A Blueprint For a Cost-Efficient IoT-Enabled Biotech Lab

Abstract

Implementations of Internet of Things (IoT) and automation have already started making their way into the biotechnology industry, but remain restricted to well-funded pharmaceutical companies and laboratories. Universities across the world have been leading innovators in this field and yet a large proportion of academic laboratories are primitive in design. This is due mainly to the investment requirements to execute such upgrades, as well as the costs of training to use high-end infrastructure. This work aims to provide basic IoT solutions for biotechnology labs at a university level. This paper provides detailed insights to several problems laboratories face around the world, including automatic storage of equipment-generated data, pipeline leakages, sensor-related damage of equipment, maintenance of biosafety lab conditions, gas level estimation and basic administrative issues such as logging of equipment (samples, pipette locations, reagents, machinery status, etc.). We attempt to compile the applications of IoT to address these issues while considering the financial constraints of academic laboratories. We discuss the component requirements and approximate cost of implementation for solutions that aim to minimize human errors, thus enabling the reproducibility of results. Furthermore, current developments and future research directions were put forth toward cutting-edge computational applications of machine learning and artificial intelligence in the biotechnology.