AI-Driven Communication and Monitoring Framework for Extraterrestrial Microbial Containment Using Cellular Automata Simulation

Abstract

Biosecurity and environmental safety of planetary environments and extraterrestrial habitats demand intelligent and autonomous systems with the potential of identifying, modelling, and reducing the threat of microbial contamination. The present study suggests an AI-driven communication/monitoring framework, which would combine simulation of invasion of the microbial spread by cellular automata with the IoTpowered containment and early-warning systems. It is a spatial-temporal model of the hypothetical extraterrestrial microbes in closed, such as spacecraft modules, bases on the moon, or biosafety laboratories, which allows predictive evaluation of contamination curves. A convolutional-LSTM network uses cellular automata data to predict hotspots of contamination and calculate a dynamic containment risk index to bootstrap automated alerts over an adaptive IoT communication channel with 
resulting delay-tolerant networking as an insurance of interplanetary reliability. The experimental simulations with different environmental and containment parameters proved that the proposed CA-AI simply recombination decreased the possibility of breaches by a factor of 35 and the detection latency was shortened by one-fifth relative to the traditional threshold-based systems. The findings confirm the capability of the framework in detecting the presence of the early-stage microbial growth, prioritisation 
of communication in low-bandwidth environment, and preemptive governance of the situation. The study provides a scalable and robust intelligent planetary biosecurity system model that has a basis in autonomous microbial surveillance during subsequent interplanetary missions and alien environments, where autonomous systems might not be capable of quickly and effectively intervening when problems arise because of the distance of communication with the home.