Pith. sign in

REVIEW 1 cited by

Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.13498 v1 pith:G3R2ZPHI submitted 2023-09-23 cs.DC cs.CR

classification cs.DCcs.CR
keywords consensusalgorithmsdistributedledgeradditionanalysiscomprehensivediscuss
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The most essential component of every Distributed Ledger Technology (DLT) is the Consensus Algorithm (CA), which enables users to reach a consensus in a decentralized and distributed manner. Numerous CA exist, but their viability for particular applications varies, making their trade-offs a crucial factor to consider when implementing DLT in a specific field. This article provided a comprehensive analysis of the various consensus algorithms used in distributed ledger technologies (DLT) and blockchain networks. We cover an extensive array of thirty consensus algorithms. Eleven attributes including hardware requirements, pre-trust level, tolerance level, and more, were used to generate a series of comparison tables evaluating these consensus algorithms. In addition, we discuss DLT classifications, the categories of certain consensus algorithms, and provide examples of authentication-focused and data-storage-focused DLTs. In addition, we analyze the pros and cons of particular consensus algorithms, such as Nominated Proof of Stake (NPoS), Bonded Proof of Stake (BPoS), and Avalanche. In conclusion, we discuss the applicability of these consensus algorithms to various Cyber Physical System (CPS) use cases, including supply chain management, intelligent transportation systems, and smart healthcare.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning

    cs.LG 2025-07 reject novelty 4.0 of 10

    A LinUCB contextual bandit selects federated aggregation rules online based on update variance, cosine similarity, and mean norm, claiming superior accuracy and tunable risk posture.

Pith tools