Fog computing is a distributed infrastructure that extends computing, communication, and storage capabilities toward the network edge. Compared to cloud computing, fog computing can support delay-sensitive service requests while reducing energy consumption and traffic congestion. Fog computing contributes to efficient resource utilization and improved performance in terms of latency, bandwidth, and energy consumption. However, one of the major challenges in fog computing is the limited computational capacity of fog nodes under increasing daily demand, especially during peak hours, which can lead to severe performance degradation. Therefore, an optimal mechanism is required to ensure satisfactory quality of service (QoS). Integrating fog computing with vehicular ad hoc networks, leading to vehicular fog computing (VFC), has emerged as a promising solution to reduce overload at base stations and minimize processing delays during peak periods. In this approach, the surplus computational resources of nearby vehicles are utilized as an on-demand, low-cost option. Consequently, the computing resources provided by a large group of vehicles can be aggregated to alleviate network congestion during peak hours without additional servers, enabling real-time computational scalability. Nevertheless, the large-scale deployment of vehicular fog networks still faces several critical challenges, such as the lack of efficient incentive mechanisms and task assignment strategies. In this study, we first propose a solution to minimize network delay from the perspective of integrated contract-based optimization. Next, the problem of computational task allocation is formulated as a two-sided matching problem between vehicles and users, and a stable, QoS-aware matching algorithm is introduced to solve it. Finally, task offloading decisions are performed to minimize total network latency. The proposed scheme can effectively guarantee network load balancing and improve the utilization of idle vehicular resources.
[1]Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, Y., Zhou, P., Chai, Y., Caine, B., Vasudevan, V., Han, W., Xiao, J., Hu, Y., & Anguelov, D. (2020). Scalability in perception for autonomous driving: Waymo Open Dataset. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2443–2451). IEEE. https://doi.org/10.1109/CVPR42600.2020.00252
[2]Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-145
[3]Adnan, U. K., & Ahmed, N. (2022). Thermal enhancement and entropy investigation in dissipative ZnO-SAE50 under thermal radiation: A computational paradigm. Waves in Random and Complex Media, 35, 3573–3588. https://doi.org/10.1080/17455030.2022.2053243
[4]Bennis, M., Debbah, M., & Poor, H. V. (2018). Ultrareliable and low-latency wireless communication: Tail, risk, and scale. Proceedings of the IEEE, 106(10), 1834–1853. https://doi.org/10.1109/JPROC.2018.2867029
[5]Bilò, V., Moscardelli, L., & Vinci, C. (2024). Uniform mixed equilibria in network congestion games with link failures. Mathematics of Operations Research, 49, 509–535. https://doi.org/10.1287/moor.2023.1365
[6]Qi, Z., Yinfa, Z., Balin, T., Ding, D., & Shuai, R. (2020). Research on the calculation method of transmission bandwidth demand of private network communication hub station. In 2020 International Conference on Wireless Communications and Smart Grid (ICWCSG) (pp. 32–36). IEEE. https://doi.org/10.1109/ICWCSG50807.2020.00016
[7]Wu, D., Zhang, P., He, Y., & Luo, X. (2023). A double-space and double-norm ensembled latent factor model for highly accurate web service QoS prediction. IEEE Transactions on Services Computing, 16, 802–814. https://doi.org/10.1109/TSC.2022.3178543
[8]Sharma, T., Mangla, T., Gupta, A., Jiang, J., & Feamster, N. (2023). Estimating WebRTC video QoE metrics without using application headers. In Proceedings of the 2023 ACM Internet Measurement Conference. Association for Computing Machinery. https://doi.org/10.1145/3618257.3624828
[9]Sciangula, G., Casini, D., Biondi, A., Scordino, C., & Natale, M. (2023). Bounding the data-delivery latency of DDS messages in real-time applications. In 2023 IEEE Real-Time Systems Symposium (pp. 9:1–9:26).
