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Predictive Modeling of Bacteria-Based Nanonetwork Performance Using Simulation-Driven Machine Learning and Genetic Algorithm Optimization
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DOI：10.1002/adts.202501275.png)
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Abstract
En 中文
Bacteria-based nanonetwork (BN) offers a biologically inspired solution for enabling information exchange between nanomachines (NMs) in environments where traditional communication methods are ineffective. This study presents a 2D simulation model of a BN system that captures the chemotactic behavior of a single Escherichia coli (E. coli) bacterium navigating from a transmitter (TX) toward a receiver (RX) under varying environmental conditions. Key parameters, which are chemoattractant release rate (Q), TX-RX distance (d), and bacterial lifespan (), are systematically varied to evaluate their impact on communication performance, measured in terms of reach time and success rate. To enable accurate performance prediction without the need for computationally expensive repeated simulations, an analytical model is constructed using various machine learning (ML) techniques, including Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Hyperparameters of MLP are optimized using a Genetic Algorithm (GA), significantly enhancing predictive accuracy and training stability. The results demonstrate the effectiveness of integrating dynamic simulation with data-driven modeling and hyperparameter optimization to represent complex system behavior. This framework offers valuable design insights for BN system development and supports the creation of efficient, scalable nanonetworks.
Keywords:
bacteria-based molecular communication
escherichia coli
genetic algorithm
machine learning
molecular communication
nanonetworks
nanotechnology
Journal
IF:
2.9
Papers:
488
Citations:
3.6K
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