Performance Analysis of Chemotaxis Controllers: Which has Better Chemotaxis Controller, Escherichia coli or Paramecium caudatum?

Swarm-Fuzzy Rule-Based Targeted Nano Delivery Using Bioinspired Nanomachines

Cooperative navigation and swarm decision making take center stage in a broad range of distributed applications with high-environmental and measurement uncertainties. Here, we propose a fuzzy carotid body-inspired nanonetwork (FCBN) of autonomous nanomachines as a swarm computational framework for cooperative navigation and decision making at the nanoscale. The carotid body is a complex sensory system in animals that trigger the global breathing decision based on locally sensed data. Similarly, each nanomachine senses relevant local data and uses a local fuzzy inference system (LFIS) to determine a local environmental trigger that, along with triggers of other nanomachines, collectively estimate the overall environmental state and the appropriate course of action by the nanonetwork. To illustrate, here, we consider the cooperative therapy of a cancer site by targeted drug delivery. The general paradigm, however, is equally applicable to large-scale networks and swarms of cooperative machines. The simulation results show that the FCBN outperforms the benchmark mathematical therapy model by decreasing cancer markers including hypoxic and endothelial cell densities by as much as 99.75% and 33.34%, respectively, while also increasing normoxic cell density by 76.18%. The FCBN also outperforms our earlier reported non-fuzzy BN approach by decreasing hypoxic cell and endothelial cell densities by as much as 29.37% and 30%, respectively, while also increasing normoxic cell density by 45.21%.