A Fair and Lightweight Consensus Algorithm for IoT
- URL: http://arxiv.org/abs/2503.08607v1
- Date: Tue, 11 Mar 2025 16:45:51 GMT
- Title: A Fair and Lightweight Consensus Algorithm for IoT
- Authors: Sokratis Vavilis, Harris Niavis, Konstantinos Loupos,
- Abstract summary: This work introduces a fair and lightweight hybrid consensus algorithm tailored for IoT.<n>The proposed approach minimizes resource demands on the nodes while ensuring a secure and fair agreement process.<n>In addition, a reputation-based block voting mechanism is incorporated to enhance trust and establish finality.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As hyperconnected devices and decentralized data architectures expand, securing IoT transactions becomes increasingly challenging. Blockchain offers a promising solution, but its effectiveness relies on the underlying consensus algorithm. Traditional mechanisms like PoW and PoS are often impractical for resource-constrained IoT environments. To address these limitations, this work introduces a fair and lightweight hybrid consensus algorithm tailored for IoT. The proposed approach minimizes resource demands on the nodes while ensuring a secure and fair agreement process. Specifically, it leverages a distributed lottery mechanism to fairly propose blocks without requiring specialized hardware. In addition, a reputation-based block voting mechanism is incorporated to enhance trust and establish finality. Finally, experimental evaluation was conducted to validate the key features of the consensus algorithm.
Related papers
- Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach [60.80257080226662]
We propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs.
Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model.
By leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory.
arXiv Detail & Related papers (2025-01-26T07:47:05Z) - Dynamic Digital Twins of Blockchain Systems: State Extraction and Mirroring [3.5376671181893897]
This paper constitutes an effort to design a Digital Twin-based blockchain management framework.<n>It aims to adapt the consensus process to fit the conditions of the underlying system.<n>Specifically, this work addresses the problems of extracting the blockchain system and mirroring it in its digital twin.
arXiv Detail & Related papers (2024-12-07T03:54:34Z) - Digital Twin-Assisted Federated Learning with Blockchain in Multi-tier Computing Systems [67.14406100332671]
In Industry 4.0 systems, resource-constrained edge devices engage in frequent data interactions.
This paper proposes a digital twin (DT) and federated digital twin (FL) scheme.
The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis.
arXiv Detail & Related papers (2024-11-04T17:48:02Z) - Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach [20.821562115822182]
This paper introduces a novel OR-aggregation approach for zero-knowledge set membership proofs.
We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis.
Results show significant improvements in proof size, generation time, and verification efficiency.
arXiv Detail & Related papers (2024-10-11T18:16:34Z) - Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework [17.919501880326383]
This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks.
Using blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients.
The framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion.
arXiv Detail & Related papers (2024-06-24T23:15:19Z) - A Novel Endorsement Protocol to Secure BFT-Based Consensus in Permissionless Blockchain [1.3723120574076126]
BFT-based consensus mechanisms are widely adopted in the permissioned blockchain to meet the high scalability requirements of the network.
Sybil attacks are one of the most potential threats when applying BFT-based consensus mechanisms in permissionless blockchain.
This paper presents a novel endorsement-based bootstrapping protocol with a signature algorithm that offers a streamlined, scalable identity endorsement and verification process.
arXiv Detail & Related papers (2024-05-04T03:00:33Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0.
To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model.
Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
arXiv Detail & Related papers (2024-03-20T01:58:38Z) - ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles [31.439376852065713]
Power of smart contracts is enabled by interacting with off-chain data, which in turn opens the possibility to undermine the block state consistency.
We propose an adaptive conformal consensus (ACon$2$) algorithm, which derives consensus from multiple oracle contracts.
In particular, the proposed algorithm returns a consensus set, which quantifies the uncertainty of data and achieves a desired correctness guarantee.
arXiv Detail & Related papers (2022-11-17T04:37:24Z) - Regulation conform DLT-operable payment adapter based on trustless -
justified trust combined generalized state channels [77.34726150561087]
Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks.
We give an overview of current solutions that differ in their fundamental values and technological possibilities.
We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
arXiv Detail & Related papers (2020-07-03T10:45:55Z) - F2A2: Flexible Fully-decentralized Approximate Actor-critic for
Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications.
We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting.
Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
arXiv Detail & Related papers (2020-04-17T14:56:29Z)
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.