Sensor Deployment Recommendation for 3D Fine Grained Air Quality Monitoring Using Semi Supervised Learning
Driven by the increasingly serious air pollution problem, the monitoring of fine grained air quality index (AQI) in urban areas has drawn considerable attention. In this paper, we design a novel algorithm to recommend the placement of sensors for energy efficient AQI monitoring in urban three dimensional (3D) space. Specifically, we first propose an entropy based semi supervised learning (ESSL) model to estimate the AQI distribution of unobserved locations, using the sparse historical spatial temporal data and other features, including 3D coordinates, wind speed and weather conditions. Based on ESSL, we then design an entropy minimization ranking (EMR) algorithm to recommend the best sensor locations for AQI monitoring. Through the emulation on a fine grained AQI dataset, the results demonstrate our scheme can provide energy efficient solutions by using the least number of sensors to achieve higher accuracy than other existing approaches.
2018 IEEE International Conference on Communications (ICC)