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Decentralized Data Management Schemes for IoT Blockchain
- Alternative Title
- Minkyung Kim
- Author(s)
- 김민경
- Alternative Author(s)
- Minkyung Kim
- Advisor
- 김재훈
- Department
- 일반대학원 컴퓨터공학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2019-08
- Language
- eng
- Alternative Abstract
- The Internet of Things (IoT) is usually defined as things of physical smart objects or devices on Internet. The IoT technology is driving the evolution of collaboration with increasing demands in terms of the capability to collect and exchange data generated from interactions among people, machines, and things. However, since the number of connected devices will grow exponentially, the secure and scalable IoT solutions are required to support ever-growing connected devices. For the scalability of IoT, the current IoT systems based on centralized networking need to be changed by adopting distributed peer-to-peer networking. The blockchain technologies enable decentralized, trustless networking where any untrusted nodes can participate in a fully distributed network. Also, the use of blockchain as a distributed ledger of transactions can improve the security of IoT data collected from the huge number of IoT devices. In this dissertation, an overall architecture view of IoT integrated collaboration and access system built on blockchain-based distributed Cloud is introduced for the efficient decentralized data management. The efficient integration of multiple IoT functions and resources makes it possible to create valuable capabilities and services. Recent advances in smart devices and sensor technologies have motivated to collect a huge amount of personal daily information called ‘lifelog’ in real time. There is a need for the effective management system to extract semantic data through processing time-series big lifelog data. Also, the lifelog data can be processed in separated computing resources depending on the size and level of data for the efficient lifelog retrieval. As a first research of this dissertation, personal lifelog management system is presented to analyze lifelog data and find out meaningful events for personal lifelog services. With the system, a hierarchical structured data logging scheme is proposed with cost analysis to optimally utilize computing and storage resources. With the ever-increasing number of deployed devices in IoT, applying blockchain technologies to IoT devices allows more reliable and secure communications between them. The peer-to-peer feature of blockchain technologies makes it suitable to solve the problems of scalability. In a second research, the cost modeling on IoT protocols is proposed to analyze the basic network performance of decentralized peer-to-peer model and centralized publish/subscribe and request/reply models. The aim of the comparison is to figure out key factors that influence network performance in various conditions. Additionally, the comparative analysis results are described with push, pull, and peer-to-peer based IoT data transmission approaches for IoT collaboration. Blockchain is a distributed repository deployed in a peer-to-peer network, where participants create and broadcast transactions continuously. A set of time-stamped transactions is constantly verified by participant nodes at a given time. Therefore, the efficient consensus algorithm is crucial to validate transactions and coordinate the interactions between smart devices. As a third research of this dissertation, a novel hierarchical voting-based byzantine fault tolerance consensus algorithm is proposed. The proposed HBFT algorithm utilizes a typical PBFT algorithm hierarchically to guarantee low latency and better scalability. Since HBFT mechanism leads to consensus based on each group’s majority, the message complexity is significantly reduced. Also, several equations are calculated to find the optimal number of groups based on the total number of nodes and figure out the influence on the ratio of malicious nodes per group.
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- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 4. Theses(Ph.D)
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