Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency

2025-04-29

AI Summary

To address high latency, energy consumption, and task failure rates in task offloading under dynamic vehicular ad hoc networks (VANETs), this paper proposes a three-tier collaborative intelligent offloading framework integrating supervised learning, deep reinforcement learning (DRL), and particle swarm optimization (PSO). The method introduces a novel “supervised prediction—reinforcement-based adaptation—PSO joint optimization” mechanism to unify real-time decision-making, robustness, and multi-objective Pareto-optimality. It jointly leverages LSTM/XGBoost for time-series task and channel prediction, DQN for dynamic policy learning, and channel-aware mobile edge computing (MEC) resource modeling. Simulation results demonstrate significant improvements: average latency reduced by 42.7%, energy consumption decreased by 38.5%, task success rate increased to 99.2%, and network throughput enhanced by 31.4%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Vehicular Ad-hoc Networks (VANETs) are integral to intelligent transportation systems, enabling vehicles to offload computational tasks to nearby roadside units (RSUs) and mobile edge computing (MEC) servers for real-time processing. However, the highly dynamic nature of VANETs introduces challenges, such as unpredictable network conditions, high latency, energy inefficiency, and task failure. This research addresses these issues by proposing a hybrid AI framework that integrates supervised learning, reinforcement learning, and Particle Swarm Optimization (PSO) for intelligent task offloading and resource allocation. The framework leverages supervised models for predicting optimal offloading strategies, reinforcement learning for adaptive decision-making, and PSO for optimizing latency and energy consumption. Extensive simulations demonstrate that the proposed framework achieves significant reductions in latency and energy usage while improving task success rates and network throughput. By offering an efficient, and scalable solution, this framework sets the foundation for enhancing real-time applications in dynamic vehicular environments.

Problem

• Optimizing task offloading in VANETs for low latency
• Reducing energy consumption in dynamic vehicular networks
• Improving task success rates with hybrid AI

Innovation

• Hybrid AI integrates supervised and reinforcement learning
• Particle Swarm Optimization reduces latency and energy
• Dynamic task offloading enhances vehicular network efficiency