Abstract
IoT technology is emerging as a fully developed automation that could be integrated in various web applications, which will be present in upcoming generations of the World Wide Web. Blockchain, like IoT, is a burgeoning field whereby every system associated in the blockchain incorporates a disseminated ledger that improves safety and consistency. Due to the blockchain network abilities to accomplish smart contracts and consensus, unauthorized users are unable to undertake any fault transactions. The IoT and blockchain can be aggregated to improve application performance dynamically at run time. However, controlling and monitoring the machines linked to sensors in an IoT background and mining the blockchain will always be a technical challenge to the researchers. With this context, this paper enables to review the fundamentals of IoT, blockchain field, and its topographies. In this paper, design architecture, namely, IoT Blockchain Assurance-Based Compliance to COVID Quarantine, is proposed and concluded up with novel architectural framework that improves the efficiency of data safety and data transparency. Unlicensed users are not permitted to conduct any erroneous transactions within the blockchain network, which has the capability to engage in smooth contracts and agreement, thus extending the safekeeping between clinicians and chronically ill patients. This methodology was created with immobile elderly chronically ill patients in mind who are suffering from COVID that require on-the-spot treatment and continuous monitoring by a doctor in mind. This paper is designed to analyze the performance of proposed IoT Blockchain Assurance-Based Compliance to COVID Quarantine with Ethereum private blockchain network beneath a genesis block and the results are conferred.
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Devi, M.S., Carmel Mary Belinda, M.J., Aruna, R., Ramesh, P.S., Sundaravadivazhagan, B. (2023). A Novel Model for IoT Blockchain Assurance-Based Compliance to COVID Quarantine. In: Kanagachidambaresan, G.R., Bhatia, D., Kumar, D., Mishra, A. (eds) System Design for Epidemics Using Machine Learning and Deep Learning. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-19752-9_5
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