Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

Abstract

With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit.In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.

Contract-Optimization Approach (COA): A New Approach for Optimizing Service Caching, Computation Offloading, and Resource Allocation in Mobile Edge Computing Network

“…Furthermore, the numerical results indicate that the proposed mechanism outperforms other baseline incentive mechanisms and significantly enhances the utility of the MEC server. These baseline incentive mechanisms are contract-based incentive mechanism under symmetric information scenario (CS), Stackelberg game-based incentive mechanism (SG) [ 43 ], and linear pricing incentive mechanism (LP). CS considers the scenario where the MEC server knows the types of preference of each MT.…”

Stackelberg Game-Based Resource Allocation with Blockchain for Cold-Chain Logistics System

“…Shi et al utilized the Stackelberg game to guide a deep reinforcement learning approach that could allow multi-agent device-to-device resource allocation [33]. Li et al proposed a vehicular paradigm based on fog-edge computing and formulated a multistage Stackelberg game for offloading computing tasks [34]. Liu et al presented a Stackelberg game method that depends on the pricepenalty mechanism to allocate device-to-device resources in a vehicular communication system [35].…”

Efficient Anchor Point Deployment for Low Latency Connectivity in MEC-Assisted C-V2X Scenarios

“…They implement a proof of concept based on a 5G network architecture that dynamically and proactively populates video chunks in MEC hosts based on mobility predictions for improving cache hit ratios. The work in [26] studies joint offloading and resource allocation decisions in vehicular fog-edge scenarios. It formulates the offloading of computing tasks involving vehicles, Road-Side Units (RSUs) and MEC servers as a Stackelberg game and propose incentive mechanisms to motivate vehicles to share their idle resources.…”