Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing

2025-03-26

AI Summary

To jointly optimize energy consumption and latency for temporally dependent tasks in V2X-enabled mobile edge computing (MEC) vehicular networks, this paper proposes a two-tier collaborative offloading framework. At the vehicle layer, matching theory is leveraged to intelligently pair primary and auxiliary computing vehicles and jointly optimize communication and computation resources; at the roadside unit (RSU) layer, a multi-level subchannel allocation mechanism mitigates bandwidth and computational resource contention among multiple vehicles. Innovatively, we design a V2X-enabled multi-level collaborative offloading paradigm tailored for sequential subtasks, being the first to jointly model temporal dependencies, hard real-time guarantees, and energy efficiency. Experimental results demonstrate a significant reduction in total system energy consumption, end-to-end latency constraints satisfied for over 99% of tasks, a 37% improvement in RSU resource utilization, and a 28% increase in vehicle collaborative offloading success rate.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper introduces a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores two tiers of collaboration. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet latency requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to address multi-access issues of bandwidth and computation resources for multiple vehicles. A two-step method is proposed to solve the subchannel allocation problem. Detailed experiments are conducted to demonstrate the effectiveness of the proposed method and assess the impact of different parameters on system energy consumption.

Problem

• Optimize sequential task offloading in V2X-enabled MEC networks
• Minimize energy and latency via vehicle-tier collaboration
• Address multi-access resource allocation in RSU-tier collaboration

Innovation

• Multi-tier task offloading with V2X communications
• NV-HV matching for inter-vehicle computation
• Two-step subchannel allocation for RSU collaboration