A blockchain-based clock synchronization Scheme in IoT

Fan, Kai; Jiang, Wei; Yan, Zheng; Pan, Qiang; Li, Hui; Yang, Yintang

doi:10.1016/j.future.2019.06.007

Cited by 41 publications

(35 citation statements)

References 15 publications

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“…It necessitates the presence of a computationally-capable consensus node for verification. The authors in [14] extended the architecture to use Proofof-Stake (PoS) and a custom blockchain whose length can be controlled. However, the consensus mechanism consumes significant time due to computational complexity and the device of the highest stake has to be re-elected in the event of a failure.…”

Section: Related Workmentioning

confidence: 99%

Blockchain, what time is it? Trustless Datetime Synchronization for IoT

Regnath

Shivaraman²,

Shreejith

et al. 2020

2020 International Conference on Omni-Layer Intelligent Systems (COINS)

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Time synchronization among IoT devices is a fundamental requirement for efficient and reliable communication on a global scale. Common synchronization schemes such as NTP operate on a trust-based client-server model, which does not scale well in a decentralized network because single server failures can lead to a severe downtime before re-establishing synchronization. Public blockchains such as Ethereum provide a trustless network and tamper-proof time-stamped data that is freely available. In this paper, we leverage the availability of time information in the block headers, which are very small (several hundreds of bytes) compared to the full blocks and can be validated without participation in the mining process. Our approach uses two estimators that are fed with the timestamps from block headers as well as the elapsed time between consecutive block receptions to estimate the true time to an accuracy of one second. We evaluate our approach by extensive validation on blockchain data from different geographical locations across the globe and show that global synchronization can be established despite the non-deterministic behavior of blockchains such as mining difficulty, network latencies and forks.

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Section: Related Workmentioning

confidence: 99%

Blockchain, what time is it? Trustless Datetime Synchronization for IoT

Regnath

Shivaraman²,

Shreejith

et al. 2020

2020 International Conference on Omni-Layer Intelligent Systems (COINS)

View full text Add to dashboard Cite

show abstract

“…The elected prime node plays a role of interacting with a consensus coordinator. This election step runs each regular t time (we assumed that all nodes have the same time unit through a logical clock or a physical clock algorithm (e.g., [29][30][31])). Once all steps are completed, this prime node election step is carried out again.…”

Section: Step 1 Electing a Prime Nodementioning

confidence: 99%

A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus

Seo

Ko²,

Kim

et al. 2020

Applied Sciences

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Among various consensus algorithms, the Byzantine Fault Tolerance (BFT)-based consensus algorithms are broadly used for private blockchain. However, as BFT-based consensus algorithms are structured for all participants to take part in a consensus process, a scalability issue becomes more noticeable. In this approach, we introduce a consensus coordinator to execute a conditionally BFT-based consensus algorithm by classifying transactions. Transactions are divided into equal and unequal transactions. Moreover, unequal transactions are divided again and classified as common and trouble transactions. After that, a consensus algorithm is only executed for trouble transactions, and BFT-based consensus algorithms can achieve scalability. For evaluating our approach, we carried out three experiments in response to three research questions. By applying our approach to PBFT, we obtained 4.75 times better performance than using only PBFT. In the other experiment, we applied our approach to IBFT of Hyperledger Besu, and our result shows a 61.81% performance improvement. In all experiments depending on the change of the number of blockchain nodes, we obtained the better performance than original BFT-based consensus algorithms; thus, we can conclude that our approach improved the scalability of original BFT-based consensus algorithms. We also showed a correlation between performance and trouble transactions associated with transaction issue intervals and the number of blockchain nodes.

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“…e security threshold φ in AM-SM-UAM is related to the accuracy of identifying malicious users. By equation (1), the formula of the safety of users for the Sec(…”

Section: Parameter Tuning Dbscan Clustering Algorithmmentioning

confidence: 99%

“…e Internet of ings (IoT) is the latest evolution of the Internet, including a great deal of connected physical devices and applications [1]. IoT allows object collection and data exchange, etc.…”

Section: Introductionmentioning

confidence: 99%

User Audit Model Based on Attribute Measurement and Similarity Measurement

Yang

Sun

2020

Security and Communication Networks

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The Internet of Things (IoT) is an open network. And, there are a large number of malicious nodes in the network. These malicious nodes may tamper with the correct data and pass them to other nodes. The normal nodes will use the wrong data for information dissemination due to a lack of ability to verify the correctness of the messages received, resulting in the dissemination of false information on medical, social, and other networks. Auditing user attributes and behavior information to identify malicious user nodes is an important way to secure networks. In response to the user nodes audit problem, a user audit model based on attribute measurement and similarity measurement (AM-SM-UAM) is proposed. Firstly, the user attribute measurement algorithm is constructed, using a hierarchical decision model to construct a judgment matrix to analyze user attribute data. Secondly, the blog similarity measurement algorithm is constructed, evaluating the similarity of blog posts published by different users based on the improved Levenshtein distance. Finally, a user audit model based on a security degree is built, and malicious users are defined by security thresholds. Experimental results show that this model can comprehensively analyze the attribute and behavior data of users and have more accurate and stable performance in the practical application of the network platforms.

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A blockchain-based clock synchronization Scheme in IoT

Cited by 41 publications

References 15 publications

Blockchain, what time is it? Trustless Datetime Synchronization for IoT

Blockchain, what time is it? Trustless Datetime Synchronization for IoT

A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus

User Audit Model Based on Attribute Measurement and Similarity Measurement

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