Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Vaiyapuri, Thavavel; Shankar, K.; Rajendran, Surendran; Kumar, Sachin; Acharya, Srijana; Kim, Hyunil

doi:10.1109/access.2023.3280798

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…For the purpose of detecting faults in IoT, a novel technique called Blockchain Assisted Data Edge Verification with Consensus Algorithm for Machine Learning (BDEV-CAML) is created and proposed by Vaiyapuri T. et al (18) . The BDEV-CAML technique proposed combines the advantages of blockchain, the IoT, and machine learning (ML) models in order to improve the trustworthiness, effectiveness, and security of the IoT network.…”

Section: Literature Surveymentioning

confidence: 99%

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

Ul Islam,

Das,

Deka

et al. 2024

IJST

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Objective: An Internet of Things (IoT) network has constraints on power consumption, processing power and security of end devices in the network. On the other hand, blockchain technology has some difficulties in storage capacity, data privacy, the ability to revoke consent, energy usage, scalability, and speed. To overcome the individual limitations of these two technologies, we integrate these two to propose a new design with an intention to authenticate, secure and share data pertaining to attendance system. In this present work the primary implementation details of our proposed work regarding coding are analyzed and a straightforward solution is offered by using indexing. Methods: Through our proposed work we introduce a multifaceted authentication system for user device substantiation, a Public Key Infrastructure (PKI) and RSA (Rivest-Shamir-Adleman) based blockchain implementation for data sharing and a rule-based access control scheme used for accessing data. Findings: The system can work with unidirectional and bidirectional IoT networks, and the data is stored on blockchain, providing more security to the data generated on the IoT network. Blockchain technology is utilized to offer a solution for certain problems encountered within a widespread network. Furthermore, a multi-factor authentication scheme is introduced and a rule-based access control scheme to access the data. Novelty: The blockchain is emerging which can be used to decentralize management and protect sensitive data. In a blockchain based attendance system, no administrative authority is permitted to modify or erase data. The individual who adds a data entry to the blockchain cannot later deny their involvement in the action. On the blockchain, all of the data and history are accessible to every participant. The blockchain based attendance system needs to offer a database that can be trusted, secure, and impossible to manipulate. Keywords: Blockchain (BC), Internet of Things (IoT), Student Attendance Management (SAM), User Authentication Key (UAK), Data Sharing

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Section: Literature Surveymentioning

confidence: 99%

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

Ul Islam,

Das,

Deka

et al. 2024

IJST

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“…Vaiyapuri et al [9] and [10] advocated for blockchainassisted data edge verification with consensus algorithms in IoT. Their research showcased the significance of data integrity and security, aspects crucial in fault detection within IoT systems.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

Fault Detection and Localization in Industrial IoT Systems using Deep Learning

Shivadekar,

Dhabliya

2023

RJCSE

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In the ever-evolving landscape of Industrial Internet of Things (IoT) systems, the need for robust fault detection and localization has become paramount. Existing methods, while commendable, have faced certain limitations that necessitate the development of more sophisticated solutions. This work embarks on a journey to address these challenges and presents a novel approach to fault detection and localization, leveraging the formidable power of Deep Learning process. The limitations of existing methodologies primarily revolve around their inability to provide the required precision and accuracy in detecting and localizing faults within complex IoT deployments. These methods often struggle to handle the diverse contextual datasets that characterize modern industrial settings. Additionally, their computational efficiency often leaves much to be desired, hindering real-time fault response mechanisms. In response to these limitations, our proposed model introduces a hybrid framework. It combines Vector AutoRegressive Moving Average with Exogenous Variables (VARMAx) for precise fault localization and Convolutional Neural Networks (CNN) for robust fault detection. This fusion harnesses the strengths of both approaches, providing a comprehensive solution for fault management in industrial IoT systems. The advantages of this model are twofold. First, VARMAx offers exceptional accuracy in pinpointing the exact location of faults within the system, facilitating swift corrective actions. Second, the integration of CNN enhances fault detection by effectively capturing patterns and anomalies in the data, resulting in a highly responsive fault detection system. Notably, this model not only outperforms existing methods with a 3.9% boost in precision, 4.5% increase in accuracy, 4.9% higher recall, 8.5% improved AUC, but also boasts a remarkable 5.9% enhancement in computational speed, ensuring real-time responsiveness. The profound impact of this work extends to the realms of industrial automation and IoT system reliability. By addressing the limitations of existing approaches and introducing a robust fault detection and localization model, this paper paves the way for safer and more efficient industrial operations. The improved precision, accuracy, and speed of fault detection will minimize downtime, reduce maintenance costs, and ultimately elevate the performance and reliability of industrial IoT systems to new heights.

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“…This is done via use of Blockchain-Based Privacy-Aware Contextual Online Learning (BPACOL) operations. Data integrity, trust among interacting parties, and the availability of services are just a few facets that require undivided attention from a security standpoint under different use cases [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

Tota,

Karmore

2023

Preprint

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As the utilization of big data environments becomes increasingly widespread, the demand for robust and efficient security mechanisms within this context becomes paramount, especially in real-time scenarios. Ensuring data integrity, trust, and availability in multi-cloud environments is of utmost importance. Existing solutions often fall short in these aspects, resulting in issues such as latency, energy inefficiency, and vulnerability to access attacks, among other drawbacks. Traditional security models designed for big data environments tend to rely on ineffective proof mechanisms and static encryption methods. These conventional approaches lead to increased data processing delays, heightened energy consumption, and reduced communication throughput. Furthermore, these existing models have demonstrated limited capabilities in accurately identifying unauthorized access attempts, thereby escalating security risks. This paper introduces an innovative security framework tailored for big data environments, which leverages a suite of cutting-edge technologies to address the limitations of current models. The proposed architecture integrates several key components, including a Proof of Spatial & Temporal Trust-based Blockchain for secure storage operations, Grey Wolf Optimization (GWO) for efficient blockchain sharding, Fully Homomorphic Encryption (FHE) for safeguarding data across various data nodes, Physical Unclonable Functions (PuFs) for resilient inter-data node communications, and Fuzzy Rule-Based Access Control to enhance overall security. Our research results demonstrate that this proposed model significantly mitigates the inefficiencies and vulnerabilities that currently plague big data security systems. Specifically, it achieves an 8.3% reduction in data processing delays, a 4.5% reduction in energy consumption, a 2.9% improvement in communication throughput, an 8.5% enhancement in the accuracy of detecting access attacks, and a 3.5% increase in access speed levels. This innovative security framework tailored for big data environments offers a comprehensive and effective approach to overcome the limitations of existing solutions. It represents a significant advancement in the field of big data security, contributing significantly to ongoing efforts aimed at creating more secure, efficient, and resilient big data ecosystems.

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Blockchain Assisted Data Edge Verification With Consensus Algorithm for Machine Learning Assisted IoT

Cited by 11 publications

References 26 publications

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

A BlockChain-Based IoT Data Securing and Sharing System for Attendance Management

Fault Detection and Localization in Industrial IoT Systems using Deep Learning

IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

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