A controllable privacy data transmission mechanism for Internet of things system based on blockchain. (March 2022)

• Record Type: Journal Article
• Authors: Nie, ZiXiang; Long, YuanZhenTai; Zhang, SenLin; Lu, YueMing
• Is Part Of: International journal of distributed sensor networks. Volume 18:Number 3(2022)
• Publication Date: 2022-03
• Subjects: Controllable data transmission -- Internet of things privacy data protection -- consortium blockchain -- data traceability; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501329221088450
• Languages: English
• ISSNs: 1550-1329

Abstract

With the in-depth integration of traditional industries and information technology in Internet of things, wireless sensor networks are used more frequently to transmit the data generated from various application scenarios. Structural health monitoring is a scene that requires recurrent data transmission in Internet of things, and the wireless sensor networks in Internet of things not only have storage and communication capabilities, but also have computing capabilities. Therefore, the demand for intelligent and decentralized data exchange between them has increased significantly which brings challenges with respect to low data reliability, chaotic data circulation, provenance tracking, and data accountability investigating, threatening the data security of structural health monitoring in Internet of things utilization. In this article, we propose a controllable data transmission mechanism based on the consortium blockchain to content the requirements of the Internet of things scenario. We identify a version-based, fine-grained, and privacy-protected data structure and propose the corresponding smart contracts for our mechanism to ensure the trusted data transmission. To prove the feasibility of our mechanism, a prototype system is implemented based on the Hyperledger Fabric, an open-source consortium blockchain framework. Our experimental results show in practice the usability and scalability of the approach in this article.