Wireless Power Transfer and Intent-Driven Network Optimization in AAVs-assisted IoT for 6G Sustainable Connectivity

2025-11-23

AI Summary

This paper addresses the reliability challenges of user-intent prediction and ultra-low-latency execution in AAV-assisted IoT for sustainable 6G connectivity, particularly under high-dimensional action sequences. To tackle these issues, we propose an intent-driven cooperative optimization framework: (1) an implicit intent modeling approach coupled with a Hyper-Dimensional Transformer (HDT) for accurate prediction of high-dimensional action sequences; and (2) a Dual-Action Multi-Agent Proximal Policy Optimization (DA-MAPPO) algorithm that explicitly captures action dependencies between intent and trajectory. The framework integrates key enablers—including hyper-dimensional vector encoding, symbolic hypercomputation, and wireless energy transfer. Evaluated on real-world IoT datasets, HDT and DA-MAPPO achieve significant improvements over state-of-the-art methods: +12.7% in intent prediction accuracy and −38.5% reduction in network response latency.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Autonomous Aerial Vehicle (AAV)-assisted Internet of Things (IoT) represents a collaborative architecture in which AAV allocate resources over 6G links to jointly enhance user-intent interpretation and overall network performance. Owing to this mutual dependence, improvements in intent inference and policy decisions on one component reinforce the efficiency of others, making highly reliable intent prediction and low-latency action execution essential. Although numerous approaches can model intent relationships, they encounter severe obstacles when scaling to high-dimensional action sequences and managing intensive on-board computation. We propose an Intent-Driven Framework for Autonomous Network Optimization comprising prediction and decision modules. First, implicit intent modeling is adopted to mitigate inaccuracies arising from ambiguous user expressions. For prediction, we introduce Hyperdimensional Transformer (HDT), which embeds data into a Hyperdimensional space via Hyperdimensional vector encoding and replaces standard matrix and attention operations with symbolic Hyperdimensional computations. For decision-making, where AAV must respond to user intent while planning trajectories, we design Double Actions based Multi-Agent Proximal Policy Optimization (DA-MAPPO). Building upon MAPPO, it samples actions through two independently parameterized networks and cascades the user-intent network into the trajectory network to maintain action dependencies. We evaluate our framework on a real IoT action dataset with authentic wireless data. Experimental results demonstrate that HDT and DA-MAPPO achieve superior performance across diverse scenarios.

Problem

• Modeling high-dimensional action sequences in AAV-assisted IoT networks
• Managing intensive on-board computation for intent-driven optimization
• Achieving reliable intent prediction with low-latency execution in 6G

Innovation

• Hyperdimensional Transformer for intent prediction
• Double Actions MAPPO for trajectory planning
• Symbolic hyperdimensional computations replace attention operations