An efficient Blockchain-based security Model with Incremental Learning & Dynamic Mutability for IoMT Deployments

Registration ID: IJNRD_209298

Published ID: IJNRD2311301

DOI: Click Here to Get

Prof Radhika P.Fuke , Dr.Rekha Ranawat

Keywords: IoMT, Machine Learning, QoS, Blockchain, Sidechain, Deployment, Scenarios

Abstract:The expanding deployment of Internet of Medical Things (IoMT) networks has necessitated the development of efficient and secure solutions to address the challenges of data sharing, quality of service, and security. This paper presents an efficient blockchain-based security model for IoMT deployments with incremental learning and dynamic mutability operations. This research is necessary due to the inherent requirements of IoMT networks, which include real-time data sharing, low delay, energy efficiency, throughput enhancement, and robust security. To meet these requirements, our proposed model employs the Firefly algorithm in conjunction with Particle Swarm Optimization (PSO) to determine optimal sidechain configurations that improve QoS in IoMT networks. In addition, Q-learning is used to select mutable data, enabling enhanced real-time data sharing between IoMT deployments. The proposed model consists of three major components: a sidechain configuration optimizer based on Firefly-PSO, a mutable information selector based on Q-learning, and an augmented set of blockchain-based security frameworks. The Firefly-PSO optimizer intelligently determines sidechain configurations for optimizing QoS parameters including delay, energy requirements, and throughput levels. The Q-learning component selects mutable information dynamically to enhance real-time data sharing operations. The blockchain-based security framework provides robust protection against a variety of attacks, such as Finney attacks, Distributed Denial of Service (DDoS) attacks, Man-in-the-middle (MITM) attacks, and Sybil attacks. Experimental evaluations demonstrate that the proposed model is effective. Compared to recently proposed blockchain models, our model achieves an 8.5% reduction in delay, a 12.4% reduction in energy requirements, a 19.5% increase in throughput, and enhanced security against a variety of attacks. These findings validate the importance of leveraging incremental learning, dynamic mutability, and intelligent algorithms to address the challenges of IoMT deployments, thereby improving the overall performance and security of IoMT networks. This research contributes to the development of efficient and secure IoMT solutions by facilitating real-time data sharing, enhancing QoS parameters, and mitigating security threats. The proposed model has applications in healthcare systems, remote patient monitoring, telemedicine, and other domains where reliable and secure data sharing is essential for real-time operability scenarios

Published Paper   E-Certificate

Prof Radhika P.Fuke & Dr.Rekha Ranawat (November-2023). An efficient Blockchain-based security Model with Incremental Learning & Dynamic Mutability for IoMT Deployments. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), d1-d11. https://ijnrd.org/papers/IJNRD2311301.pdf

Volume 8 Issue 11, November-2023

Pages : d1-d11

Paper Reg. ID: IJNRD_209298

Published Paper Id: IJNRD2311301

Downloads: 000122001

Research Area: Engineering

Country: Badnera Amravati, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311301.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311301

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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