Work place: Department of Computational Intelligence, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
E-mail: arivazhn@srmist.edu.in
Website:
Research Interests:
Biography
N. Arivazhagan working as a Associate Professor in Department of Computational Intelligence, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu. He earned his Ph.D. in Computer Science with a specialization in Machine Learning in 2020 from SRM Institute of Science and Technology. He completed his M.S. degree in Systems and Information from the Birla Institute of Technology, Pilani, in 1995. Dr. N. Arivazhagan has over 34 years of teaching experience and has authored more than 20 publications in reputed international journals and conference proceedings. His research interests include machine learning and medical image processing. He is an active professional member of ACM, ISTE, and IEANG.
By B. J. Praveena N. Arivazhagan
DOI: https://doi.org/10.5815/ijcnis.2026.05.09, Pub. Date: 8 Oct. 2026
As the Internet of Things (IoT) health monitoring systems expand, measures must be taken to ensure the safe, real-time handling of data, particularly for highly infectious diseases which limit the ability for human interaction. Blockchain provides the necessary transparency and immutability so that patient families and hospitals can align on trust with health data. Hyperledger Fabric is the most commonly used solution for these scenarios, as it operates on X.509 certificates for the authentication of device identity. Yet, with the frequent transmission of the IoT sensors, the need for repeated certificate authentication causes considerable computational lags, which can hinder critical real-time assessments in the care continuum. We propose CAVIC (Certificate Authentication Verification Intelligent Cache), which is aimed at simplifying the verification and caching processes with the goal of diminishing the effects of redundant cryptographic activities. CAVIC tracks the high-traffic IoT devices in a network, including temperature, heart rate, and oxygen saturation sensors, and creates a secured cache for their authenticated certificates. Further processes from these devices are verified via instant cache lookups rather than comprehensive certificate re-evaluations, which drastically reduce verification overhead. In situations where a patient's condition can worsen to an extent requiring immediate action from a doctor, CAVIC guarantees that the system's time is dedicated to the analysis of patient data instead of the continual validation of trusted sensors. Incorporating CAVIC into a Hyperledger-based healthcare testbed, we show reduced latency, lower computational costs, and better responsiveness, all of which add to the safety and efficiency of managing patients remotely.
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