BlueAer: A fine-grained urban PM2.5 3D monitoring system using mobile sensing

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

et al. 2019

Given the increasingly serious air pollution problem, the monitoring of air quality index (AQI) in urban areas has drawn considerable attention. This paper presents ImgSens-ingNet, a vision guided aerial-ground sensing system, for finegrained air quality monitoring and forecasting using the fusion of haze images taken by the unmanned-aerial-vehicle (UAV) and the AQI data collected by an on-ground three-dimensional (3D) wireless sensor network (WSN). Specifically, ImgSensingNet first leverages the computer vision technique to tell the AQI scale in different regions from the taken haze images, where haze-relevant features and a deep convolutional neural network (CNN) are designed for direct learning between haze images and corresponding AQI scale. Based on the learnt AQI scale, ImgSensingNet determines whether to wake up on-ground wireless sensors for small-scale AQI monitoring and inference, which can greatly reduce the energy consumption of the system. An entropybased model is employed for accurate real-time AQI inference at unmeasured locations and future air quality distribution forecasting. We implement and evaluate ImgSensingNet on two university campuses since Feb. 2018, and has collected 17,630 photos and 2.6 millions of AQI data samples. Experimental results confirm that ImgSensingNet can achieve higher inference accuracy while greatly reduce the energy consumption, compared to state-of-the-art AQI monitoring approaches.Index Terms-Mobile sensing, air quality index, computer vision, sensor networks, unmanned aerial vehicle (UAV).

Section: Taxonomy a Aqi Monitoring Methodsmentioning

confidence: 99%

“…In [6], [7], crowd-sourcing and a Gaussian process model are used for 2D AQI inference. [10] extends the inference to 3D space by using a random walk model. A fine-grained AQI distribution model is proposed in [14] for real-time AQI estimation over a 3D space.…”

Section: B Aqi Inference At Unmeasured Locationsmentioning

confidence: 99%

ImgSensingNet: UAV Vision Guided Aerial-Ground Air Quality Sensing System

Yang¹,

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

et al. 2019

“…To ensure that the location selection is not affected by the specific environment setting, T should be set large enough to integrate all possible stochastic situations based on Eqn. (8).…”

Section: Location Selection Strategymentioning

confidence: 99%

“…Therefore, it is preferred that large number of low-cost tiny sensing devices are deployed to provide air quality sensing for the regions with complicated terrain [8], [9]. Since the deployment of tiny sensing devices can be dense and the data collection can be frequent, the air quality distribution can be updated with low latency and high resolution [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fine-Grained Air Quality Sensing Networks in Smart City: Design, Implementation, and Optimization

Bai

IEEE Internet Things J.

et al. 2019

Driven by the increasingly serious air pollution problem, the monitoring of air quality has gained much attention in both theoretical studies and practical implementations. In this paper, we present the architecture, implementation and optimization of our own air quality sensing system, which provides realtime and fine-grained air quality map of the monitored area. As the major component, the optimization problem of our system is studied in detail. Our objective is to minimize the average joint error of the established real-time air quality map, which involves data inference for the unmeasured data values. A deep Q-learning solution has been proposed for the power control problem to reasonably plan the sensing tasks of the power-limited sensing devices online. A genetic algorithm has been designed for the location selection problem to efficiently find the suitable locations to deploy limited number of sensing devices. The performance of the proposed solutions are evaluated by simulations, showing a significant performance gain when adopting both strategies.

“…10. The greedy algorithm aims to select the nearest target cube in M to generate the trajectory [9], while sequential selection is done by selecting cubes from the bottom (or left) to the top (or right) in order [11].…”

Section: B Performance Analysismentioning

confidence: 99%

Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing

Yang

Zheng

IEEE Internet Things J.

et al. 2018

145

Abstract-Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm.