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.

翻译：城市噪声污染对公众健康构成重大威胁，然而现有监测基础设施在空间覆盖率和适应性方面存在局限。本文提出一种可扩展、低成本、基于物联网的实时环境噪声监测方案，该方案采用移动节点（搭载于移动车辆上的传感器节点）。系统利用集成GPS模块的低成本声音传感器，以一秒钟为间隔采集地理标记的噪声数据。在实验室环境中，通过多种机器学习算法（如简单线性回归、多元线性回归、多项式回归、分段回归、支持向量回归、决策树和随机森林回归）将声音节点与参考声级计进行校准以确保精度。虽然实验室校准显示出较高精度，但研究表明节点在移动车辆中进行数据采集时性能会下降。为解决此问题，研究证明必须基于移动环境中采集的数据并结合参考设备，在物联网节点上执行校准。在所有采用的机器学习模型中，随机森林回归在移动校准中表现最佳，其R²达到0.937，RMSE为1.09。该系统在印度海得拉巴通过为期27天的三次测量活动完成部署，共捕获436,420个数据点。结果揭示了工作日、周末及排灯节期间噪声的时空变化特征。将车辆速度纳入校准过程可显著提升精度。所提出的系统展现了在智慧城市中大规模部署物联网噪声传感网络的潜力，为有效的噪声污染管理和城市规划提供了技术支持。