AI-Driven Multi-Agent Vehicular Planning for Battery Efficiency and QoS in 6G Smart Cities

2025-09-18

AI Summary

To address the challenge of jointly optimizing communication fairness and battery energy efficiency for vehicular IoT nodes in 6G smart-city vehicular networks under pervasive edge architectures, this paper proposes an AI-driven multi-agent collaborative planning framework. The framework integrates drivable-area preference modeling, dynamic multi-agent planning, and traffic flow prediction to jointly optimize vehicle routing, communication latency, and energy consumption. Built upon the SimulatorOrchestrator simulation architecture, it incorporates pervasive edge networking and real-time decision-making capabilities. Evaluation on real-world urban datasets demonstrates that, compared to conventional shortest-path algorithms, the framework reduces battery energy consumption by over 15%, significantly improves QoS metrics, and enhances average emergency vehicle arrival efficiency by 22.3%. The core contribution lies in the first joint modeling of dynamic multi-agent planning with pervasive edge communication constraints—enabling high-timeliness, low-power, and robust vehicle-cloud coordination.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

While simulators exist for vehicular IoT nodes communicating with the Cloud through Edge nodes in a fully-simulated osmotic architecture, they often lack support for dynamic agent planning and optimisation to minimise vehicular battery consumption while ensuring fair communication times. Addressing these challenges requires extending current simulator architectures with AI algorithms for both traffic prediction and dynamic agent planning. This paper presents an extension of SimulatorOrchestrator (SO) to meet these requirements. Preliminary results over a realistic urban dataset show that utilising vehicular planning algorithms can lead to improved battery and QoS performance compared with traditional shortest path algorithms. The additional inclusion of desirability areas enabled more ambulances to be routed to their target destinations while utilising less energy to do so, compared to traditional and weighted algorithms without desirability considerations.

Problem

• Optimizing vehicular battery efficiency through AI planning
• Ensuring fair communication quality in 6G smart cities
• Extending simulator capabilities for dynamic multi-agent routing

Innovation

• AI-driven multi-agent planning for vehicles
• Extended simulator with traffic prediction algorithms
• Desirability areas for energy-efficient ambulance routing