Dec-Safe: Dynamic Decision Generation Mechanism for Delivering Safety Services in Vehicular Networks

Pradhan, Patrali; Roy, C.L.; Misra, Sudip; Chattopadhyay, Samiran

doi:10.1109/tvt.2023.3292210

Cited by 1 publication

(1 citation statement)

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“…IoT and IIoT blockchain encompasses several domains, including big data, cloud storage, track food sources, smart cities, smart homes, cloud and edge computing for electric vehicles, and-above all-mobile commerce (Mahato et al 2020). A decentralized ledger called the blockchain keeps track of transactions in a chronological order (Pradhan et al 2023). Unlike centralized ledger, where ledger is kept on server, all node updates transactions on server, every participating node in a blockchain network provides the ledger (Chen 2023).…”

Section: Introductionmentioning

confidence: 99%

Integrated Fuzzy Decision Tree based Blockchain Federated Safety- as-a-Service for IIoT

Tripathi,

Umrao

2024

Preprint

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Blockchains are not appropriate for smart appliances because they are expensive to compute, have a lot of overhead bandwidth and cause delays. Improving data delivery, quality, and precision are especially important for ML. The combination of ML and Blockchain technologies make exact outcomes. The IIoT, has quickly been recognized and is attainment huge attention in educational areas with manufacturing, but IoT solitude danger and privacy exposures are emerging by lack of significant security technology. The Blockchain Driven Cyber-Physical system (BDCPS) is supported by IoT-cloud services. In this paper, Integrated Fuzzy Decision Tree based Blockchain Federated Safety-as-a-Service for IIoT (IFDT-BCF-SAS-IIOT) is proposed. In this study, Lightweight Homomorphic Cryptographic Algorithm is run on separate board system. The suggested system uses Delegated Proof of Work Consensus Protocol to resolve problems likes’ lightweight, warehousing transactions, evaporation, and shipment time. The data flow of blockchain is proposed to establish application of integrated fuzzy decision tree to food traceability. The proposed method is executed in Python, performance of proposed technique is analysed with performance metrics like, accuracy, precision, F1-score, recall, specificity, Mean Absolute Error, MSE, RMSE are analyzed.The proposed IFDT-BCF-SAS-IIOT method attains 32.58%, 26.73% and 24.22%, higher Specificity, 23.58%, 20.73%, 19.32% higher precision, 30.18%, 28.66% and 24.32% higher recall, 21.58%, 20.73%, 18.32% lower RMSE analysed with existing techniques like new blockchain federated safety-as-a-service system for IIoT utilizing ML (NBF-SAS-IIOT), IIoT: requirements, architecture, challenges, future research directions (IIOT-SAS-RAC), differential privacy for IIoT: opportunities, applications, challenges (IIOT-SAS-OAC) respectively.

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Section: Introductionmentioning

confidence: 99%