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arxiv: 1809.05613 · v4 · pith:WTB7PGW4new · submitted 2018-09-14 · 💻 cs.NI · cs.CR

A Survey on Consensus Protocols in Blockchain for IoT Networks

classification 💻 cs.NI cs.CR
keywords blockchainconsensusmethodscomputationaldevicesnetworkscryptocurrenciesdifferent
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The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well. The main advantages of blockchain for its potential use in other domains are its inherent security mechanisms and immunity to different attacks. A blockchain relies on a consensus method for agreeing on any new data. Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not apt for resource-constrained systems. In this article, we discuss and survey the various blockchain based consensus methods that are applicable to resource constrained IoT devices and networks. A typical IoT network consists of several devices which have limited computational and communications capabilities. Most often, these devices cannot perform intensive computations and are starved for bandwidth. Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods. We also talk about some of the alternatives to the public blockchain like private blockchain and tangle, along with their potential adoption for IoT networks. Furthermore, we review the existing consensus methods that have been implemented and explore the possibility of utilizing them to realize a blockchain based IoT network. Some of the open research challenges are also put forward.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Analysis of Distributed Average Consensus Algorithms for Robust IoT networks

    cs.DC 2021-04 unverdicted novelty 4.0

    Derives explicit Laplacian eigenvalues for q-triangular r-regular networks and uses them to show negligible effects of noise and communication delay on consensus metrics.