Nanomachine placement strategies for detecting Brownian molecules in nanonetworks

In this paper, we consider a nano-biosensor network composed of nano-to-micro scale biological machines distributed over a two-dimensional bounded area. The goal of the nano-biosensor network is to detect a target signal that propagates via Brownian motion in the monitoring area. Three simple nanomachine placement strategies are investigated: random, proportional, and regular placement. In the random placement, nanomachines are distributed randomly over the area. In the proportional placement, more nanomachines are placed where signals appear more frequently. In the regular placement, nanomachines are distributed to maintain a specific distance from adjacent nanomachines. Three placement strategies are evaluated through simulation based on the mean residence time which is defined as the average amount of time that a target signal stays in the monitoring area. Our simulation results show that the regular placement performs best when signal arrival locations follow normal distribution. Simulation results are also provided to show the impact of nanomachine failure on the mean residence time.