Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing oﬄoading: a game-theoretic approach✩

Yu Dai; Jie Tian; Tiantian Li; Jing Wang; Chuanfen Feng

doi:10.1016/j.dcan.2025.11.001

›› 2026, Vol. 12 ›› Issue (2) :294 -305. DOI: 10.1016/j.dcan.2025.11.001

Regular Papers

research-article

Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing oﬄoading: a game-theoretic approach^✩

Author information +

^a School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

^b Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212003, China

^* Changqinghu Campus, Shandong Normal University, No. 1, University Road, Science Park, Changqing District, Ji’nan 250358, China. E-mail address: tianjie@sdnu.edu.cn (J. Tian).

Yu Dai received B.E. degree from the School of Physics and Engineering, Qufu Normal University, Qufu, China, in 2022. She received the M.E. degrees from the School of Infor-mation Science and Engineering, Shandong Normal University, Jinan, China, in 2025. She is a 2022-level student supervised by Professor Tian Jie, with her research interest focus-ing on radio resource management.

Jie Tian received the B.E. and M.E. degrees from Shandong Normal University, Ji-nan, China, in 2008 and 2011, respectively, and the Ph.D. degree in communication and information systems from the School of Information Science and Engineering, Shandong University, Jinan, in 2016. She is currently a Professor with the School of Information Science and Engineering, Shandong Normal University. Her research interests include intelligent radio resource management, Industrial Internet of Things, and mobile edge computing. She is an Associate Editor for the International Journal of Communication Systems.

Tiantian Li received the B.E. and M.E. degrees from the School of Physics and Elec-tronics, Shandong Normal University, Jinan, China, in 2009 and 2012, respectively. She received the Ph.D. degree in information and communication engineering from the School of Information Science and Engineering, Shandong University, Jinan, China, in 2021. She is currently an associate professor with the School of Information Science and Engineering, Shandong Normal University, China. Her research interests include rate splitting multi-ple access (RSMA), ultra-reliable low-latency communications (URLLC), and integrated sensing-computing-communication in 6G wireless networks.

Jing Wang received the B.E. degree from the School of Information Science and Engineering, Shandong Normal University, Jinan, China, in 2018, and the Ph.D. degree in information and communication engineering from the School of Information Science and Engineering, Shandong University, China, in 2024. She is currently with the Ocean College, Jiangsu University of Science and Technology. Her research interests include UAV assisted communications, radio resource management, and intelligent communica-tion technologies.

Chuanfen Feng received the B.E. degree from Ludong University, Yantai, China, in 2002, and the Ph.D. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 2007. He is currently a professorate senior engineer with the School of Information Science and Engineering, Shandong Normal University. His current research interests include mobile network planning, mobile edge computing and internet of things.

Show less

History +

PDF

Abstract

Multi-access Edge Computing (MEC) enhances computational eﬃciency by enabling resource-constrained User Devices (UD) to oﬄoad tasks to edge servers. Compared to traditional edge servers fixed on the Small Cellular Base Stations (SBS), mobile vehicles with idle resources serve as mobile edge servers, which can reduce UD’s task latency due to closer proximity to the UD. However, due to the limited computation resources of vehicles and highly competitive among UD, the available computation resources provided by vehicles for UD are uncertain, which poses a challenge for UD in making task oﬄoading decisions. In this paper, we establish a risk-aware task oﬄoading framework in vehicle-assisted MEC networks with computation resource uncertainty, where UD make oﬄoading decisions by considering their risk-aware behavior. We first characterize and model UD’s risk-aware behavior based on Prospect Theory (PT) and then formulate a user satisfaction maximization problem by optimizing the oﬄoading strategy of UD. To solve it, we reformulate the above problem among multiple users as a non-cooperative game and prove the uniqueness of the Pure Nash Equilibrium (PNE). We also propose a low-complexity distributed iterative optimization algorithm to obtain the optimal oﬄoading strategy. The simulation results demonstrate that the proposed scheme significantly enhances satisfaction utility of UD and reduces failure probability of vehicles compared to other benchmark methods.

Keywords

Task oﬄoading / Multi-access edge computing / Vehicle-assisted / Computation uncertainty / Prospect theory / Game theory

Cite this article

Download citation ▾

Yu Dai, Jie Tian, Tiantian Li, Jing Wang, Chuanfen Feng. Risk-aware user satisfaction maximization in vehicle-assisted multi-access edge computing oﬄoading: a game-theoretic approach^✩. , 2026, 12(2): 294-305 DOI:10.1016/j.dcan.2025.11.001

登录浏览全文

4963

注册一个新账户忘记密码

CRediT authorship contribution statement

Yu Dai: Writing-original draft, Software, Methodology, Investiga-tion. Jie Tian: Writing-review & editing, Formal analysis. Tiantian Li: Supervision, Resources. Jing Wang: Writing-review & editing. Chuan-fen Feng: Validation.

Declaration of competing interest

The authors declare that they have no known competing ﬁnancial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The work was supported in part by the National Natural Sci-ence Foundation of China (U22A2003, 62271295, U23A20277), and the Taishan Scholar Program of Shandong Province of China (No. tsqn202408137).

