Quantum-Enabled Energy Efficient Secure Data Transmission for Bio-Cyber Interfaces with QIoE and QKD for Future Healthcare Systems

The integration of Quantum Internet of Everything (QIoE) with Bio-Cyber Interfaces (BCIs) offers revolutionary possibilities in healthcare, enabling precise real time neural signal processing and advanced medical treatments. This paper addresses the critical challenges of security, privacy, and energy efficiency in QIoE-driven BCIs. This paper proposes a secure data transmission framework using Quantum Key Distribution (QKD) to protect sensitive neural data, ensuring low latency, high-security communication between BCIs and quantum-enhanced IoT devices. Additionally, an energy optimization model is introduced, leveraging quantum-enhanced energy harvesting techniques to minimize the power consumption of QIoE enabled devices embedded within BCIs. Through a combination of quantitative analysis and simulation-based performance evaluations, our proposed system demonstrates significant improvements in secure communication, energy efficiency, and real-time processing capabilities over classical IoT solutions. These advancements have direct implications for applications such as neuro prosthetics and remote health monitoring, where secure, energy-efficient, and privacy preserving systems are crucial. This research establishes a foundation for scalable, secure, and energy-efficient QIoE technologies, driving the evolution of future healthcare systems.

Authors

Institutions

Publication Details

Published
2025-04-20
DOI
https://doi.org/10.1109/sustech63138.2025.11025625
Primary Topic
Molecular Communication and Nanonetworks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantum-Enabled Energy Efficient Secure Data Transmission for Bio-Cyber Interfaces with QIoE and QKD for Future Healthcare Systems

Deepika Rana
1 links
Molecular Communication and Nanonetworks
article

Quantum-Enabled Energy Efficient Secure Data Transmission for Bio-Cyber Interfaces with QIoE and QKD for Future Healthcare Systems

Deepika Rana
article en

Abstract

The integration of Quantum Internet of Everything (QIoE) with Bio-Cyber Interfaces (BCIs) offers revolutionary possibilities in healthcare, enabling precise real time neural signal processing and advanced medical treatments. This paper addresses the critical challenges of security, privacy, and energy efficiency in QIoE-driven BCIs. This paper proposes a secure data transmission framework using Quantum Key Distribution (QKD) to protect sensitive neural data, ensuring low latency, high-security communication between BCIs and quantum-enhanced IoT devices. Additionally, an energy optimization model is introduced, leveraging quantum-enhanced energy harvesting techniques to minimize the power consumption of QIoE enabled devices embedded within BCIs. Through a combination of quantitative analysis and simulation-based performance evaluations, our proposed system demonstrates significant improvements in secure communication, energy efficiency, and real-time processing capabilities over classical IoT solutions. These advancements have direct implications for applications such as neuro prosthetics and remote health monitoring, where secure, energy-efficient, and privacy preserving systems are crucial. This research establishes a foundation for scalable, secure, and energy-efficient QIoE technologies, driving the evolution of future healthcare systems.

Chandigarh University (IN)
Affordable and clean energy
Openalex Percentile: Top 87%
Molecular Communication and Nanonetworks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.