Journal of Soft Computing Paradigm
IRO Journals
Volume 6 — Issue 4 — December 2024

A Scalable and Secure Data Analytics Framework for Decentralized Autonomous Healthcare Systems using Fuzzy Logic, Blockchain Sharding, Dynamic Network Slicing, and ECC

https://doi.org/10.36548/jscp.2024.4.007
Venkata Surya Bhavana Harish Gollavilli
Under Armour, Maryland
Kalyan Gattupalli
Yash Tek inc, Ontario
Harikumar Nagarajan
Global Data Mart Inc (GDM), New Jersey
Poovendran Alagarsundaram
Humetis Technologies Inc, Kingston, NJ
Surendar Rama Sitaraman
Intel Corporation, California
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How to Cite

Gollavilli, Venkata Surya Bhavana Harish, Kalyan Gattupalli, Harikumar Nagarajan, Poovendran Alagarsundaram, and Surendar Rama Sitaraman. 2025. “A Scalable and Secure Data Analytics Framework for Decentralized Autonomous Healthcare Systems Using Fuzzy Logic, Blockchain Sharding, Dynamic Network Slicing, and ECC”. Journal of Soft Computing Paradigm 6 (4): 412-32. https://doi.org/10.36548/jscp.2024.4.007.
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Keywords

— Fuzzy Logic
— Blockchain Sharding
— Dynamic Network Slicing
— Elliptic Curve Cryptography (ECC)
— Decentralized Healthcare
Published: 07-02-2025

Abstract

Healthcare systems are increasingly challenged by the complexity of managing scalable and secure data. Addressing this, the proposed novel framework integrates elliptic curve cryptography (ECC), dynamic network slicing, blockchain sharding, and fuzzy logic to enable efficient, adaptive, and secure data analytics. The framework processes healthcare data from IoT devices, wearable sensors, and patient records, ensuring real-time analytics, scalability, and robust security. By utilizing blockchain sharding for scalability, ECC for secure encryption, and fuzzy logic for decision-making, the architecture effectively overcomes the constraints of traditional systems. The results demonstrate significant improvements, including enhanced security (98%), reduced latency (10 ms), and higher scalability (5000 TPS). These advancements establish a reliable, decentralized foundation for predictive healthcare insights, resource optimization, and adaptive governance, setting a benchmark for modern healthcare data management systems.

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