Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis

• URL: http://arxiv.org/abs/2309.13498v1
• Date: Sat, 23 Sep 2023 23:32:11 GMT
• Title: Consensus Algorithms of Distributed Ledger Technology -- A Comprehensive Analysis
• Authors: Ahmad J. Alkhodair, Saraju P. Mohanty, Elias Kougianos,
• Abstract summary: This article provides a comprehensive analysis of the various consensus algorithms used in distributed ledger technologies (DLT) and blockchain networks. 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem. These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem. We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization [5.404315085380945]
  Newton-type (NT) methods have been advocated as enablers of robust convergence rates in DO problems. We propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization. We show that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.
  arXiv  Detail & Related papers  (2023-05-18T10:15:03Z)
• Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems [2.54403164787763]
  We formulate a joint optimization model with three metrics, namely latency, security, and computational cost. We propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm (ADPSA) to obtain near-optimal solutions. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches.
  arXiv  Detail & Related papers  (2023-03-28T09:29:29Z)
• Deep Combinatorial Aggregation [58.78692706974121]
  Deep ensemble is a simple and effective method that achieves state-of-the-art results for uncertainty-aware learning tasks. In this work, we explore a generalization of deep ensemble called deep aggregation (DCA) DCA creates multiple instances of network components and aggregates their combinations to produce diversified model proposals and predictions.
  arXiv  Detail & Related papers  (2022-10-12T17:35:03Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness [53.351863569314794]
  The critical node problem (CNP) aims to find a set of critical nodes from a network whose deletion maximally degrades the pairwise connectivity of the residual network. This work proposes a feature importance-aware graph attention network for node representation. It combines it with dueling double deep Q-network to create an end-to-end algorithm to solve CNP for the first time.
  arXiv  Detail & Related papers  (2021-12-03T14:23:05Z)
• Sharing classical secrets with continuous-variable entanglement: Composable security and network coding advantage [0.913755431537592]
  We show that multi-partite entangled resources achieve a genuine advantage over point-to-point protocols for quantum communication. This is the first concrete compelling examples of multi-partite entangled resources achieving a genuine advantage over point-to-point protocols for quantum communication.
  arXiv  Detail & Related papers  (2021-04-21T17:37:28Z)
• Causal Policy Gradients [6.123324869194195]
  Causal policy gradients (CPGs) provide a common framework for analysing key state-of-the-art algorithms. CPGs are shown to generalise traditional policy gradients, and yield a principled way of incorporating prior knowledge of a problem domain's generative processes.
  arXiv  Detail & Related papers  (2021-02-20T14:51:12Z)
• Determinantal consensus clustering [77.34726150561087]
  We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms. DPPs favor diversity of the center points within subsets. We show through simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets.
  arXiv  Detail & Related papers  (2021-02-07T23:48:24Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)
• Joint Contrastive Learning with Infinite Possibilities [114.45811348666898]
  This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling. We derive a particular form of contrastive loss named Joint Contrastive Learning (JCL)
  arXiv  Detail & Related papers  (2020-09-30T16:24:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.