| dc.description.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. | en_US |
