Intent-Driven DRL-Based Resource Allocation for RIS-Assisted Wireless-Powered IoT Edge Networks
Liu, Tong Zhang, Shilong Chen, Jinhua AlFarraj, Osama Abdulaziz Yu, Keping Guizani, Mohsen
Liu, Tong
Zhang, Shilong
Chen, Jinhua
AlFarraj, Osama Abdulaziz
Yu, Keping
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Abstract
The rapid proliferation of the Industrial Internet of Things (IoT), particularly in smart manufacturing, necessitates intent-driven edge intelligence to provide highly reliable, low-latency computational services for a vast array of connected devices. However, inherent challenges such as the limited computational capacity of IoT devices and unpredictable wireless channels hinder the development of such networks. To address these obstacles, this paper presents an intent-driven optimization framework that integrates reconfigurable intelligent surface (RIS) and wireless power transfer (WPT) into an IoT edge computing architecture. Following the principles of intent-based networking (IBN), our framework targets binary task offloading for energy-constrained devices by translating the specific high-level intent of maximizing the total computational rate into a concrete system-level objective. We formulate this objective as a mixed-integer nonlinear programming problem and propose a Deep Reinforcement Integrated Optimization (DRIO) framework to solve it under dynamic network conditions. DRIO effectively decouples the high-dimensional action space, employing a double deep Q-network for coarse RIS phase control, a combination of gradient search and element-wise local search for fine-tuning RIS phases, and a dedicated deep reinforcement learning component for managing binary offloading decisions. Simulation results confirm that DRIO surpasses traditional and standard reinforcement learning methods by successfully fulfilling the intent of maximizing computational rate, showing adaptability to dynamic environments and energy harvesting fluctuations. This study enhances IBN for industrial IoT through AI-powered resource allocation, thereby boosting scalability and efficiency in next-generation communications.
Citation
T. Liu, S. Zhang, J. Chen, O. Alfarraj, K. Yu and M. Guizani, "Intent-Driven DRL-Based Resource Allocation for RIS-Assisted Wireless-Powered IoT Edge Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3635210.
Source
IEEE Internet of Things Journal
Conference
Keywords
Binary offloading, Deep reinforcement learning, Edge computing, Intent-driven optimization, IoT communication, Reconfigurable intelligent surface
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Source
Publisher
IEEE