Sudip Kumar Adhikari

Work place: Department of CSE, Kalyani Government Engineering College, Nadia, 741235, India

E-mail: sudipadhikari@ieee.org

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Biography

Sudip Kumar Adhikari passed B. Tech in Computer Science & Engineering from Vidyasagar University. He obtained the M.E. and Ph.D. degree in Computer Science & Engineering from Jadavpur University. He had more than 20 years of teaching experiences. He is currently an assistant professor in Computer Science & Engineering Department of Kalyani Government Engineering College, Kalyani, India. He had published a good number of research papers in reputed International Journals and Conferences. His research interest includes Medical image processing, IoT, Machine Learning. Dr. Adhikari is a Senior Member of IEEE and member of Institute of Engineers. He served as a reviewer in several International conferences and also in several reputed international journals like, Applied Soft Computing, Elsevier, IET Computer Vision, IEEE Access, and IEEE Transaction on Fuzzy System etc. He has been the member of the organizing and technical program committees of several national and international conferences.

Author Articles
Adaptive Context-Aware Replication Mechanism for Distributed IoT Environments

By Mrinal Kanti Mahato Bikash Choudhury Tanushree Garai Sudip Kumar Adhikari Himadri Nath Saha

DOI: https://doi.org/10.5815/ijcnis.2026.04.06, Pub. Date: 8 Aug. 2026

The rapid evolution of the Internet of Things (IoT), supported by the convergence of cloud, edge and mist computing layers, opens new avenues for delivering reliable and responsive services to distributed smart devices. However, ensuring efficient and adaptive service replication in such resource-constrained and dynamically changing IoT environments remains a significant challenge. To tackle this, we introduce Elastic Context-Aware Replication (ECAR), an intelligent replication strategy tailored for IoT systems. ECAR dynamically redistributes services across the IoT continuum by leveraging both physical and logical contextual information. Unlike traditional replication schemes, ECAR continuously adapts to real-time workload fluctuations and network conditions, ensuring low latency and efficient resource usage. ECAR’s effectiveness is demonstrated through a comparative evaluation involving diverse IoT deployment scenarios, including Cloud-Intensive Replication (CIR), Cloud-Edge-Intensive Replication (CEIR) and Cloud-Edge-Access-Intensive Replication (CEAIR), alongside two existing replication strategies, Group-Delay-Aware Replication (GDAR) and Combined Context-Aware Replication (CCA). The evaluation shows ECAR achieving up to 18% reduction in service drop rates, 82% improvement in allocation efficiency and 82% better resource utilization. These results underline ECAR’s effectiveness in supporting scalable, reliable and latency-aware service delivery for IoT deployments.

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