Work

Reputation-Aware Multi-Agent Cooperative Offloading Mechanism for Vehicular Network Attack Scenarios

Year: 2025

Type: article

Abstract: The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading.

Source: Vehicles

DOI: 10.3390/vehicles7040150

Authors: Liping Ye, Na Fan, Junhui Zhang, Yexiong Shang, Yu Shi, Wenjun Fan

Institutions: Chang'an University, Xi’an Jiaotong-Liverpool University

Language: English

Topic: Vehicular Ad Hoc Networks (VANETs)

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930