Blockchain-Based Edge Computing Resource Allocation in IoT: A Deep Reinforcement Learning Approach

TLDR

The rapid expansion of IoT devices has outpaced cloud computing’s ability to provide low latency, high bandwidth, and intelligent services, prompting a shift toward edge‑centric architectures that face significant security and privacy challenges which blockchain technology can address. This work proposes a blockchain‑based framework that uses an A3C‑powered smart contract to allocate edge computing resources for IoT, while outlining the security, privacy, and AI‑blockchain integration benefits. The mechanism involves a private blockchain network where a smart contract, driven by the asynchronous advantage actor‑critic algorithm, orchestrates resource allocation through a defined transaction workflow between IoT devices and edge computing nodes. Simulation results demonstrate the effectiveness of the proposed A3C‑based allocation scheme in improving resource utilization and reducing latency in edge‑centric IoT environments.

Abstract

With the exponential growth in the number of Internet-of-Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low latency, high bandwidth, ease of availability, and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, has been recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes (ECNs). A blockchain, as a decentralized, trustless, and immutable public ledger, can well solve the trust-absence issue. In this article, we first elaborate on the security and privacy issues of edge-computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge-computing-enabled IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an ECN. In addition, we design a smart contract within a private blockchain network that exploits the state-of-the-art machine learning algorithm, asynchronous advantage actor-critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.