QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks
Yongtao Yao, Yao Yang, Haorui Shi, Canglu Zhu, Miaojiang Chen, Ahmed Farouk

TL;DR
This paper introduces QAROO, an AI-driven online task offloading framework for MEC networks that enhances adaptability and efficiency using quantum neural networks and attention mechanisms.
Contribution
It proposes a novel quantum attention-based reinforcement learning framework for energy-efficient online task offloading in dynamic MEC environments.
Findings
Outperforms existing schemes in computation speed and processing time.
Enhances adaptability and exploration efficiency in IoT environments.
Provides a stable and efficient solution for large-scale MEC networks.
Abstract
With the rapid advancement of artificial intelligence (AI) and intelligent science, intelligent edge computing has been widely adopted. However, the limitations of traditional methods, such as poor adaptability and the slow convergence of heuristic algorithms, are becoming increasingly evident. To enable sustainable and resource-efficient edge applications, this paper proposes an online task offloading framework for wireless powered mobile edge computing (MEC) networks, called Quantum Attention-based Reinforcement learning for Online Offloading (QAROO). The system employs a binary offloading strategy with the aim of co-optimizing computing and energy resources in dynamic channel environments. In response to the issues of poor adaptability in traditional approaches and the slow convergence of heuristic algorithms, the framework integrates quantum neural networks and attention mechanisms,…
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