Eshita’s Status Report for 2/11

This week I focused on researching cloud implementations and alternatives for our sensor data collection and for hosting our classification algorithm. I also focused on the proposal presentation with Aditti and Caroline, where we met several times to go over various design details. There are several alternatives to consider: we could collect the data directly onto the Arduino serial monitor for training purposes, and send telemetry data for classification once our model is implemented using payloads with Azure. We could also send the same payloads for both collecting the training data and for the actual classification. The Machine Learning model for classification would be imported into a Jupyter/Python instance. I also found a project which utilizes Python notebooks and libraries along with AWS IoT to read data from sensors, and am spending more time doing trade studies between the two. I am more drawn towards AWS because of my prior experience with it, but doing research on AWS and Azure shows advantages for both. They are economical solutions that offer a lot of message-sending abilities from various IoT devices to their dashboards. The main tradeoff I envision currently is the difference in ML capabilities between Azure and AWS. While AWS is less friendly to beginners and more costly, Azure’s ML capabilities might be harder to implement with their IoT hub. My goal for week 5 is to create an instance with an Arduino board I have lying around and see if I can send some basic data about an LED light being on/off on Azure, on schedule with the Gantt chart presented during our proposal.