Our approach for those challenges is to create an IoT system based on the Nordic Thingy:53™,development board that will run a machine learning model trained using the Edge Impulse platform that can detect the sound of breaking glass and send a notification via Bluetooth when this event is detected. We have narrowed our hardware selection to the Nordic Thingy:53™ as it integrates multiple sensors (including an accelerometer, gyroscope, microphone, and temperature sensor) onto a single board, which will simplify our data collection process. In addition, the Nordic Thingy:53™ has built-in Bluetooth Low Energy (BLE) connectivity, which will allow us to easily send notifications to nearby smartphones or other devices when our glass/window breaking detection system is triggered. The Nordic Thingy:53 is powered by the nRF5340 SoC, Nordic Semiconductor’s flagship dual-core wireless SoC that combines an Arm® Cortex®-M33 CPU with a state-of-the-art floating point unit (FPU) and Machine Learning (ML) accelerator. This will enable us to run our machine learning model locally on the Thingy:53, without needing to send data to the cloud for processing.