Date of Publication :8th September 2025

Abstract: In this study, we introduce an IOT -based intelligent streetlight system designed to improve safety in urban areas. This system utilizes audio-based event detection via Edge Impulse's machine learning platform and is implemented on the Arduino nano BLE 33 Sense board, which includes a built-in microphone for capturing audio in real-time. Our model is trained to identify distress signals like the word "Help," triggering visual alerts (such as flashing streetlights) to draw attention and potentially notify authorities. This real-time, low-power solution operates locally, ensuring a swift response without the need for constant cloud connectivity. We outline the entire process—from data collection and model training to hardware integration and deployment—and showcase its effectiveness through practical simulations.

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