Bio-Inspired Quorum Sensing-Based Nanonetwork Synchronization Using Birth-Death Growth Model

Abstract

We construct a bio-inspired nanomachine network via the quorum sensing (QS) mechanism and analyze that nanonetwork from the perspective of global synchronization time and channel capacity. We propose a realistic (stochastic) approach using birth-death-process-based bacterial growth model and compare it to a conventional ideal (deterministic) approach using exponentially-increased bacterial growth model. For the comparative study, we first define a diffusion-based molecular communication channel between bacterial density and autoinducer (AI) concentration as an approximated Gaussian process, and then analyze the presented QS behavior model numerically as well as theoretically. Increases in the bacterial density augment the diffused AI concentration. When the AI concentration satisfies a specified threshold indicating gene expression, almost all bacteria in that colony represent a collective QS behavior such as biofilm formation. Compared to the ideal approach that is simple but not feasible in real life given the limited resources (e.g., food), the realistic approach is complex but better at representing real and probabilistic QS nature, less sensitive at gene expression, and so more suitable for global synchronization analysis. Via simulation, we evaluate the proposed model in terms of AI concentration versus bacterial density, synchronization time, and information sensing capacity, and demonstrate its superiority over the traditional model.