Ambient Noise Detection Using a User- Controlled Aerial System with Cloud-Based Alerting and Data Management

Urbanization and industrial growth have significantly increased environmental noise pollution, now recognized as a serious public health concern linked to hearing loss, sleep disturbances, cardiovascular issues, and elevated stress. Traditional noise monitoring systems, typically based on fixed-location sensors, offer limited spatial coverage and adaptability, highlighting the need for a more dynamic and scalable solution. This paper presents a real- time, drone-based noise monitoring system that integrates unmanned aerial systems (UAS), edge computing, and cloud services for mobile and intelligent surveillance. The system employs a multirotor drone equipped with a Raspberry Pi 5 for onboard data processing, a sound level sensor interfaced via an ADS1115 ADC for accurate decibel measurement, a GPS module for geolocation, and a wide-angle camera for contextual video capture. When sound levels exceed a predefined threshold, the system autonomously records video, annotates it with geospatial metadata, and uploads the data to AWS S3 cloud storage, while simultaneously sending real-time alerts via AWS SNS. A web-based dashboard visualizes live noise levels, geo-tagged events, and video feeds, offering actionable insights to stakeholders. Field evaluations demonstrate the system’s effectiveness in detecting excessive noise, delivering low-latency alerts, and optimizing bandwidth through threshold-based data capture. The proposed framework provides a robust, flexible, and scalable approach to urban noise monitoring, with practical implications for public health, regulatory enforcement, and smart city infrastructure.