Towards a blockchain-based trustful mechanism for IoT-enabled data trading systems

Abstract

Internet of Things (IoT) devices generate and collect massive amounts of IoT data. Monetizing the flood of data generated by the IoT devices has enabled the creation of IoT data trading systems where individuals and businesses may trade data. In the current IoT data trading systems, a third-party broker collects and manages IoT data for buyers who would like to promote their services and make more profit. However, there are three main challenges that may hinder the development of secure IoT data trading systems. First, there is a lack of data transparency and ownership. While the economic value of IoT data is increasing, it is not very well known how this data can be conceptualized, measured, and monetized in a trusted and transparent way. Second, the literature lacks studies about performance models to demonstrate IoT data trading system usability in real-world systems. Third, the reputation of the trading parties is an important attribute that affects their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation and single point of failure. This thesis identifies and addresses the three above challenges for IoT data trading systems. First, this thesis introduces a trustful IoT data trading system based on the blockchain as a means of providing anonymity, security, transparency, and mutual trust for participants. Using a game-theoretic approach, this study develops a strategic negotiation model that maximizes data buyers’ utility. To ensure that data owners’ IoT data are accessible by trustful buyers, a novel mechanism design is used to impede untruthful buyers from accessing the IoT data. Second, this thesis evaluates the performance of the blockchain-based IoT data trading system using the Hyperledger blockchain. Unlike existing research, this study measures and analyzes transaction throughput, latency, elapsed time, and resource consumption (memory consumption, CPU utilization, and disc read/write operations). Third, this thesis proposes a blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. This thesis also proposes an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. Lastly, this thesis carries out extensive theoretical analysis with respect to economic and security properties.