Noise Pollution Monitoring IoT — Implementation & Analysis

Abstract — Noise Pollution Monitoring IoT

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 R 2 \mathrm{R}^{2} 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.

Operational Methodology & Framework

Noise pollution, also known as environmental noise or sound pollution, refers to unwanted or excessive sound that disrupts human activities and negatively impacts human health . The known sources of noise pollution include transportation (such as road traffic), industrial activities, construction, and urban crowding . Unlike other forms of pollution, noise pollution is invisible and, as a result, is often overlooked despite its severe consequences on public health. Prolonged exposure to high noise levels can lead to hearing loss, cardiovascular diseases, sleep disturbances, and increased stress levels .

Experimental Findings & Validation

Figure 1: Conceptual Model & Experimental Workflow for Noise Pollution Monitoring Iot 2. System Architecture & Functional Block Diagram Given the widespread impact of noise pollution, continuous and accurate monitoring is essential. Noise pollution monitoring enables policymakers to implement effective mitigation strategies, such as deploying green buffers, sound-absorbing infrastructure, and enforcing zoning regulations to improve urban living conditions. Traditionally, noise levels are measured using stationary sound meters deployed by the Central Pollution Control Board (CPCB) in India . As part of this effort, CPCB established the National Ambient Noise Monitoring Network (NANMN), which operates in seven metropolitan cities with monitors deployed at 35 locations across Delhi, Hyderabad, Kolkata, Mumbai, Lucknow, Bangalore, and Chennai. While this system provides reliable data, it often lacks real-time adaptability and sufficient spatial coverage.

Scope & Practical Applications

• Real-time automated control and signal telemetry
• Embedded verification testbeds for institutional research
• High-throughput data streaming and anomaly alerting
• Performance optimization and resource consumption minimization
• Full integration ready for IEEE dissertation submission