Presented by: Sai Yamanoor, DesignAbly

Summary: Citizen Science or Community Driven efforts can provide data that can be used to predict or identify common and repeatable events. For e.g.: Images collected from community experiments have been used to identify certain insect species etc. Also, satellite images have been used to predict start of wildfires. Likewise, it is possible to use data community collected data to predict events in Air Quality Monitoring or detect anomalies. Powerful computing resources are required to build and train a model to predict events. With the recent advancements in the field of Machine Learning and Computing power, it is possible to run neural networks on hardware that costs less than $20. This enables conducting analysis on the collected data at the source instead of transferring it to a centralized “data lake.'' It also reduces costs as it helps avoid certain cloud computing and related infrastructure costs. This concept of conducting the data analysis at the source instead of the cloud is called Edge Computing. Edge computing can be a great resource in citizen science experiments because it helps avoid “reinventing the wheel”. In citizen science applications, the arrival of lost cost edge computing hardware enables running community driven experiments at a low cost. This also enables verify or re-emulating results observed in pre-existing datasets. In our presentation/poster, we are proposing an open source edge computing platform for citizen science experiments. The platform is centered around the Artemis module that costs US$8. The platform comes with interfaces to accommodate sensors with different outputs like UART, I2C etc. We would also like to demonstrate a use case for the platform using an existing dataset.

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