Target Detection in Clustered Mobile Nanomachine Networks

TL;DR

This paper develops an analytical framework for target detection in clustered nano-machine networks using molecular communication, highlighting the influence of clustering on detection performance and providing insights for optimal system design.

Contribution

It introduces a novel Poisson cluster process-based model for analyzing target detection in 3D nano-machine networks, including derivation of detection probabilities and validation through simulations.

Findings

Detection probability is significantly affected by clustering.

Spatial arrangement impacts detection performance.

Analytical expressions match simulation results.

Abstract

This work focuses on the development of an analytical framework to study a diffusion-assisted molecular communication-based network of nano-machines (NMs) with a clustered initial deployment to detect a target in a three-dimensional (3D) medium. Leveraging the Poisson cluster process to model the initial locations of clustered NMs, we derive the analytical expression for the target detection probability with respect to time along with relevant bounds. We also investigate a single-cluster scenario. All the derived expressions are validated through extensive particle-based simulations. Furthermore, we analyze the impact of key parameters, such as the mean number of NMs per cluster, the density of the cluster, and the spatial spread, on the detection performance. Our results show that detection probability is greatly influenced by clustering, and different spatial arrangements produce varying performances. The results offer a better understanding of how molecular communication systems should be designed for optimal target detection in nanoscale and biological environments.