DR-BFT: A consensus algorithm for blockchain-based multi-layer data integrity framework in dynamic edge computing system

doi:10.1016/j.future.2021.04.020

Return

DR-BFT: A consensus algorithm for blockchain-based multi-layer data integrity framework in dynamic edge computing system

2021-11-01

PRE

DOI：10.1016/j.future.2021.04.020

Original

Original request for help

Save

Abstract

En 中文

Edge computing, as a part a distributed computing architecture, has become an increasingly popular paradigm. It expands the capacity of cloud by facilitating data from the end devices to be stored and processed at the edge of the network closer to the data instead of delivering it to the cloud. Data integrity is a big concern in edge computing. As a promising solution to data integrity, blockchain is viable to protect the data in edge computing systems. However, most existing consensus algorithms cannot meet the requirements of edge computing in a dynamic network, where the nodes may join or leave the blockchain network dynamically. In this paper, we introduce a two-layer block-chainbased framework to provide data integrity in edge computing, and propose a novel Dynamic Random Byzantine Fault Tolerance (DR-BFT) consensus algorithm. DR-BFT consists of three sub-algorithms, String Consensus, Data Correctness Validation, and Binary Consensus. String consensus tries to reach consensus on the data of end devices or edge servers, and the sub-algorithm is based on an agreement and borrows some ideas from the Phase King Protocol. If the string consensus fails early termination'', each node will agree on a value from a random primary node and go through data correctness validation sub-algorithm. The system then reaches consensus on the data with binary consensus sub-algorithm which is a variant of Ben-Or and Michael's Random Consensus. We also propose an improved quorum method to cope with contention and dynamic node leaving/joining. We analyze DR-BFT with regard to consensus correctness, security, and system overhead. DR-BFT satisfies agreement, validity and termination. We conduct experiments through simulations. Experimental results show that the proposed consensus DR-BFT can effectively improve the performance in dynamic edge computing, including communication overhead and consensus latency. (C) 2021 Elsevier B.V. All rights reserved.

Keywords:

Blockchain

Consensus

Edge computing

Dynamic network

Data integrity

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.