Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges
Dinh C. Nguyen, Van-Dinh Nguyen, Ming Ding, Symeon Chatzinotas, Pubudu, N. Pathirana, Aruna Seneviratne, Octavia Dobre, Albert Y. Zomaya

TL;DR
This paper introduces a novel cooperative scheme for blockchain-based mobile edge computing that leverages deep reinforcement learning to optimize task offloading and blockchain mining, addressing latency and system utility challenges.
Contribution
It proposes a new TOBM scheme integrating task offloading and blockchain mining with a lightweight consensus mechanism and a multi-agent deep reinforcement learning approach for dynamic environments.
Findings
Enhanced system reward and utility compared to existing schemes
Lower blockchain mining latency in MEC environments
Effective adaptation to dynamic system states
Abstract
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by 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 deals with 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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · IoT and Edge/Fog Computing · Privacy-Preserving Technologies in Data
