications by multi-agent deep reinforcement learning publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.3014788 – volume: 19 start-page: 2443 issue: 4 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b15 article-title: Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2020.2964765 – volume: 24 start-page: 173 issue: 1 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b21 article-title: Stochastic geometry-based analysis of airborne base stations with laser-powered UAVs publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2019.2947039 – volume: 68 start-page: 1930 issue: 2 year: 2019 ident: 10.1016/j.comcom.2022.06.017_b26 article-title: Learning-based computation offloading for IoT devices with energy harvesting publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2890685 – volume: 17 start-page: 2375 issue: 4 year: 2018 ident: 10.1016/j.comcom.2022.06.017_b29 article-title: Wireless powered cooperation-assisted mobile edge computing publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2018.2794345 – ident: 10.1016/j.comcom.2022.06.017_b19 doi: 10.1109/ICCW.2018.8403572 – volume: 7 start-page: 6201 issue: 7 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b45 article-title: Multiagent deep reinforcement learning for joint multichannel access and task offloading of mobile-edge computing in industry 4.0 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.2968951 – volume: 19 start-page: 2322 issue: 4 year: 2017 ident: 10.1016/j.comcom.2022.06.017_b4 article-title: A survey on mobile edge computing: The communication perspective publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2017.2745201 – volume: 19 start-page: 3170 issue: 5 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b27 article-title: Computation efficiency maximization in wireless-powered mobile edge computing networks publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2020.2970920 – volume: 19 start-page: 729 issue: 2 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b46 article-title: Multi-agent reinforcement learning-based resource allocation for UAV networks publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2019.2935201 – volume: 99 start-page: 1939 issue: 11 year: 2011 ident: 10.1016/j.comcom.2022.06.017_b47 article-title: The role of high-altitude platforms (HAPs) in the global wireless connectivity publication-title: Proc. IEEE doi: 10.1109/JPROC.2011.2159690 – volume: 69 start-page: 14104 issue: 11 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b39 article-title: Federated learning assisted multi-UAV networks publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.3028011 – ident: 10.1016/j.comcom.2022.06.017_b7 doi: 10.1109/GLOBECOM42002.2020.9322269 – volume: 107 start-page: 1738 issue: 8 year: 2019 ident: 10.1016/j.comcom.2022.06.017_b5 article-title: Edge intelligence: Paving the last mile of artificial intelligence with edge computing publication-title: Proc. IEEE doi: 10.1109/JPROC.2019.2918951 – volume: 8 start-page: 1308 issue: 3 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b43 article-title: Soft actor-critic DRL for live transcoding and streaming in vehicular fog computing-enabled IoV publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3003398 – volume: 20 start-page: 264 issue: 1 year: 2018 ident: 10.1016/j.comcom.2022.06.017_b17 article-title: Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2017.2783901 – year: 2018 ident: 10.1016/j.comcom.2022.06.017_b41 – volume: 36 start-page: 1927 issue: 9 year: 2018 ident: 10.1016/j.comcom.2022.06.017_b32 article-title: Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2018.2864426 – volume: 19 start-page: 8083 issue: 12 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b35 article-title: Wireless-powered edge computing with cooperative UAV: Task, time scheduling and trajectory design publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2020.3019097 – ident: 10.1016/j.comcom.2022.06.017_b25 – volume: 46 start-page: 56 issue: 1 year: 2008 ident: 10.1016/j.comcom.2022.06.017_b1 article-title: On effective offloading services for resource-constrained mobile devices running heavier mobile internet applications publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2008.4427231 – volume: 9 start-page: 299 issue: 1 year: 2022 ident: 10.1016/j.comcom.2022.06.017_b36 article-title: MER-WearNet: Medical-emergency response wearable networking powered by UAV-assisted computing offloading and WPT publication-title: IEEE Trans. Network Sci. Eng. doi: 10.1109/TNSE.2021.3066598 – volume: 8 start-page: 9827 issue: 12 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b38 article-title: Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3021006 – volume: 5 start-page: 3853 issue: 5 year: 2018 ident: 10.1016/j.comcom.2022.06.017_b20 article-title: Distributed laser charging: A wireless power transfer approach publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2851070 – volume: 58 start-page: 544 issue: 2 year: 2011 ident: 10.1016/j.comcom.2022.06.017_b11 article-title: Analysis, experimental results, and range adaptation of magnetically coupled resonators for wireless power transfer publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2010.2046002 – volume: 12 start-page: 1989 issue: 5 year: 2013 ident: 10.1016/j.comcom.2022.06.017_b16 article-title: MIMO broadcasting for simultaneous wireless information and power transfer publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2013.031813.120224 – volume: 35 start-page: 167 issue: 4 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b6 article-title: UAVs as an intelligent service: Boosting edge intelligence for air-ground integrated networks publication-title: IEEE Netw. doi: 10.1109/MNET.011.2000651 – volume: 39 start-page: 1101 issue: 4 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b8 article-title: Constrained deep reinforcement learning for energy sustainable multi-UAV based random access IoT networks with NOMA publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2020.3018804 – volume: 17 start-page: 757 issue: 2 year: 2015 ident: 10.1016/j.comcom.2022.06.017_b14 article-title: Wireless networks with RF energy harvesting: A contemporary survey publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2014.2368999 – ident: 10.1016/j.comcom.2022.06.017_b18 doi: 10.1109/GCWkshps50303.2020.9367493 – ident: 10.1016/j.comcom.2022.06.017_b34 doi: 10.1109/TGCN.2022.3157735 – volume: 8 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b42 article-title: Optimization of task offloading strategy for mobile edge computing based on multi-agent deep reinforcement learning publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3036416 – volume: 19 start-page: 3257 issue: 5 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b22 article-title: Efficiency maximization for UAV-enabled mobile relaying systems with laser charging publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2020.2971987 – volume: 19 start-page: 2581 issue: 11 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b30 article-title: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2019.2928811 – volume: 9 start-page: 104 issue: 1 year: 2022 ident: 10.1016/j.comcom.2022.06.017_b13 article-title: Multiagent DDPG-based joint task partitioning and power control in fog computing networks publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3091508 – volume: 35 start-page: 42 issue: 5 year: 2021 ident: 10.1016/j.comcom.2022.06.017_b23 article-title: Intelligent task offloading and energy allocation in the UAV-aided mobile edge-cloud continuum publication-title: IEEE Netw. doi: 10.1109/MNET.010.2100025 – volume: 5 start-page: 805 issue: 7 year: 2005 ident: 10.1016/j.comcom.2022.06.017_b2 article-title: Architecture, mobility management and quality of service for integrated 3G and WLAN networks publication-title: Wirel. Commun. Mob. Comput. doi: 10.1002/wcm.344 – volume: 68 start-page: 5242 issue: 8 year: 2020 ident: 10.1016/j.comcom.2022.06.017_b10 article-title: Uplink NOMA for cellular-connected UAV: Impact of UAV trajectories and altitude publication-title: IEEE Trans. Commun. doi: 10.1109/TCOMM.2020.2995373 |
| SSID | ssj0004773 |
| Score | 2.511566 |
| Snippet | Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 234 |
| SubjectTerms | Laser MASAC Multi-agent Markov game SWIPT Task offloading UAV |
| Title | Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks |
| URI | https://dx.doi.org/10.1016/j.comcom.2022.06.017 |
| Volume | 192 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED71scCAeIryqDywmjZxEidjVSgFRCeKukV-BaVUSdWGld-O7SSlSAgkxlg5Jzpb97C_-w7gSirqMm7QU-Zu3RCSYybCPo4CwZ0-EwHl5mjgaRKMp97DzJ81YFjXwhhYZWX7S5turXU10qu02Vumac_Ckkig42eLM4j8JrRdoj_Wgvbg_nE8-SqPpOVFs0EyGoG6gs7CvPT0Bjbial9miTxt57IfPNSW1xntw14VLqJB-UcH0FDZIexukQgeweJGqSVaKUuBKuxpH6p6Qbxi46QkmuepHizY-g2xTCJl6_1QniSL3ELoUZqh6eAFG7pIiYI7lG6IOgtkDtxQVqLF18cwHd0-D8e46qGAhU4GCsxDX-qgQkXSjaTnCea4etWixCEqMdxgTIdDUlLi9T2VJIknVECoTvF04sJDShQ5gVaWZ-oUUN9nfsQ97dCp9IigTLpK-aE2ATxQget2gNR6i0VFMG76XCziGkk2j0ttx0bbsQHUObQDeCO1LAk2_nif1ksSf9sosfYBv0qe_VvyHHbMU4n7u4BWsXpXlzoWKXgXmtcfTrfacZ9duN6N |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnZ3PT4MwFMebOQ_qwfgzzp89eMUBLRSOy3RO3XbazG6ktMUwF1g2vPq321dAZ2I08QotIa_Ne6_tp9-H0LVUzOUx0FNwtg6C5BYXgW2FvogdmwufxbA1MBz5_Ql9nHrTBurWd2EAq6x8f-nTjbeunrQra7YXado2WBLxdf5sOIPQ20Cb1CMMuL6b9y_Og7LymBk4Rmhe358zkJf-OEAjro5kRsbT1C37IT6txZzeHtqtkkXcKf9nHzVUdoB21iQED9H8VqkFXiojgCrMXh-uKkG8WBCiJJ7lqX5Y8NUr5pnEytz2w3mSzHMD0OM0w5POswVikRL79zj9lOksMGy34axkxVdHaNK7G3f7VlVBwRJ6KVBYceBJnVKoULqhpFRwx9VjFiYOUQkog3GdDEnJCLWpSpKECuUTphd4etkSB4wocoyaWZ6pE4Rtj3thTHU4Z5ISwbh0lfIC7QBiX_mu20KktlskKnlxqHIxj2qObBaV1o7A2hHgdA5rIeuz16KU1_ijPauHJPo2TSIdAX7tefrvnldoqz8eDqLBw-jpDG3Dm5IAPEfNYvmmLnRWUsSXZtZ9AD4p31g |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+reinforcement+learning-based+joint+task+and+energy+offloading+in+UAV-aided+6G+intelligent+edge+networks&rft.jtitle=Computer+communications&rft.au=Cheng%2C+Zhipeng&rft.au=Liwang%2C+Minghui&rft.au=Chen%2C+Ning&rft.au=Huang%2C+Lianfen&rft.date=2022-08-01&rft.issn=0140-3664&rft.volume=192&rft.spage=234&rft.epage=244&rft_id=info:doi/10.1016%2Fj.comcom.2022.06.017&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_comcom_2022_06_017 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0140-3664&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0140-3664&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0140-3664&client=summon |