{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T20:03:15Z","timestamp":1767211395499,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"],"award-info":[{"award-number":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"],"award-info":[{"award-number":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"]}]},{"name":"QLU Talent Research Project","award":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"],"award-info":[{"award-number":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"]}]},{"name":"Young Talent of Lifting Engineering for Science and Technology in Shandong","award":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"],"award-info":[{"award-number":["62401304","62225105","ZR2022QF040","2023RCKY138","SDAST2025QTA077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the rapid development of intelligent transportation systems (ITSs) and Internet of Things (IoT), vehicle-mounted edge computing (VEC) networks are facing the challenge of handling increasingly growing computation-intensive and latency-sensitive tasks. In the UAV-assisted VEC network, by introducing mobile edge servers, the coverage of ground infrastructure is effectively supplemented. However, there is still the problem of decision-making lag in a highly dynamic environment. This paper proposes a deep reinforcement learning framework based on the long short-term memory (LSTM) network for trajectory prediction to optimize resource allocation in UAV-assisted VEC networks. Uniquely integrating vehicle trajectory prediction with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, this framework enables proactive computation offloading and UAV trajectory planning. Specifically, we design an LSTM network with an attention mechanism to predict the future trajectory of vehicles and integrate the prediction results into the optimization decision-making process. We propose state smoothing and data augmentation techniques to improve training stability and design a multi-objective optimization model that incorporates the Age of Information (AoI), energy consumption, and resource leasing costs. The simulation results show that compared with existing methods, the method proposed in this paper significantly reduces the total system cost, improves the information freshness, and exhibits better environmental adaptability and convergence performance under various network conditions.<\/jats:p>","DOI":"10.3390\/info16080646","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T16:16:10Z","timestamp":1753805770000},"page":"646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Trajectory Prediction and Decision Optimization for UAV-Assisted VEC Networks: An Integrated LSTM-TD3 Framework"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4010-7877","authenticated-orcid":false,"given":"Jiahao","family":"Xie","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2765-3303","authenticated-orcid":false,"given":"Hao","family":"Hao","sequence":"additional","affiliation":[{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan 250300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alalwany, E., and Mahgoub, I. (2024). Security and trust management in the internet of vehicles (IoV): Challenges and machine learning solutions. Sensors, 24.","DOI":"10.3390\/s24020368"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110791","DOI":"10.1016\/j.comnet.2024.110791","article-title":"Task offloading strategies for mobile edge computing: A survey","volume":"254","author":"Dong","year":"2024","journal-title":"Comput. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7712","DOI":"10.1109\/TITS.2024.3349546","article-title":"Deep reinforcement learning-based task offloading for vehicular edge computing with flexible RSU-RSU cooperation","volume":"25","author":"Fan","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100668","DOI":"10.1016\/j.cosrev.2024.100668","article-title":"A survey on reconfigurable intelligent surfaces assisted multi-access edge computing networks: State of the art and future challenges","volume":"54","author":"Ahmed","year":"2024","journal-title":"Comput. Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1109\/JIOT.2021.3058213","article-title":"Dynamic Digital Twin and Distributed Incentives for Resource Allocation in Aerial-Assisted Internet of Vehicles","volume":"9","author":"Sun","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1109\/LWC.2024.3416742","article-title":"Spatio-Temporal Trajectory Design for UAVs: Enhancing URLLC and LoS Transmission in Communications","volume":"13","author":"Yu","year":"2024","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19023","DOI":"10.1109\/JIOT.2024.3364230","article-title":"State-of-the-art and future research challenges in uav swarms","volume":"11","author":"Javed","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6321","DOI":"10.1109\/TITS.2025.3525735","article-title":"Efficient Vehicle Selection and Resource Allocation for Knowledge Distillation-Based Federated Learning in UAV-Assisted VEC","volume":"26","author":"Li","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","first-page":"100594","article-title":"A survey of energy efficient methods for UAV communication","volume":"41","author":"Jin","year":"2023","journal-title":"Veh. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21473","DOI":"10.1109\/JIOT.2025.3547630","article-title":"The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling","volume":"12","author":"Yi","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3298","DOI":"10.1109\/TWC.2023.3307154","article-title":"Latency Minimization for UAV-Enabled URLLC-Based Mobile Edge Computing Systems","volume":"23","author":"Wu","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/LWC.2019.2916549","article-title":"Energy Minimization in Internet-of-Things System Based on Rotary-Wing UAV","volume":"8","author":"Zhan","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6709","DOI":"10.1109\/JIOT.2020.2999063","article-title":"Energy\u2013Latency Tradeoff for Computation Offloading in UAV-Assisted Multiaccess Edge Computing System","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.1109\/TWC.2023.3291692","article-title":"Energy and Latency Efficient Joint Communication and Computation Optimization in a Multi-UAV-Assisted MEC Network","volume":"23","author":"Pervez","year":"2024","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15110","DOI":"10.1109\/JSEN.2024.3378844","article-title":"Joint Energy and AoI Optimization in UAV-Assisted MEC-WET Systems","volume":"24","author":"Yang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"19882","DOI":"10.1109\/JIOT.2024.3370553","article-title":"Deep-Reinforcement-Learning-Based Computation Offloading in UAV-Assisted Vehicular Edge Computing Networks","volume":"11","author":"Yan","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s11036-020-01624-1","article-title":"Vehicular edge computing and networking: A survey","volume":"26","author":"Liu","year":"2021","journal-title":"Mob. Netw. Appl."