Performance Improvement of Blockchain-based IoT Applications using Deep Learning Techniques

Abstract

Internet of Things (IoT) deployments have increased drastically based on third-party (fog-assisted architecture) mechanisms to store, process, and share sensor data. IoT environments are mostly vulnerable to security threats due to the lack of intrinsic security measures. Blockchain technology with an untrusty framework to establish trust communication among IoT devices becomes a major concern in lightweight IoT frameworks. To solve this trust issue, we propose a DeepIoT-Block model that combines the consensual deep learning (CDL) technique using the elliptic Diffihelman protocol to strengthen the blockchain-based data storage scheme (BDSS) and Directed Acyclic Graph (DAG) to construct the blockchain network. DeepIoT-Block has implemented using a blockchain system for IoT applications to address storage security issues. DeepIoT-Block guarantees simultaneous computational complexity and transaction efficiency. The performance of the proposed model was verified and validated for IoT-based smart road traffic data. The simulation outcomes show that our proposed model, DeepIoT-Block, is computationally efficient and secure for larger scale IoT applications.