Empowering Nanoscale Connectivity through Molecular Communication: A Case Study of Virus Infection

2025-08-06

AI Summary

Addressing challenges in epidemic control—including difficulty modeling viral transmission, weak real-time detection and localization of infected individuals, and delayed identification of viral mutations—this paper proposes a molecular communication (MC)-enabled Internet of Bio-Nano Things (IoBNT) framework. Methodologically, it establishes a macro-micro cross-scale molecular channel model, integrates ORF3a protein sequence analysis with signal processing techniques, and designs a dynamic communication protocol and nanoscale localization algorithm tailored to viral spread. Innovatively applying MC theory to viral surveillance, the framework enables high-fidelity transmission simulation, precise detection and spatial localization of infected individuals, and early identification of potential mutation sites. Simulation results demonstrate significant improvements in detection sensitivity and mutation classification accuracy. This work establishes a verifiable, IoBNT-driven paradigm for precision epidemic prevention and control.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Bio-inspired Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Models for Web evolution

Abstract

The Internet of Bio-Nano Things (IoBNT), envisioned as a revolutionary healthcare paradigm, shows promise for epidemic control. This paper explores the potential of using molecular communication (MC) to address the challenges in constructing IoBNT for epidemic prevention, specifically focusing on modeling viral transmission, detecting the virus/infected individuals, and identifying virus mutations. First, the MC channels in macroscale and microscale scenarios are discussed to match viral transmission in both scales separately. Besides, the detection methods for these two scales are also studied, along with the localization mechanism designed for the virus/infected individuals. Moreover, an identification strategy is proposed to determine potential virus mutations, which is validated through simulation using the ORF3a protein as a benchmark. Finally, open research issues are discussed. In summary, this paper aims to analyze viral transmission through MC and combat viral spread using signal processing techniques within MC.

Problem

• Model viral transmission using molecular communication
• Detect viruses and infected individuals at different scales
• Identify virus mutations through signal processing techniques

Innovation

• Model viral transmission via molecular communication
• Detect virus using microscale localization mechanisms
• Identify mutations with ORF3a protein simulation