Authors

Ms. Anjana Devi

Gayatri Vidya Parishad College of Engineering, Andhra Pradesh, India

Abstract

The evolution of sixth-generation (6G) wireless communication is expected to enable ultra-reliable, low-latency, and intelligent connectivity for next-generation Internet of Things (IoT) ecosystems. Assistive applications—such as remote healthcare monitoring, smart rehabilitation systems, wearable support devices, and intelligent mobility aids—require highly adaptive and resilient network architectures to ensure uninterrupted service and real-time responsiveness. This research proposes a dynamic and robust 6G IoT framework for assistive applications using graph-augmented learning to enhance network efficiency, scalability, and reliability. The proposed framework models IoT devices, edge nodes, and communication links as graph structures, enabling efficient representation of dynamic network topologies. Graph-based learning mechanisms are integrated with deep reinforcement learning (DRL) to optimize resource allocation, routing decisions, and latency management under varying traffic and mobility conditions. The graph-augmented approach captures spatial and relational dependencies among nodes, improving adaptability to network congestion, interference, and device heterogeneity. The system further incorporates edge intelligence to reduce transmission delays and enhance localized decision-making for time-critical assistive services. Simulation-based evaluation demonstrates improved throughput, reduced latency, enhanced energy efficiency, and higher reliability compared to conventional optimization methods. The results indicate that the proposed framework effectively supports mission-critical assistive applications in 6G-enabled IoT environments. This research contributes to the development of intelligent, resilient, and human-centric wireless infrastructures capable of meeting the demanding requirements of future assistive technologies.

Keywords

Adaptive Cruise Control (ACC) Autonomous Vehicles Raspberry Pi Real-Time Embedded Systems Sensor Integration Ultrasonic Sensor LiDAR Closed-Loop Control System Intelligent Transportation Systems

Citation of this Article

Ms. Anjana Devi. (2025). Dynamic and Robust 6G IoT Framework for Assistive Applications Using Graph-Augmented Learning. Journal of Artificial Intelligence and Emerging Technologies. 2(10), 14-21. Article DOI: https://doi.org/10.47001/JAIET/2025.210002

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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