A Many-objective Optimized Sharding Scheme for Blockchain Performance Improvement in End-Edge Enabled Internet of Things

This paper establishes an edge computing architecture based on blockchain sharding to apply blockchain technology at the edge layer. The tasks offloaded to the edge layer are performed by a set of edge server nodes for consensus operations, which improves the security of offloading. At the same time...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 10; no. 24; p. 1
Main Authors: Cui, Zhihua, Xue, Zhaoyu, Ma, Yanan, Cai, Xingjuan, Chen, Jinjun
Format: Journal Article
Language:English
Published: Piscataway IEEE 15.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Blockchain
blockchain sharding
Blockchains
Cryptography
Cybersecurity
Distribution functions
Edge computing
Energy consumption
Entropy (Information theory)
Evolutionary algorithms
Fault tolerance
information entropy
Internet of Things
many-objective evolutionary algorithm
Multiple objective analysis
Network latency
Nodes
Optimization
Problem solving
Security
Servers
Sharding
Task analysis
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:This paper establishes an edge computing architecture based on blockchain sharding to apply blockchain technology at the edge layer. The tasks offloaded to the edge layer are performed by a set of edge server nodes for consensus operations, which improves the security of offloading. At the same time, the nodes and offloaded tasks are divided into multiple shards, and the typical practical byzantine fault tolerance (PBFT) consensus is performed in each shard to improve scalability. In order to avoid the impact of malicious nodes in an unreasonable sharding scheme on network security and performance, we model the many-objective optimization sharding problems, which includes reducing consensus latency, energy consumption, sharding failure probability, and improving sharding throughput. Moreover, we design a many-objective evolutionary algorithm based on information entropy (MaOEA-IE) to solve the model. In the process of population environment selection, the distribution information of individuals is mapped to a Gaussian function, and we aggregate the distribution functions of all individuals and calculate the information entropy to measure the distribution of the population. The simulation results of benchmark test problems and model solving problems show that the solution set of MaOEA-IE has the best convergence, diversity, and comprehensive performance. This paper provides an idea for the formulation of blockchain sharding scheme.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3292369