论文标题
智能区块链通过深度强化学习:解决方案和挑战
Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges
论文作者
论文摘要
移动边缘计算(MEC)和区块链的融合正在通过基于区块链挖掘的安全性增强功能使任务卸载,从而在无线互联网网络中转换了当前的计算服务。然而,这些促成技术的现有方法是孤立的,仅提供针对特定服务和方案的量身定制解决方案。为了填补这一空白,我们为基于区块链的MEC系统提出了一个新颖的合作任务卸载和区块链挖掘(TOBM)方案,其中每个Edge设备不仅处理计算任务,而且还处理用于改善系统实用程序的块挖掘。为了解决MEC区块链操作引起的延迟问题,我们基于轻量级区块验证策略开发了一种新的指标证明共识机制。为了适应高度动态的环境和高维系统状态空间,我们通过使用多代理的深层确定性策略梯度算法应用了一种新颖的分布式强化学习方法。实验结果表明,与现有的合作社和非合件方案相比,在增强系统奖励,通过较低区块链采矿潜伏期和更好的系统效用方面,提出的TOBM方案的卓越性能,改善了卸载效用以及更好的系统效用。本文以关键的技术挑战和未来基于区块链的MEC研究的可能方向结束。
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 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 paper concludes with key technical challenges and possible directions for future blockchain-based MEC research.