标题： 基于物联网的移动节点噪声监测用于智慧城市 显示英文标题

标题： IoT-based Noise Monitoring using Mobile Nodes for Smart Cities

摘要： 城市噪声污染对公共健康构成重大威胁，但现有的监测基础设施在空间覆盖范围和适应性方面存在局限。本文提出了一种可扩展、低成本、基于物联网的实时环境噪声监测解决方案，使用移动节点（安装在移动车辆上的传感器节点）。该系统利用集成GPS模块的低成本声音传感器，在每秒间隔内收集带有地理标签的噪声数据。声音节点在实验室环境中与参考声级计进行校准，以确保准确性，使用了多种机器学习（ML）算法，如简单线性回归（SLR）、多元线性回归（MLR）、多项式回归（PR）、分段回归（SR）、支持向量回归（SVR）、决策树（DT）和随机森林回归（RFR）。虽然实验室校准显示出高准确性，但研究表明，在移动车辆中进行数据收集时，节点的性能会下降。为了解决这个问题，证明必须根据在移动环境中收集的数据以及参考设备对基于物联网的节点进行校准。在使用的ML模型中，RFR在移动校准中表现最佳，R2为0.937，RMSE为1.09。该系统在印度海得拉巴市通过27天的三次测量活动进行了部署，捕获了436,420个数据点。结果突显了工作日、周末和排灯节期间的时间和空间噪声变化。将车辆速度纳入校准显著提高了准确性。所提出的系统展示了在智慧城市中广泛部署基于物联网的噪声传感网络的潜力，有助于有效的噪声污染管理和城市规划。 显示英文摘要

摘要： 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.