Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach

By Zhenyu Zhou, Pengju Liu, Junhao Feng, Yan Zhang, Shahid Mumtaz, Jonathan Rodriguez. 2019

319 citations recorded by the source corpus.

Vehicular fog computing (VFC) has emerged as a promising solution to relieve the overload on the base station and reduce the processing delay during the peak time. The computation tasks can be offloaded from the base station to vehicular fog nodes by leveraging the under-utilized computation resources of nearby vehicles. However, the wide-area deployment of VFC still confronts several critical challenges, such as the lack of efficient incentive and task assignment mechanisms. In this paper, we address the above challenges and provide a solution to minimize the network delay from a contract-matching integration perspective. First, we propose an efficient incentive mechanism based on contract theoretical modeling. The contract is tailored for the unique characteristic of each vehicle type to maximize the expected utility of the base station. Next, we transform the task assignment problem into a two-sided matching problem between vehicles and user equipment. The formulated problem is solved by a pricing-based stable matching algorithm, which iteratively carries out the “propose” and “price-rising” procedures to derive a stable matching based on the dynamically updated preference lists. Finally, numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

Subfields: Automotive Engineering, Computer Networks and Communications, Information Systems

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Citations in the corpus

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, background): “Each data owner chooses its desired contract item to maximize its profit [10].”
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, background): “Each worker chooses its desired contract item to maximize its profit [6].”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, methodology): “While the study of contract-matching has also been explored for resource allocation in vehicular fog computing [47], the contract considered is single-dimensional with simpler considerations.”
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning (2020, unknown): “Moreover, clusters of end devices, e.g., stationary vehicles [10], can potentially serve as virtual edge servers for device-to-device (D2D) computation ofﬂoading [1].”
Blockchain for Internet of Things: A Survey (2019, background): “For example, [67] presents a contract-match approach to allocate computational resource and assign tasks”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “A similar study was carried in [119], where vehicles act as”