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	S.-J. Yoo, S.-H. Choi, Indoor AR navigation and emergency evacuation system based on machine learning and IoT technologies, IEEE Internet Things J. 9 (21) (2022) 20853-20868.

[2]	F. Giust, X. Costa-Perez, A. Reznik, Multi-access edge computing: an overview of ETSI MEC ISG, IEEE 5G Tech Focus 1 (4) (2017) 4.

[3]	T. Li, H. Zhang, S. Guo, D. Yuan, Max-min fair RIS-aided rate-splitting multiple access for multigroup multicast communications, China Commun. 20 (1) (2023) 184-198.

[4]	X. Deng, J. Yin, P. Guan, N.N. Xiong, L. Zhang, S. Mumtaz, Intelligent delay-aware partial computing task oﬄoading for multiuser industrial internet of things through edge computing, IEEE Internet Things J. 10 (4) (2023) 2954-2966.

[5]	Y. Liu, Y. Mao, Z. Liu, Y. Yang, Deep learning-assisted online task oﬄoading for la-tency minimization in heterogeneous mobile edge, IEEE Trans. Mob. Comput. 23 (5) (2023) 4062-4075.

[6]	X. Jiao, Y. Wang, S. Guo, H. Zhang, H. Dai, M. Li, P. Zhou, Deep reinforcement learning empowers wireless powered mobile edge computing: towards energy-aware online oﬄoading, IEEE Trans. Commun. 71 (9) (2023) 5214-5227.

[7]	L. Liu, X. Yuan, D. Chen, N. Zhang, H. Sun, A. Taherkordi, Multi-user dynamic computation oﬄoading and resource allocation in 5G MEC heterogeneous networks with static and dynamic subchannels, IEEE Trans. Veh. Technol. 72 (11) (2023) 14924-14938.

[8]	Y. Gong, J. Tian, X. Li, Q. Liu, T. Li, J. Bian, Stackelberg game-based task oﬄoad-ing in mobile edge computing-enabled hierarchical multi-coalition unmanned aerial vehicle networks, Int. J. Commun. Syst. 37 (4) (2024) e5674.

[9]	C. Yang, Q. Chen, Z. Zhu, Z.-A. Huang, S. Lan, L. Zhu, Evolutionary multitasking for costly task oﬄoading in mobile-edge computing networks, IEEE Trans. Evol. Com-put. 28 (2) (2023) 338-352.

[10]	L. Huang, Q. Yu, Mobility-aware and energy-eﬃcient oﬄoading for mobile edge computing in cellular networks, Ad Hoc Netw. 158 (2024) 103472.

[11]	M. Sheng, Y. Wang, X. Wang, J. Li, Energy-eﬃcient multiuser partial computation oﬄoading with collaboration of terminals, radio access network, and edge server, IEEE Trans. Commun. 68 (3) (2020) 1524-1537.

[12]	D. Wang, J. Tian, H. Zhang, D. Wu, Task oﬄoading and trajectory scheduling for UAV-enabled MEC networks: an optimal transport theory perspective, IEEE Wirel. Commun. Lett. 11 (1) (2021) 150-154.

[13]	T.X. Tran, D. Pompili, Joint task oﬄoading and resource allocation for multi-server mobile-edge computing networks, IEEE Trans. Veh. Technol. 68 (1) (2019) 856-868.

[14]	Z. Gao, L. Yang, Y. Dai, Large-scale cooperative task oﬄoading and resource allo-cation in heterogeneous MEC systems via multiagent reinforcement learning, IEEE Internet Things J. 11 (2) (2024) 2303-2321.

[15]	X. Zhang, Z. Wang, F. Tian, Z. Yang, Stackelberg-game-based multi-user multi-task oﬄoading in mobile edge computing, IEEE Trans. Cloud Comput. 12 (2) (2024) 459-475.

[16]	H. Yuan, Q. Hu, J. Bi, J. Lü, J. Zhang, M. Zhou, Profit-optimized computation of-floading with autoencoder-assisted evolution in large-scale mobile-edge computing, IEEE Internet Things J. 10 (13) (2023) 11896-11909.

[17]	J. Tian, D. Wang, H. Zhang, D. Wu, Service satisfaction-oriented task oﬄoading and UAV scheduling in UAV-enabled MEC networks, IEEE Trans. Wirel. Commun. 22 (12)(2023) 8949-8964.

[18]	Y. Liu, H. Yu, S. Xie, Y. Zhang, Deep reinforcement learning for oﬄoading and re-source allocation in vehicle edge computing and networks, IEEE Trans. Veh. Technol. Veh. Technol. 68 (11) (2019) 11158-11168.

[19]	H. Gai, H. Zhang, S. Guo, D. Yuan, Information freshness-oriented trajectory planning and resource allocation for UAV-assisted vehicular networks, China Commun. 20 (5) (2023) 244-262.

[20]	J. Tian, Q. Liu, H. Zhang, D. Wu, Multiagent deep-reinforcement-learning-based re-source allocation for heterogeneous QoS guarantees for vehicular networks, IEEE Internet Things J. 9 (3) (2022) 1683-1695.

