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Ph. D. Defense: Gang Wang

September 18 @ 1:00 pm - 3:00 pm EDT

Title: Towards Blockchain-based Industrial IoT Platform

Ph.D. Candidate: Gang Wang

Major Advisor: Prof. Song Han 

Associate Advisors: Prof. Zhijie Jerry Shi and Prof. Shengli Zhou

Date/Time:  Friday, September 18th, 2020, 1:00 pm – 3:00 pm


Meeting link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m05464faf205de01b020574922df7a6ab

Password: MtR5bEpdi57

Join by phone: +1-415-655-0002 US Toll

Access code: 120 207 0408


The fast-developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Blockchain technology offers many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. When integrating blockchain into IIoT platforms, it encounters many critical challenges inherent in IIoT and blockchain themselves. In this dissertation, we propose several novel schemes to integrate blockchain into industrial IoT platforms. 

In the first part of the dissertation, we propose an architecture, called ChainSpiltter, partitioning the IIoT infrastructure into three layers: local IIoT networks, the blockchain overlay network, and the cloud. To address the storage challenges, ChainSplitter proposes to store the blocks in a hierarchical manner: the majority of the blockchain is stored in the cloud to leverage its abundant storage capacity, while the most recent blocks are stored in overlay network of the individual local IIoT networks. 

In the second part of the dissertation, we propose a novel blockchain structure, SMChain, which is devised specifically to meet data immutability and trustworthiness among industrial plants. SMChain adopts a two-layer blockchain design to fulfill the industrial scenarios, leveraging the local chains that form independently within their own plants. Each plant is responsible for its own local chain without sharing information with other plants, and these local chains together form a state chain. 

In the third part of the dissertation, we continue to improve the scalable BFT protocol with a detection mechanism, called DetBFT.  DetBFT has three phases consensus, detection, and view-change, extending the two-phase process in traditional BFT protocols. DetBFT partitions replicas into two committees, active committee and passive committee, and only replicas in active committee can participate in the consensus process.  By leveraging threshold encryption and message aggregation verifiers, the consensus process achieves a linear communication complexity among active replicas. With the Byzantine detection mechanism, faulty functional replicas are identified and moved to the passive committee. Besides, DetBFT utilizes a Verifiable Random Function (VRF), instead of the round-robin method, to elect new leaders.


September 18
1:00 pm - 3:00 pm EDT

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