Journal ArticleDOI
Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach
TLDR
This paper proposes an efficient incentive mechanism based on contract theoretical modeling to minimize the network delay from a contract-matching integration perspective and demonstrates that significant performance improvement can be achieved by the proposed scheme.Abstract:
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.read more
Citations
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Blockchain for Internet of Things: A Survey
TL;DR: An in-depth survey of BCoT is presented and the insights of this new paradigm are discussed and the open research directions in this promising area are outlined.
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Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory
TL;DR: This article introduces reputation as the metric to measure the reliability and trustworthiness of the mobile devices, then designs a reputation-based worker selection scheme for reliable federated learning by using a multiweight subjective logic model and leverages the blockchain to achieve secure reputation management for workers with nonrepudiation and tamper-resistance properties.
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Mobile Edge Intelligence and Computing for the Internet of Vehicles
Jun Zhang,Khaled Ben Letaief +1 more
TL;DR: Key design issues, methodologies, and hardware platforms are introduced, including edge-assisted perception, mapping, and localization for intelligent IoV, and typical use cases for intelligent vehicles are illustrated.
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Blockchain for Internet of Things: A Survey
TL;DR: In this paper, the authors investigate the integration of blockchain technology with IoT and investigate the issues about using blockchain for 5G beyond in IoT as well as industrial applications of BCoT.
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Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
TL;DR: This work adopts a deep Q-learning approach for designing optimal offloading schemes and proposes an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure and evaluates the proposed schemes based on real traffic data.
References
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Book
Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis
Alvin E. Roth,Marilda Sotomayor +1 more
TL;DR: The marriage model and the labor market for medical interns, a simple model of one seller and many buyers, and Discrete models with money, and more complex preferences are examined.
Journal ArticleDOI
Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures
TL;DR: An interesting relationship among the communication capability, connectivity, and mobility of vehicles is unveiled, and the characteristics about the pattern of parking behavior are found, which benefits from the understanding of utilizing the vehicular resources.
Journal ArticleDOI
Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading
TL;DR: A cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks is proposed, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions, which greatly reduces the cost of computation and improves task transmission efficiency.
Journal ArticleDOI
Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching
TL;DR: In this paper, the authors proposed a joint optimization framework for all the nodes, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion, where a Stackelberg game was formulated to analyze the pricing problem for the DSO and the resource allocation problem for DSS.
Journal ArticleDOI
Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges
TL;DR: This article formalizes the vehicular fog computing architecture and presents a typical use case in vehicular Fog Computing, and discusses several key security and forensic challenges and potential solutions.