IoT-based Noise Monitoring using Mobile Nodes for Smart Cities

2025-08-31

AI Summary

Urban noise pollution monitoring faces dual challenges of insufficient spatial coverage and low accuracy in mobile sensing. This paper proposes a real-time vehicular noise monitoring system leveraging IoT-enabled mobile nodes, integrating low-power acoustic sensors, high-precision GPS, and a dynamic calibration mechanism to establish the first adaptive noise perception framework tailored for mobile environments. We innovatively design a spatiotemporal-context-aware online calibration method and incorporate machine learning models—particularly random forest regression—to enhance measurement robustness. Three field campaigns in Hyderabad, India, yielded 436,000 noise samples. Post-calibration, the model achieves an R² of 0.937 and RMSE of only 1.09 dB, significantly outperforming static calibration approaches. The system provides a scalable technical paradigm for urban noise source identification, dynamic assessment, and fine-grained governance in smart cities.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Developing IoT-based mobile noise monitoring for smart cities
• Addressing limited spatial coverage in urban noise pollution
• Improving calibration accuracy for mobile environmental sensors

Innovation

• IoT mobile nodes with GPS for noise data
• Machine learning calibration for accuracy improvement
• Real-time deployment capturing spatial-temporal variations