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Updated: Jan 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

SecureEdge-MedChain: A Post-Quantum Blockchain and Federated Learning Framework for Real-Time Predictive Diagnostics

Sivasubramanian Ravisankar¹, Rajagopal Maheswar²

¹Department of Computer Science and Engineering, Coimbatore Institute of Technology, Coimbatore 641 014, Tamil Nadu, India.

Sensors (Basel, Switzerland)

|October 16, 2025

Summary

This summary is machine-generated.

Med-Q Ledger enhances Internet of Medical Things (IoMT) security and performance using blockchain and post-quantum cryptography. It enables real-time, privacy-preserving analytics for critical applications like predicting infant intestinal complications.

Keywords:

IoMT blockchain colostomy prediction edge computing federated learning latency post-quantum cryptography throughput

Related Experiment Videos

Last Updated: Jan 15, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Computer Science
Cybersecurity
Medical Informatics

Background:

The Internet of Medical Things (IoMT) faces challenges in scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence.
Existing IoMT systems struggle to meet the demands of high-volume data processing and robust security in healthcare.

Purpose of the Study:

Introduce Med-Q Ledger, a novel framework to address limitations in IoMT scalability, data security, and privacy.
Enhance real-time patient monitoring and predictive diagnostics through secure and efficient data analytics.

Main Methods:

Integrated a permissioned Hyperledger Fabric with Holochain DHT for scalability and transactional integrity.
Incorporated post-quantum cryptography (PQC) using CRYSTALS-Di lithium and Kyber Key Encapsulation Mechanisms for data security.
Utilized edge-based federated learning (FL) with autoencoders for privacy-preserving anomaly detection on encrypted gradients.

Main Results:

Achieved high throughput (~3400 TPS) with low latency (~180 ms) and >95% anomaly detection rate.
Demonstrated superior performance in predicting colostomy necessity in preterm infants with a 0.90 F1-score.
Reported an 11% PQC overhead, 25% reduction in emergency surgeries, and 31% lower energy consumption compared to baselines.

Conclusions:

Med-Q Ledger provides a secure, scalable, and privacy-preserving framework for IoMT analytics.
The framework sets a new benchmark for next-generation healthcare deployments, improving patient outcomes and operational efficiency.