Priority and Stackelberg Game-Based Incentive Task Allocation for Device-Assisted MEC Networks
Yang Li, Xing Zhang, Bo Lei, Zheyan Qu, Wenbo Wang
TL;DR
This work tackles resource-limited mobile edge computing by enabling device-assisted MEC with incentive design. It combines a Vickrey auction to recruit auxiliary devices and a Stackelberg game to optimize pricing and task allocation between the edge server and task devices, guided by priority-based decisions. The proposed framework yields a significant increase in edge-server utility while sustaining the interests of TDs and ADs, evidenced by simulation results that show notable gains over baseline strategies. The approach enhances MEC scalability for IoT, with potential extensions to multi-cell deployments and cross-cell collaboration.
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.