{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:49:36Z","timestamp":1784296176209,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,24]],"date-time":"2024-03-24T00:00:00Z","timestamp":1711238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010880","name":"Science and Technology Project of SGCC","doi-asserted-by":"publisher","award":["5108-202318054A-1-1-ZN"],"award-info":[{"award-number":["5108-202318054A-1-1-ZN"]}],"id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010880","name":"Science and Technology Project of SGCC","doi-asserted-by":"publisher","award":["SJCX23-0403"],"award-info":[{"award-number":["SJCX23-0403"]}],"id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["5108-202318054A-1-1-ZN"],"award-info":[{"award-number":["5108-202318054A-1-1-ZN"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["SJCX23-0403"],"award-info":[{"award-number":["SJCX23-0403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the ongoing advancement of electric power Internet of Things (IoT), traditional power inspection methods face challenges such as low efficiency and high risk. Unmanned aerial vehicles (UAVs) have emerged as a more efficient solution for inspecting power facilities due to their high maneuverability, excellent line-of-sight communication capabilities, and strong adaptability. However, UAVs typically grapple with limited computational power and energy resources, which constrain their effectiveness in handling computationally intensive and latency-sensitive inspection tasks. In response to this issue, we propose a UAV task offloading strategy based on deep reinforcement learning (DRL), which is designed for power inspection scenarios consisting of mobile edge computing (MEC) servers and multiple UAVs. Firstly, we propose an innovative UAV-Edge server collaborative computing architecture to fully exploit the mobility of UAVs and the high-performance computing capabilities of MEC servers. Secondly, we established a computational model concerning energy consumption and task processing latency in the UAV power inspection system, enhancing our understanding of the trade-offs involved in UAV offloading strategies. Finally, we formalize the task offloading problem as a multi-objective optimization issue and simultaneously model it as a Markov Decision Process (MDP). Subsequently, we proposed a task offloading algorithm based on a Deep Deterministic Policy Gradient (OTDDPG) to obtain the optimal task offloading strategy for UAVs. The simulation results demonstrated that this approach outperforms baseline methods with significant improvements in task processing latency and energy consumption.<\/jats:p>","DOI":"10.3390\/s24072070","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:32:36Z","timestamp":1711369956000},"page":"2070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Task Offloading Strategy for Unmanned Aerial Vehicle Power Inspection Based on Deep Reinforcement Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1103-5148","authenticated-orcid":false,"given":"Wei","family":"Zhuang","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5479-1678","authenticated-orcid":false,"given":"Fanan","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,24]]},"reference":[{"key":"ref_1","first-page":"107","article-title":"Modeling Method for Bussiness Sequential Logic and Workload of Edge Computing Terminal in Electric Internet of Things","volume":"45","author":"Cen","year":"2021","journal-title":"Autom. Electr. Power Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhuang, W., Fan, J.L., Xia, M., and Zhu, K. (2023). A Multi-Scale Spatial-Temporal Graph Neural Network-Based Method of Multienergy Load Forecasting in Integrated Energy System. IEEE Trans. Smart Grid.","DOI":"10.1109\/TSG.2023.3315750"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"531","DOI":"10.23919\/cje.2022.00.103","article-title":"A Hybrid Entropy and Blockchain Approach for Network Security Defense in SDN-Based IIoT","volume":"32","author":"Su","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_4","first-page":"112","article-title":"Research and application of key technology for intelligent inspection of OTH UAV in mountainous environment","volume":"2019","author":"Huang","year":"2019","journal-title":"Electr. Eng."},{"key":"ref_5","first-page":"3636","article-title":"Review on Mounted UAV for Transmission Line Inspection","volume":"45","author":"Sui","year":"2021","journal-title":"Power Syst. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1007\/s12083-019-00793-5","article-title":"UAV-assisted wireless relay networks for mobile offloading and trajectory optimization","volume":"12","author":"Feng","year":"2019","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1109\/JIOT.2018.2887086","article-title":"UAV communications for 5G and beyond: Recent advances and future trends","volume":"6","author":"Li","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_8","first-page":"161","article-title":"Design and Application of UAV Intelligent Inspection System for Transmission Lines Based on Cloud and Fog-edge Heterogeneous Collaborative Computing Architecture","volume":"53","author":"Huang","year":"2020","journal-title":"Electr. Power"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1049\/iet-com.2016.1114","article-title":"Mobile cloud computing with a UAV-mounted cloudlet: Optimal bit allocation for communication and computation","volume":"11","author":"Jeong","year":"2017","journal-title":"IET Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1002\/wcm.1203","article-title":"A survey of mobile cloud computing: Architecture, applications, and approaches","volume":"13","author":"Dinh","year":"2013","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_11","first-page":"1332","article-title":"Adaptive monitoring based fault detection for cloud computing systems","volume":"41","author":"Wang","year":"2018","journal-title":"Chin. J. