Air pollution monitoring model in real-time with cloud based channel ranging and stego privacy preservation

Abstract

In today's rapidly urbanizing world, the monitoring of air pollution has become imperative for safeguarding public health and the environment. Monitoring air quality in polluted environments is crucial for human health, but traditional methods using meteorological equipment have limitations in complex terrains and are costly. This study proposes a novel approach to address air pollution monitoring in urban areas by visually assessing the haziness of distant photos. This research explores the correlation between air quality indexes (e.g., AQI, PM2.5, PM10) and haziness levels in monitoring images. Results indicate that objective indicators accurately reflect air pollution levels, regardless of image size. To implement this observation practically, we introduce a new method called the "Channel ranging based prediction model in cloud with steganography-based privacy preservation framework." This model calculates a ratio between dark and bright channel information in scaled images, serving as a visual index of air pollution. By utilizing cloud infrastructure and sophisticated channel ranging mechanisms, the system intends to enhance the efficiency and reliability of air pollution monitoring, addressing both environmental concerns and privacy considerations in data transmission and storage. Experimental findings demonstrate the effectiveness of this metric in terms of correlation, computational speed, and classification accuracy compared to existing metrics. Particularly, the comparison of Pearson Correlation Coefficients (PCCs) across various image quality indices and subjective ratings from the RHID_AQIs dataset reveals that the Channel ranging based cloud model achieves a PCC of 0.97, indicating strong correlation with image quality.