Priority and Stackelberg Game-Based Incentive Task Allocation for Device-Assisted MEC Networks

TL;DR

This paper proposes a game-theoretic incentive scheme for task allocation in device-assisted MEC networks, improving resource utilization and cooperation among MEC servers, IoT devices, and auxiliary devices.

Contribution

It introduces a novel combination of Vickrey auction and Stackelberg game for incentive-driven task offloading in MEC, optimizing service pricing and resource allocation.

Findings

Significant utility improvement for MEC servers.

Enhanced cooperation among IoT devices and auxiliary devices.

Achieved a balanced, triple-win scenario for all parties.

Abstract

Mobile edge computing (MEC) is a promising computing paradigm that offers users proximity and instant computing services for various applications, and it has become an essential component of the Internet of Things (IoT). However, as compute-intensive services continue to emerge and the number of IoT devices explodes, MEC servers are confronted with resource limitations. In this work, we investigate a task-offloading framework for device-assisted edge computing, which allows MEC servers to assign certain tasks to auxiliary IoT devices (ADs) for processing. To facilitate efficient collaboration among task IoT devices (TDs), the MEC server, and ADs, we propose an incentive-driven pricing and task allocation scheme. Initially, the MEC server employs the Vickrey auction mechanism to recruit ADs. Subsequently, based on the Stackelberg game, we analyze the interactions between TDs and the MEC server. Finally, we establish the optimal service pricing and task allocation strategy, guided by the Stackelberg model and priority settings. Simulation results show that the proposed scheme dramatically improves the utility of the MEC server while safeguarding the interests of TDs and ADs, achieving a triple-win scenario.