Joint Task Offloading and User Scheduling in 5G MEC under Jamming Attacks

TL;DR

This paper introduces a joint task offloading and user scheduling framework for 5G MEC systems that effectively mitigates jamming attacks, reducing task drop ratios and prioritizing critical tasks.

Contribution

It presents a novel optimization framework using genetic algorithms to enhance task scheduling under jamming threats in 5G MEC environments.

Findings

Achieves the lowest task drop ratio compared to benchmarks.

Effectively manages high-priority tasks during jamming.

Reduces drop ratio to 63% at 0.8 jamming probability.

Abstract

In this paper, we propose a novel joint task offloading and user scheduling (JTO-US) framework for 5G mobile edge computing (MEC) systems under security threats from jamming attacks. The goal is to minimize the delay and the ratio of dropped tasks, taking into account both communication and computation delays. The system model includes a 5G network equipped with MEC servers and an adversarial on-off jammer that disrupts communication. The proposed framework optimally schedules tasks and users to minimize the impact of jamming while ensuring that high-priority tasks are processed efficiently. Genetic algorithm (GA) is used to solve the optimization problem, and the results are compared with benchmark methods such as GA without considering jamming effect, Shortest Job First (SJF), and Shortest Deadline First (SDF). The simulation results demonstrate that the proposed JTO-US framework achieves the lowest drop ratio and effectively manages priority tasks, outperforming existing methods. Particularly, when the jamming probability is 0.8, the proposed framework mitigates the jammer's impact by reducing the drop ratio to 63%, compared to 89% achieved by the next best method.