A Scalable Architecture for Monitoring IoT Devices Using Ethereum and Fog Computing

TL;DR

This paper presents a scalable, decentralized IoT device monitoring architecture combining Ethereum blockchain and fog computing, addressing security, privacy, and scalability issues in traditional centralized systems.

Contribution

It introduces a novel blockchain-based architecture utilizing Ethereum and fog computing for scalable, secure IoT device monitoring, with a proof-of-concept prototype demonstrating feasibility.

Findings

The architecture is highly scalable for large IoT networks.

The solution is compatible with resource-constrained IoT devices.

Evaluation shows improved security and reduced latency.

Abstract

With the recent considerable developments in the Internet of Things (IoT), billions of resource-constrained devices are interconnected through the internet. Monitoring this huge number of IoT devices that are heterogeneous in terms of underlying communication protocols and data format is challenging. The majority of existing IoT device monitoring solutions heavily rely on centralized architectures. Since using centralized architectures comes at the expense of trusting an authority, it has several inherent drawbacks, including vulnerability to security attacks, lack of data privacy, and unauthorized data manipulation. Hence, a new decentralized approach is crucial to remedy these drawbacks. One of the most promising technologies which is widely used to provide decentralization is blockchain. Additionally, to ease the burden of communication overhead and computational power on resource-constrained IoT devices, fog computing can be exploited to decrease communication latency and provide better network scalability. In this paper, we propose a scalable blockchain-based architecture for monitoring IoT devices using fog computing. To demonstrate the feasibility and usability of the proposed solution, we have implemented a proof-of-concept prototype, leveraging Ethereum smart contracts. Finally, a comprehensive evaluation is conducted. The evaluation results indicate that the proposed solution is significantly scalable and compatible with resource-constrained IoT devices.