Incentive-Driven Task Offloading and Collaborative Computing in Device-Assisted MEC Networks

TL;DR

This paper proposes an incentive-driven, multi-level task offloading framework for device-assisted MEC networks, improving task processing efficiency and resource utilization in IoT environments.

Contribution

It introduces a novel incentive mechanism combining bargaining game and double auction for collaborative task offloading among heterogeneous IoT devices and edge servers.

Findings

Outperforms benchmark methods in simulations

Enhances resource utilization and task processing efficiency

Addresses uneven task distribution with a priority-based scheduling algorithm

Abstract

Edge computing (EC), positioned near end devices, holds significant potential for delivering low-latency, energy-efficient, and secure services. This makes it a crucial component of the Internet of Things (IoT). However, the increasing number of IoT devices and emerging services place tremendous pressure on edge servers (ESs). To better handle dynamically arriving heterogeneous tasks, ESs and IoT devices with idle resources can collaborate in processing tasks. Considering the selfishness and heterogeneity of IoT devices and ESs, we propose an incentive-driven multi-level task allocation framework. Specifically, we categorize IoT devices into task IoT devices (TDs), which generate tasks, and auxiliary IoT devices (ADs), which have idle resources. We use a bargaining game to determine the initial offloading decision and the payment fee for each TD, as well as a double auction to incentivize ADs to participate in task processing. Additionally, we develop a priority-based inter-cell task scheduling algorithm to address the uneven distribution of user tasks across different cells. Finally, we theoretically analyze the performance of the proposed framework. Simulation results demonstrate that our proposed framework outperforms benchmark methods.