Delegated Proof of Accessibility (DPoAC): A Novel Consensus Protocol for Blockchain Systems

Kaur, Manpreet; Gupta, Shikha; Kumar, Deepak; Verma, Chaman; Neagu, Bogdan-Constantin; Răboaca, Maria Simona

doi:10.3390/math10132336

Cited by 21 publications

(10 citation statements)

References 36 publications

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“…In this study, a novel hybrid CNN‐LSTM Deep Learning (DL) algorithm model was evaluated in great detail. The Convolutional Neural Network (CNN) Learning model and the Long Short‐Term Memory (LSTM) Learning model were compared and contrasted for their individual and combined efficacy 25 . Standard measures for gauging deep learning algorithm performance were applied to these models.…”

Section: Results Analysismentioning

confidence: 99%

“…The Convolutional Neural Network (CNN) Learning model and the Long Short-Term Memory (LSTM) Learning model were compared and contrasted for their individual and combined efficacy. 25 Standard measures for gauging deep learning algorithm performance were applied to these models. Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Accuracy were used as performance indicators in this study (RMSE).…”

Section: Datasetmentioning

confidence: 99%

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Internet of things‐enabled intelligent systems for remote chronic disease monitoring

Tai,

Rajawat,

Goyal

et al. 2023

Trans Emerging Tel Tech

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The possibility of remotely monitoring and managing chronic diseases is made feasible by the Internet of Things (IoT), which enables the collection and analysis of vast amounts of data from various sources such as sensors, devices, and wearables. However, conventional machine learning approaches often encounter difficulties when dealing with IoT data due to its noise, heterogeneity, and high dimensions. In this study, we introduce a novel technique for developing human‐centric intelligent systems for remote monitoring of chronic diseases using deep learning in an IoT context. This technique is capable of handling complex and diverse IoT data, providing accurate and comprehensible predictions and recommendations. We outline a standardized architecture for these systems, comprising of three distinct phases: data acquisition, data processing, and data visualization. To demonstrate the effectiveness of our methodology, we apply it to a case study involving the development and evaluation of a human‐centered intelligent system for remote monitoring of hypertension and diabetic patients, involving real users. Our findings reveal a substantial enhancement in prediction accuracy when employing our proposed hybrid algorithm. When compared to traditional algorithms, our proposed hybrid algorithm exhibits superior performance, achieving an accuracy of 93.5%, surpassing the Restricted Boltzmann Machine (RBM) with an accuracy of 88.2%, Long Short‐Term Memory (LSTM) networks at 90.4%, and Convolutional Neural Networks (CNN) at 91.6%. This improvement in accuracy is critical for ensuring reliable disease monitoring and management, highlighting the effectiveness of our approach. In terms of precision, scalability, and user satisfaction, we demonstrate the effectiveness and efficiency of our method. Furthermore, we address the social and ethical implications inherent in our methodology and suggest avenues for future research in this domain.

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Section: Results Analysismentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

Tai,

Rajawat,

Goyal

et al. 2023

Trans Emerging Tel Tech

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“…Unlike previous peer-to-peer systems such as BitTorrent [26], Git [27], Self-certi ed File Systems, and distributed hash tables, IPFS provides a comprehensive framework for the distributed sharing of extensive datasets. Moreover, IPFS provides a storage solution supporting large data volumes and utilizing content-based hyperlinks [24].…”

Section: Distributed Storage Layermentioning

confidence: 99%

A Scalable Blockchain-based Framework for Efficient IoT Data Management Using Lightweight Consensus

Haque,

Shah,

Iqbal

et al. 2024

Preprint

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Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network’s performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 milliseconds. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

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“…A recent paper [29] proposed a delegated proof of accessibility (DPoAC) protocol, mostly based on the previous idea. It employed secret sharing, PoS with random selection, and an interplanetary file system (IPFS).…”

Section: Proof Of Accuracy Protocolmentioning

confidence: 99%

On Proof-of-Accuracy Consensus Protocols

Aponte-Novoa

Villanueva-Polanco

2022

Mathematics

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Consensus protocols are a fundamental part of any blockchain; although several protocols have been in operation for several years, they still have drawbacks. For instance, some may be susceptible to a 51% attack, also known as a majority attack, which may suppose a high risk to the trustworthiness of the blockchains. Although this attack is theoretically possible, executing it in practice is often regarded as arduous because of the premise that, with sufficiently active members, it is not ’straightforward’ to have much computing power. Since it represents a possible vulnerability, the community has made efforts to solve this and other blockchain problems, which has resulted in the birth of alternative consensus protocols, e.g., the proof of accuracy protocol. This paper presents a detailed proposal of a proof-of-accuracy protocol. It aims to democratize the miners’ participation within a blockchain, control the miners’ computing power, and mitigate the majority attacks.

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Delegated Proof of Accessibility (DPoAC): A Novel Consensus Protocol for Blockchain Systems

Cited by 21 publications

References 36 publications

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

Internet of things‐enabled intelligent systems for remote chronic disease monitoring

A Scalable Blockchain-based Framework for Efficient IoT Data Management Using Lightweight Consensus

On Proof-of-Accuracy Consensus Protocols

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