IoT-based Noise Monitoring using Mobile Nodes for Smart Cities

• URL: http://arxiv.org/abs/2509.00979v1
• Date: Sun, 31 Aug 2025 20:13:09 GMT
• Title: IoT-based Noise Monitoring using Mobile Nodes for Smart Cities
• Authors: Bhima Sankar Manthina, Shreyash Gujar, Sachin Chaudhari, Kavita Vemuri1, Shivam Chhirolya,
• Abstract summary: This paper presents a scalable, low-cost, IoT-based, real-time environmental noise monitoring solution using mobile nodes.<n>The system utilizes a low-cost sound sensor integrated with GPS-enabled modules to collect geotagged noise data at one-second intervals.<n>The proposed system demonstrates the potential for widespread deployment of IoT-based noise sensing networks in smart cities.
• Score: 0.45671221781968324
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Urban noise pollution poses a significant threat to public health, yet existing monitoring infrastructures offer limited spatial coverage and adaptability. This paper presents a scalable, low-cost, IoT-based, real-time environmental noise monitoring solution using mobile nodes (sensor nodes on a moving vehicle). The system utilizes a low-cost sound sensor integrated with GPS-enabled modules to collect geotagged noise data at one-second intervals. The sound nodes are calibrated against a reference sound level meter in a laboratory setting to ensure accuracy using various machine learning (ML) algorithms, such as Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Polynomial Regression (PR), Segmented Regression (SR), Support Vector Regression (SVR), Decision Tree (DT), and Random Forest Regression (RFR). While laboratory calibration demonstrates high accuracy, it is shown that the performance of the nodes degrades during data collection in a moving vehicle. To address this, it is demonstrated that the calibration must be performed on the IoT-based node based on the data collected in a moving environment along with the reference device. Among the employed ML models, RFR achieved the best performance with an R2 of 0.937 and RMSE of 1.09 for mobile calibration. The system was deployed in Hyderabad, India, through three measurement campaigns across 27 days, capturing 436,420 data points. Results highlight temporal and spatial noise variations across weekdays, weekends, and during Diwali. Incorporating vehicular velocity into the calibration significantly improves accuracy. The proposed system demonstrates the potential for widespread deployment of IoT-based noise sensing networks in smart cities, enabling effective noise pollution management and urban planning.

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