},{"key":"ref_18","first-page":"132","article-title":"Review on offloading of vehicle edge computing","volume":"2","author":"Wang","year":"2022","journal-title":"J. Artif. Intell. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MCOM.2019.1800772","article-title":"A Hierarchical Architecture for the Future Internet of Vehicles","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8852","DOI":"10.1109\/JIOT.2021.3116108","article-title":"Revenue and Energy Efficiency-Driven Delay-Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach","volume":"9","author":"Huang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"103341","DOI":"10.1016\/j.jnca.2022.103341","article-title":"Survey on computation offloading in UAV-Enabled mobile edge computing","volume":"201","author":"Huda","year":"2022","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3121","DOI":"10.1109\/JIOT.2023.3294535","article-title":"Modeling on Resource Allocation for Age-Sensitive Mobile-Edge Computing Using Federated Multiagent Reinforcement Learning","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4988","DOI":"10.1109\/TII.2020.3017573","article-title":"Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning","volume":"17","author":"Wu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"74252","DOI":"10.1109\/ACCESS.2025.3561083","article-title":"SDN-Based Edge Computing in Vehicular Communication Networks: A Survey of Existing Approaches","volume":"13","author":"Imran","year":"2025","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MNET.2018.1700364","article-title":"Enabling Collaborative Edge Computing for Software Defined Vehicular Networks","volume":"32","author":"Wang","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/TMC.2019.2953163","article-title":"Software-Defined Cooperative Data Sharing in Edge Computing Assisted 5G-VANET","volume":"20","author":"Luo","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13677-023-00461-3","article-title":"Optimizing task offloading and resource allocation in edge-cloud networks: A DRL approach","volume":"12","author":"Ullah","year":"2023","journal-title":"J. Cloud Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hao, H., Xu, C., Zhang, W., Yang, S., and Muntean, G.M. (IEEE Trans. Wirel. Commun., 2025). Task-Driven Priority-Aware Computation Offloading Using Deep Reinforcement Learning, IEEE Trans. Wirel. Commun., early eccess.","DOI":"10.1109\/TWC.2025.3564356"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/JIOT.2023.3300718","article-title":"Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multiagent DRL Approach","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8649","DOI":"10.1109\/TMC.2024.3350078","article-title":"Joint Task Offloading, Resource Allocation, and Trajectory Design for Multi-UAV Cooperative Edge Computing With Task Priority","volume":"23","author":"Hao","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"102068","DOI":"10.1016\/j.jksuci.2024.102068","article-title":"RNN-LSTM: From applications to modeling techniques and beyond\u2014Systematic review","volume":"36","author":"Hassan","year":"2024","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_34","first-page":"1","article-title":"Vehicle trajectory data processing, analytics, and applications: A survey","volume":"57","author":"Liu","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Altch\u00e9, F., and de La Fortelle, A. (, January 16\u201319). An LSTM network for highway trajectory prediction. Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317913"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TIV.2020.2991952","article-title":"Attention based vehicle trajectory prediction","volume":"6","author":"Messaoud","year":"2020","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tu, Y., Chen, H., Yan, L., and Zhou, X. (2022). Task offloading based on LSTM prediction and deep reinforcement learning for efficient edge computing in IoT. Future Internet, 14.","DOI":"10.3390\/fi14020030"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Behera, S.R., Panigrahi, N., Bhoi, S.K., Sahoo, K.S., Jhanjhi, N., and Ghoniem, R.M. (2023). Time series-based edge resource prediction and parallel optimal task allocation in mobile edge computing environment. Processes, 11.","DOI":"10.3390\/pr11041017"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/LWC.2014.2342736","article-title":"Optimal LAP altitude for maximum coverage","volume":"3","author":"Kandeepan","year":"2014","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3536","DOI":"10.1109\/TMC.2021.3059691","article-title":"Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-Assisted Mobile Edge Computing","volume":"21","author":"Wang","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1109\/TWC.2019.2902559","article-title":"Energy Minimization for Wireless Communication With Rotary-Wing UAV","volume":"18","author":"Zeng","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108000","DOI":"10.1109\/ACCESS.2024.3425497","article-title":"Deep Reinforcement Learning for AoI Minimization in UAV-Aided Data Collection for WSN and IoT Applications: A Survey","volume":"12","author":"Amodu","year":"2024","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/JSAC.2021.3065072","article-title":"Age of Information: An Introduction and Survey","volume":"39","author":"Yates","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/TNN.1998.712192","article-title":"Reinforcement learning: An introduction","volume":"9","author":"Sutton","year":"1998","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/8\/646\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:18:18Z","timestamp":1760033898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/8\/646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,29]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["info16080646"],"URL":"https:\/\/doi.org\/10.3390\/info16080646","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,7,29]]}}}