Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data

Currently, air pollution has become the tremendous problem humankind has ever faced. Countries governments have struggled with conflicting demands from industries benefits and people/natural health. Scientists have investigated to find solutions that can balance these demands. Along with these directions, this research introduces a convenient and economical solution for estimating PM2.5 at the current time and predicting PM2.5 in a short- and medium-term period just by using images captured from personal devices (e.g., smartphones, cameras, lifelog cameras). The proposed method aims to leverage the association between urban nature (e.g., street greenness, street building), urban traffic (e.g., the volume of vehicles), and air pollution (e.g., PM2.5) to discover a set of periodic-frequent patterns and to build the PM2.5 estimation model. The estimated PM2.5, together with a set of patterns, is utilized to predict the PM2.5 in the short-term future. Evaluation running on different datasets collected from India and Vietnam shows the productivity of the proposed method.