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.

翻译：移动边缘计算（MEC）是一种前景广阔的计算范式，能为各类应用提供近距离、即时性的计算服务，已成为物联网（IoT）的重要组成部分。然而，随着计算密集型服务不断涌现以及物联网设备数量激增，MEC服务器面临着资源限制的挑战。本文研究了一种设备辅助边缘计算的任务卸载框架，该框架允许MEC服务器将特定任务分配给辅助物联网设备（AD）进行处理。为促进任务物联网设备（TD）、MEC服务器与AD之间的高效协作，我们提出了一种激励驱动的定价与任务分配方案。首先，MEC服务器采用Vickrey拍卖机制招募AD。随后，基于Stackelberg博弈，我们分析了TD与MEC服务器之间的交互过程。最后，在Stackelberg模型与优先级设置的指导下，我们建立了最优的服务定价与任务分配策略。仿真结果表明，所提方案在保障TD与AD利益的同时，显著提升了MEC服务器的效用，实现了三方共赢。