[21]	P.A. Apostolopoulos, E.E. Tsiropoulou, S. Papavassiliou, Risk-aware social cloud computing based on serverless computing model, in: 2019 IEEE GLOBECOM, IEEE, 2019, pp. 1-6.

[22]	D. Kahneman, A. Tversky, Prospect theory: an analysis of decision under risk, ECTA 47 (2) (1979) 363-391.

[23]	P.A. Apostolopoulos, E.E. Tsiropoulou, S. Papavassiliou, Cognitive data oﬄoading in mobile edge computing for internet of things, IEEE Access 8 (2020) 55736-55749.

[24]	L. Tang, S. He, Multi-user computation oﬄoading in mobile edge computing: a be-havioral perspective, IEEE Netw. 32 (1) (2018) 48-53.

[25]	G. Mitsis, E.E. Tsiropoulou, S. Papavassiliou, Data oﬄoading in UAV-assisted multi-access edge computing systems: a resource-based pricing and user risk-awareness approach, Sensors 20 (8) (2020) 2434.

[26]	P.A. Apostolopoulos, E.E. Tsiropoulou, S. Papavassiliou, Risk-aware data oﬄoading in multi-server multi-access edge computing environment, IEEE/ACM Trans. Netw. 28 (3) (2020) 1405-1418.

[27]	D.-G. Zhang, W.-M. Dong, T. Zhang, J. Zhang, P. Zhang, G.-X. Sun, Y.-H. Cao, New computing tasks oﬄoading method for MEC based on prospect theory framework, IEEE Trans. Comput. Soc. Syst. 11 (1) (2024) 770-781.

[28]	P.A. Apostolopoulos, G. Fragkos, E.E. Tsiropoulou, S. Papavassiliou, Data oﬄoading in UAV-assisted multi-access edge computing systems under resource uncertainty, IEEE Trans. Mob. Comput. 22 (1) (2021) 175-190.

[29]	G. Mitsis, E.E. Tsiropoulou, S. Papavassiliou, Price and risk awareness for data of-floading decision-making in edge computing systems, IEEE Syst. J. 16 (4) (2022) 6546-6557.

[30]	Q. Cui, X. Hu, W. Ni, X. Tao, P. Zhang, T. Chen, K.-C. Chen, M. Haenggi, Vehicu-lar mobility patterns and their applications to internet-of-vehicles: a comprehensive survey, Sci. China Inf. Sci. 65 (11) (2022) 211301.

[31]	X. Huang, R. Yu, D. Ye, L. Shu, S. Xie, Eﬃcient workload allocation and user-centric utility maximization for task scheduling in collaborative vehicular edge computing, IEEE Trans. Veh. Technol. 70 (4) (2021) 3773-3787.

[32]	X.-Q. Pham, T. Huynh-The, E.-N. Huh, D.-S. Kim, Partial computation oﬄoading in parked vehicle-assisted multi-access edge computing: a game-theoretic approach, IEEE Trans. Veh. Technol. 71 (9) (2022) 10220-10225.

[33]	J.B. Rosen, Existence and uniqueness of equilibrium points for concave n-person games, ECTA: JES (1965) 520-534.

[34]	A.B. de Souza, P.A.L. Rego, T. Carneiro, P.H.G. Rocha, J.N. de Souza, A context-oriented framework for computation oﬄoading in vehicular edge computing using WAVE and 5G networks, Veh. Commun. 32 (2021) 100389.

[35]	L. Liu, J. Feng, X. Mu, Q. Pei, D. Lan, M. Xiao, Asynchronous deep reinforce-ment learning for collaborative task computing and on-demand resource alloca-tion in vehicular edge computing, IEEE Trans. Intell. Transp. Syst. 24 (12) (2023) 15513-15526.

[36]	B. Sedlak, V.C. Pujol, A. Morichetta, P.K. Donta,S. Dustdar, Adaptive stream pro-cessing on edge devices through active inference, arXiv preprint arXiv:2409.17937.

[37]	V.C. Pujol, B. Sedlak, P.K. Donta, S. Dustdar, On causality in distributed continuum systems, IEEE Internet Comput. 28 (2) (2024) 57-64.

[38]	B. Sedlak, V.C. Pujol, P.K. Donta, S. Dustdar, Equilibrium in the computing contin-uum through active inference, Future Gener. Comput. Syst. 160 (2024) 92-108.

[39]	S.P. Boyd, L. Vandenberghe,Convex Optimization, ﬁrst ed.ed., Cambridge University Press, Cambridge, 2004.

[40]	X. Chen, L. Jiao, W. Li, X. Fu, Eﬃcient multi-user computation oﬄoading for mobile-edge cloud computing, IEEE/ACM Trans. Netw. 24 (5) (2015) 2795-2808.

[41]	T. Crummey, R. Farshadnia, P. Fleming, A. Grace, S. Hancock,An optimization tool-box for MATLAB, in:International Conference on Control 1991, Control’91, IET, 1991, pp. 744-749.