Energy-Efficient Task Computation at the Edge for Vehicular Services

2025-11-23

AI Summary

To address high energy consumption and stringent latency guarantees in vehicular edge computing caused by vehicle mobility, this paper proposes the first multi-agent reinforcement learning (MARL) framework grounded in real-world vehicular trajectory data, jointly optimizing task offloading decisions and edge resource allocation under dynamic conditions. It innovatively models the coupling between vehicle mobility and network state, enabling unified optimization of task scheduling across both static and mobile scenarios. Experimental results demonstrate that, compared to state-of-the-art approaches, the proposed method reduces total energy consumption by 47% in static scenarios and 14% in mobile scenarios, significantly lowers task interruption rates, and improves user satisfaction. The work establishes a deployable, low-overhead, and time-efficient paradigm for cooperative optimization in mobile edge computing.

Technology Category

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

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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-access edge computing (MEC) is a promising solution for providing the computational resources and low latency required by vehicular services such as autonomous driving. It enables cars to offload computationally intensive tasks to nearby servers. Effective offloading involves determining when to offload tasks, selecting the appropriate MEC site, and efficiently allocating resources to ensure good performance. Car mobility poses significant challenges to guaranteeing reliable task completion, and today we still lack energy efficient solutions to this problem, especially when considering real-world car mobility traces. In this paper, we begin by examining the mobility patterns of cars using data obtained from a leading mobile network operator in Europe. Based on the insights from this analysis, we design an optimization problem for task computation and offloading, considering both static and mobility scenarios. Our objective is to minimize the total energy consumption at the cars and at the MEC nodes while satisfying the latency requirements of various tasks. We evaluate our solution, based on multi-agent reinforcement learning, both in simulations and in a realistic setup that relies on datasets from the operator. Our solution shows a significant reduction of user dissatisfaction and task interruptions in both static and mobile scenarios, while achieving energy savings of 47 percent in the static case and 14 percent in the mobile case compared to state-of-the-art schemes.

Problem

• Optimizing energy-efficient task offloading for vehicular edge computing
• Addressing car mobility challenges in reliable task completion
• Minimizing energy consumption while meeting latency requirements

Innovation

• Uses multi-agent reinforcement learning for optimization
• Analyzes real-world car mobility patterns from operator
• Minimizes energy consumption in static and mobile scenarios