Intelligent Blockchain-Based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

【Author】 Nguyen, Dinh C.; Nguyen, Van-Dinh; Ding, Ming; Chatzinotas, Symeon; Pathirana, Pubudu N.; Seneviratne, Aruna; Dobre, Octavia; Zomaya, Albert Y.

【Source】IEEE NETWORK

【影响因子】10.294

【Abstract】The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things (IoT) networks, enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for these enabling technologies are isolated, providing only tailored solutions for specific services and scenarios. To fill this gap, we propose a novel cooperative task offloading and blockchain mining (TOBM) scheme for a blockchain-based MEC system, where each edge device not only handles computation tasks but also conducts block mining for improving system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. To accommodate the highly dynamic environment and high-dimensional system state space, we apply a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. Experimental results demonstrate the superior performance of the proposed TOBM scheme in terms of enhanced system reward, improved offloading utility with lower blockchain mining latency, and better system utility, compared to the existing cooperative and non-cooperative schemes. The article concludes with key technical challenges and possible directions for future blockchain-based MEC research.

【Keywords】Task analysis; Blockchains; Data mining; Servers; Quality of experience; Optimization; Resource management

【发表时间】2022 NOV-DEC

【收录时间】2023-07-16

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-边缘计算