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","article-title":"Convergence of Edge Computing and Deep Learning: A Comprehensive Survey","volume":"22","author":"Wang","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiang, J., Ananthanarayanan, G., and Bodik, P. (2018, January 20\u201325). Chameleon: Scalable adaptation of video analytics. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, Budapest, Hungary.","DOI":"10.1145\/3230543.3230574"},{"key":"ref_14","first-page":"69","article-title":"Edge computing: State-of-the-Art and future directions","volume":"56","author":"Shi","year":"2019","journal-title":"J. Comput. Res. Dev."},{"key":"ref_15","first-page":"199","article-title":"Offloading strategy for UAV power inspection task based on deep reinforcement learning","volume":"Volume 12610","author":"Tong","year":"2023","journal-title":"Proceedings of the Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"127779","DOI":"10.1109\/ACCESS.2021.3112104","article-title":"Energy Efficient UAV-Enabled Mobile Edge Computing for IoT Devices: A Review","volume":"9","author":"Abrar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MC.2010.98","article-title":"Cloud computing for mobile users: Can offloading computation save energy?","volume":"43","author":"Kumar","year":"2010","journal-title":"Computer"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MSP.2014.2334709","article-title":"Communicating while computing: Distributed mobile cloud computing over 5G heterogeneous networks","volume":"31","author":"Barbarossa","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108356","DOI":"10.1016\/j.comnet.2021.108356","article-title":"Smart computational offloading for mobile edge computing in next-generation Internet of Things networks","volume":"198","author":"Ali","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9113","DOI":"10.1109\/TII.2022.3225313","article-title":"Quantum particle swarm optimization for task offloading in mobile edge computing","volume":"19","author":"Dong","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","first-page":"3649","article-title":"Power Inspection and Unloading Strategy of UAV Based on Game Theory and Reinforcement Learning","volume":"45","author":"Deng","year":"2021","journal-title":"Power Syst. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100130","DOI":"10.1016\/j.geits.2023.100130","article-title":"GREENSKY: A fair energy-aware optimization model for UAVs in next-generation wireless networks","volume":"3","author":"Thantharate","year":"2024","journal-title":"Green Energy Intell. Transp."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dai, Z., Xu, G., and Liu, Z. (2022). Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning. Future Internet, 14.","DOI":"10.3390\/fi14080226"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53708","DOI":"10.1109\/ACCESS.2021.3070908","article-title":"Task offloading and trajectory control for UAV-assisted mobile edge computing using deep reinforcement learning","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shen, H., Jiang, Y., and Deng, F. (2022). Task Unloading Strategy of Multi UAV for Transmission Line Inspection Based on Deep Reinforcement Learning. Electronics, 11.","DOI":"10.3390\/electronics11142188"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Alhelaly, S., Muthanna, A., and Elgendy, I.A. (2022). Optimizing task offloading energy in multi-user multi-UAV-enabled mobile edge-cloud computing systems. Appl. Sci., 12.","DOI":"10.3390\/app12136566"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.23919\/cje.2021.00.312","article-title":"Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT","volume":"32","author":"Shao","year":"2021","journal-title":"Chin. J. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hua, M., Huang, Y., Sun, Y., Wang, Y., and Yang, L. (2018, January 19\u201321). Energy optimization for Cellular-Connected UAV Mobile Edge Computing Systems. Proceedings of the 2018 IEEE International Conference on Communication Systems (ICCS), Chengdu, China.","DOI":"10.1109\/ICCS.2018.8689226"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, S., Chen, J., Jiang, T., and Ye, F. (2020, January 17\u201320). Multi-UAV Cooperative Mission Assignment Algorithm Based on ACO method. Proceedings of the 2020 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA.","DOI":"10.1109\/ICNC47757.2020.9049667"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cao, X., Xu, J., and Zhang, R. (2018, January 25\u201328). Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization. Proceedings of the 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece.","DOI":"10.1109\/SPAWC.2018.8445936"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dhiman, S., Chauhan, A., Kasuhal, S., and Kumar, H. (2023, January 24\u201326). Computation Offloading in Mobile Edge Computing for Next Generation Networks: A deep reinforcement learning approach. Proceedings of the 2023 International Conference for Advancement in Technology (ICONAT), Goa, India.","DOI":"10.1109\/ICONAT57137.2023.10080439"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78702","DOI":"10.1109\/ACCESS.2020.2990166","article-title":"Joint Beamforming and Trajectory Optimization for Intelligent Reflecting Surfaces-Assisted UAV Communications","volume":"8","author":"Ge","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/JIOT.2018.2878876","article-title":"Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems","volume":"6","author":"Hu","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_34","unstructured":"Lillicrap, P.T., Hunt, J.J., and Pritzel, A. (2015). Continuous control with deep reinforcement learning. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2070\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:17:48Z","timestamp":1760105868000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,24]]},"references-count":34,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072070"],"URL":"https:\/\/doi.org\/10.3390\/s24072070","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,24]]}}}