{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T20:24:15Z","timestamp":1773692655762,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["62002213"],"award-info":[{"award-number":["62002213"]}]},{"name":"National Science Foundation of China","award":["20YF1413700"],"award-info":[{"award-number":["20YF1413700"]}]},{"name":"National Science Foundation of China","award":["21YF1413800"],"award-info":[{"award-number":["21YF1413800"]}]},{"name":"National Science Foundation of China","award":["21511102502"],"award-info":[{"award-number":["21511102502"]}]},{"name":"National Science Foundation of China","award":["21511102500"],"award-info":[{"award-number":["21511102500"]}]},{"name":"National Science Foundation of China","award":["221100240100"],"award-info":[{"award-number":["221100240100"]}]},{"name":"National Science Foundation of China","award":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}]},{"name":"Shanghai Sailing Program","award":["62002213"],"award-info":[{"award-number":["62002213"]}]},{"name":"Shanghai Sailing Program","award":["20YF1413700"],"award-info":[{"award-number":["20YF1413700"]}]},{"name":"Shanghai Sailing Program","award":["21YF1413800"],"award-info":[{"award-number":["21YF1413800"]}]},{"name":"Shanghai Sailing Program","award":["21511102502"],"award-info":[{"award-number":["21511102502"]}]},{"name":"Shanghai Sailing Program","award":["21511102500"],"award-info":[{"award-number":["21511102500"]}]},{"name":"Shanghai Sailing Program","award":["221100240100"],"award-info":[{"award-number":["221100240100"]}]},{"name":"Shanghai Sailing Program","award":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["62002213"],"award-info":[{"award-number":["62002213"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["20YF1413700"],"award-info":[{"award-number":["20YF1413700"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["21YF1413800"],"award-info":[{"award-number":["21YF1413800"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["21511102502"],"award-info":[{"award-number":["21511102502"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["21511102500"],"award-info":[{"award-number":["21511102500"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["221100240100"],"award-info":[{"award-number":["221100240100"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan","award":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}]},{"name":"Henan Science and Technology Major","award":["62002213"],"award-info":[{"award-number":["62002213"]}]},{"name":"Henan Science and Technology Major","award":["20YF1413700"],"award-info":[{"award-number":["20YF1413700"]}]},{"name":"Henan Science and Technology Major","award":["21YF1413800"],"award-info":[{"award-number":["21YF1413800"]}]},{"name":"Henan Science and Technology Major","award":["21511102502"],"award-info":[{"award-number":["21511102502"]}]},{"name":"Henan Science and Technology Major","award":["21511102500"],"award-info":[{"award-number":["21511102500"]}]},{"name":"Henan Science and Technology Major","award":["221100240100"],"award-info":[{"award-number":["221100240100"]}]},{"name":"Henan Science and Technology Major","award":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}]},{"name":"National Natural Science Foundation of China","award":["62002213"],"award-info":[{"award-number":["62002213"]}]},{"name":"National Natural Science Foundation of China","award":["20YF1413700"],"award-info":[{"award-number":["20YF1413700"]}]},{"name":"National Natural Science Foundation of China","award":["21YF1413800"],"award-info":[{"award-number":["21YF1413800"]}]},{"name":"National Natural Science Foundation of China","award":["21511102502"],"award-info":[{"award-number":["21511102502"]}]},{"name":"National Natural Science Foundation of China","award":["21511102500"],"award-info":[{"award-number":["21511102500"]}]},{"name":"National Natural Science Foundation of China","award":["221100240100"],"award-info":[{"award-number":["221100240100"]}]},{"name":"National Natural Science Foundation of China","award":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.<\/jats:p>","DOI":"10.3390\/s22239340","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T03:03:41Z","timestamp":1669863821000},"page":"9340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing"],"prefix":"10.3390","volume":"22","author":[{"given":"Chenhong","family":"Cao","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meijia","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyu","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miaoling","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9754-0008","authenticated-orcid":false,"given":"Jiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MVT.2017.2668838","article-title":"Mobile-edge computing for vehicular networks: A promising network paradigm with predictive off-loading","volume":"12","author":"Zhang","year":"2017","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1109\/TSC.2021.3064579","article-title":"Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge Computing","volume":"15","author":"Luo","year":"2021","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/MNET.2019.1900120","article-title":"Autonomous Driving Cars in Smart Cities: Recent Advances, Requirements, and Challenges","volume":"34","author":"Yaqoob","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7944","DOI":"10.1109\/TVT.2019.2917890","article-title":"Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks","volume":"68","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11158","DOI":"10.1109\/TVT.2019.2935450","article-title":"Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks","volume":"68","author":"Liu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dai, P., Hu, K., Wu, X., Xing, H., and Yu, Z. (2021, January 10\u201313). Asynchronous Deep Reinforcement Learning for Data-Driven Task Offloading in MEC-Empowered Vehicular Networks. Proceedings of the IEEE INFOCOM, Vancouver, BC, Canada.","DOI":"10.1109\/INFOCOM42981.2021.9488886"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1007\/s11227-019-03011-4","article-title":"Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks","volume":"76","author":"Wan","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, M., Zhai, Q., Chen, Y., Feng, S., and Shu, F. (2021). Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing. Sensors, 21.","DOI":"10.3390\/s21082628"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1109\/JIOT.2018.2876298","article-title":"Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks","volume":"6","author":"Dai","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.comcom.2019.11.019","article-title":"Efficient task scheduling for servers with dynamic states in vehicular edge computing","volume":"150","author":"Wu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1109\/TITS.2016.2529000","article-title":"High-Precision Vehicle Navigation in Urban Environments using a MEM\u2019s IMU and Single-frequency GPS Receiver","volume":"17","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MVT.2018.2882873","article-title":"Mobile edge computing-enabled 5G vehicular networks: Toward the integration of communication and computing","volume":"14","author":"Ning","year":"2018","journal-title":"IEEE Veh. Technol. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5087","DOI":"10.1109\/TVT.2019.2905432","article-title":"Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach","volume":"68","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10660","DOI":"10.1109\/TVT.2017.2714704","article-title":"AVE: Autonomous vehicular edge computing framework with ACO-based scheduling","volume":"66","author":"Feng","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","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":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1080\/01621459.1970.10481112","article-title":"Tables of normal percentile points","volume":"65","author":"White","year":"1970","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_19","unstructured":"Didi (2021, May 01). Urban Traffic Time Index and Trajectory Data (New). Available online: https:\/\/gaia.didichuxing.com."},{"key":"ref_20","unstructured":"(2022, April 01). Openstreetmap. Available online: https:\/\/master.apis.dev.openstreetmap.org."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101867","DOI":"10.1016\/j.phycom.2022.101867","article-title":"Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach","volume":"55","author":"Wu","year":"2022","journal-title":"Phys. Commun."},{"key":"ref_22","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal policy optimization algorithms. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Su, M., Cao, C., Dai, M., Li, J., and Li, Y. (2023, January 10\u201312). Towards Fast and Energy-Efficient Offloading for Vehicular Edge Computing. Proceedings of the 2022 IEEE 28th International Conference on Parallel and Distributed Systems (ICPADS), Nanjing, China.","DOI":"10.1109\/ICPADS56603.2022.00090"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, C., Wang, Z., Pei, Q., He, C., and Dou, Z. (2020, January 9\u201311). Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles. Proceedings of the 2020 IEEE\/CIC International Conference on Communications in China (ICCC), Chongqing, China.","DOI":"10.1109\/ICCC49849.2020.9238970"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/OJITS.2022.3142065","article-title":"Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over the Multi-RSU-Overlapped Environment","volume":"3","author":"Lin","year":"2022","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1109\/TITS.2020.2997832","article-title":"Intelligent edge computing in internet of vehicles: A joint computation offloading and caching solution","volume":"22","author":"Ning","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","unstructured":"Zheng, J., Luan, T.H., Gao, L., Zhang, Y., and Wu, Y. (2021). Learning based task offloading in digital twin empowered internet of vehicles. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.comcom.2022.04.006","article-title":"Task offloading in vehicular edge computing networks via deep reinforcement learning","volume":"189","author":"Karimi","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8877","DOI":"10.1109\/TVT.2022.3174530","article-title":"Joint Resource Allocation and Multi-Part Collaborative Task Offloading in MEC Systems","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1109\/TNSE.2021.3106955","article-title":"A Smart Network Resource Management System for High Mobility Edge Computing in 5G Internet of Vehicles","volume":"8","author":"Pang","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"9041","DOI":"10.1109\/TVT.2020.2999617","article-title":"A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing","volume":"69","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:30:41Z","timestamp":1760146241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":31,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239340"],"URL":"https:\/\/doi.org\/10.3390\/s22239340","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,30]]}}}