[10]Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656. https://doi.org/10.1109/COMST.2017.2682318
[11]Kashani, M. H., & Mahdipour, E. (2023). Load balancing algorithms in fog computing. IEEE Transactions on Services Computing, 16, 1505–1521. https://doi.org/10.1109/TSC.2022.3174475
[12]Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., & Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289–330. https://doi.org/10.1016/j.sysarc.2019.02.009
[13]Kai, K., Cong, W., & Tao, L. (2017). Fog computing for vehicular ad hoc networks: Paradigms, scenarios, and issues. Journal of China Universities of Posts and Telecommunications, 23, 56–65. https://doi.org/10.1016/S1005-8885(16)60021-3
[14]Zhou, Z., Liu, P., Feng, J., Zhang, Y., Mumtaz, S., & Rodriguez, J. (2019). Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach. IEEE Transactions on Vehicular Technology, 68(4), 3113–3125. https://doi.org/10.1109/TVT.2019.2894851
[15]Zhou, Z., Liao, H., Wang, X., Mumtaz, S., & Rodriguez, J. (2020). When vehicular fog computing meets autonomous driving: Computational resource management and task offloading. IEEE Network, 34, 70–76. https://doi.org/10.1109/MNET.001.1900527
[17]Ning, Z., Dong, P., Wang, X., Guo, L., Rodrigues, J. J. P. C., Kong, X., & Huang, J. (2019). Deep reinforcement learning for intelligent Internet of Vehicles: An energy-efficient computational offloading scheme. IEEE Transactions on Cognitive Communications and Networking, 5, 1060–1072. https://doi.org/10.1109/TCCN.2019.2930521
[18]Zhou, S., Sun, Y., Jiang, Z., & Niu, Z. (2019). Exploiting moving intelligence: Delay-optimized computation offloading in vehicular fog networks. IEEE Communications Magazine, 57, 49–55. https://doi.org/10.1109/MCOM.2019.1800230
[19]Ouyang, T., Zhou, Z., & Chen, X. (2018). Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing. IEEE Journal on Selected Areas in Communications, 36(10), 2333–2345. https://doi.org/10.1109/JSAC.2018.2869954
[20]Alameddine, H., Assem, H., Sharafeddine, S., Sebbah, S., & Ayoubi, S. (2019). Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing. IEEE Journal on Selected Areas in Communications, 37(3), 668–682. https://doi.org/10.1109/JSAC.2019.2894306
[21]Huang, C., Lu, R., & Choo, K. K. R. (2019). Vehicular fog computing: Architecture, use case, and security and forensic challenges. IEEE Communications Magazine, 57, 105–111. https://doi.org/10.1109/MCOM.2017.1700322
Haghighizadeh,R and Zali,Z . (2025). Resource Management in Vehicular Fog Networks Based on Contract Theory. Transactions on Machine Intelligence, 8(2), 69-86. doi: 10.22034/tmi.2025.244841
MLA
Haghighizadeh,R , and Zali,Z . "Resource Management in Vehicular Fog Networks Based on Contract Theory", Transactions on Machine Intelligence, 8, 2, 2025, 69-86. doi: 10.22034/tmi.2025.244841
HARVARD
Haghighizadeh R, Zali Z. (2025). 'Resource Management in Vehicular Fog Networks Based on Contract Theory', Transactions on Machine Intelligence, 8(2), pp. 69-86. doi: 10.22034/tmi.2025.244841
CHICAGO
R Haghighizadeh and Z Zali, "Resource Management in Vehicular Fog Networks Based on Contract Theory," Transactions on Machine Intelligence, 8 2 (2025): 69-86, doi: 10.22034/tmi.2025.244841
VANCOUVER
Haghighizadeh R, Zali Z. Resource Management in Vehicular Fog Networks Based on Contract Theory. Trans. Mach. Intell.. 2025;8(2):69-86. doi: 10.22034/tmi.2025.244841
