A Data Set Accuracy Weighted Random Forest Algorithm for IoT Fault Detection Based on Edge Computing and Blockchain

Wenbo Zhang, Jiaxing Wang, Guangjie Han, Shuqiang Huang, Yongxin Feng, Lei Shu

2020IEEE Internet of Things Journal38 citationsDOI

Abstract

The continuously increasing number of connected smart devices has led to the emergence of a crucial fault detection challenge to the Internet of Things (IoT). In this study, we aim to identify a method for the effective detection of faults in IoT devices. An IoT network model is first established, and a data edge verification mechanism based on blockchain is proposed; the blockchain is used to ensure that the data cannot be tampered with, and their accuracy is verified using the edge. Finally, a data set accuracy weighted random forest based on particle swarm optimization is proposed. The simulation results demonstrate that the proposed detection algorithm is both effective and efficient.

Topics & Concepts

Computer scienceInternet of ThingsBlockchainEnhanced Data Rates for GSM EvolutionRandom forestParticle swarm optimizationEdge computingSet (abstract data type)Data miningData setFault detection and isolationFault (geology)AlgorithmArtificial intelligenceEmbedded systemComputer securityGeologySeismologyProgramming languageActuatorIoT and Edge/Fog ComputingNetwork Security and Intrusion DetectionAnomaly Detection Techniques and Applications