Journal Article10.1109/TII.2021.3051607
A Sharding Scheme-Based Many-Objective Optimization Algorithm for Enhancing Security in Blockchain-Enabled Industrial Internet of Things
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TL;DR: This article proposes a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model and demonstrates that the proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.
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Abstract: While the industrial Internet of Things (IIoT) can support efficient control of the physical world through large amounts of industrial data, data security has been a challenge due to various interconnections and accesses. Blockchain technology can support security and privacy preservation in IIoT data with its trusted and reliable security mechanism. Sharding technology can help improve the overall throughput and scalability of blockchain networks. However, the effectiveness of sharding is still challenging due to the uneven distribution of malicious nodes. By aiming to improve the performance of blockchain networks and reduce the possibility of malicious node aggregation, in this article, we propose a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP) to optimize the shard validation validity model. Then, an optimal blockchain sharding scheme is obtained. Compared with other state-of-the-art many-objective optimization algorithms, MaOEA-DRP performs better on the DTLZ test suite. The simulation results demonstrate that our proposed algorithm can significantly improve the throughput and validity of sharding for better security in the blockchain-enabled IIoT.
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References
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.
Performance assessment of multiobjective optimizers: an analysis and review
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
A Secure Sharding Protocol For Open Blockchains
Loi Luu,Viswesh Narayanan,Chaodong Zheng,Kunal Baweja,Seth Gilbert,Prateek Saxena +5 more
- 24 Oct 2016
TL;DR: ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries, and scalability experiments on Amazon EC2 with up to $1, 600$ nodes confirm ELASTICO's theoretical scaling properties.
1.5K
The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication
Marko Vukolic
- 29 Oct 2015
TL;DR: In the early days of Bitcoin, the performance of its probabilistic proof-of-work (PoW) based consensus fabric, also known as blockchain, was not a major issue, and Bitcoin became a success story, despite its consensus latencies on the order of an hour and the theoretical peak throughput of only up to 7 transactions per second.