Work place: Department of Computer Science, Surendranath Evening College, Kolkata, 700009, India
E-mail: contactathimadri@gmail.com
Website:
Research Interests:
Biography
Himadri Nath Saha is a distinguished academician and researcher with over 24 years of experience in teaching, research and academic leadership. Recognized among Stanford University and Elsevier’s world’s top 2% scientists, he holds a Ph.D. (Engineering) from Jadavpur University in secured MANET routing protocols, along with degrees from Jadavpur University, IIEST Shibpur, and an MBA. His research spans AI/ML, IoT, cybersecurity, cryptography, UAVs, network security, and algorithms. Dr. Saha has authored over 130 publications with 2,500+ citations, holds multiple patents and copyrights, and has written more than 10 textbooks. A Senior Member of IEEE and Fellow of IETE and IEI, he has served as visiting professor/research scientist at institutions in the USA and Germany, and continues to contribute globally through research collaborations, invited talks, editorial roles, and academic governance.
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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