AQNet: Fine-Grained 3D Spatio-Temporal Air QualityMonitoring by Aerial-Ground WSN

Yuzhe Yang, Zixuan Bai, Zhiwen Hu, Zijie Zheng, Kaigui Bian, and Lingyang SongSchool of Electrical Engineering and Computer Science, Peking University, Beijing, ChinaEmail: {yuzhe.yang, zixuan.bai, zhiwen.hu, zijie.zheng, bkg, lingyang.song}@pku.edu.cn

Abstract—This demo presents AQNet, an aerial-ground wire-less sensor network (WSN) system, for fine-grained air qualitymonitoring and forecasting in urban three-dimensional (3D) area.AQNet contains 200 programmable on-ground PM2.5 sensors for2D baseline monitoring, and an unmanned-aerial-vehicle (UAV)with the same sensor for air quality profiling at different heights.These low-cost sensors are programmed to wake up betweenadjustable time intervals, record and send real-time PM2.5 databack to the central server for data fusion. A learning model isproposed to utilize the data in both spatio-temporal perspectivesto estimate PM2.5 at unmeasured locations and forecast the airquality distribution in the near future. Further, the collecteddata is also used to control and optimize the UAV’s monitoringoperation. For the convenience of user queries, we present thePM2.5 map by a website-based GUI for real-time visualization.AQNet has been realized and deployed on campus of PekingUniversity, and is scalable and energy-efficient to be extended tolarger and more dedicated areas.

I. INTRODUCTION

According to the World Health Organization (WHO), fineparticulate matter (PM2.5) has been identified as the mostharmful particles to public health. A higher PM2.5 can causeharmful health effects for people. Thus, monitoring PM2.5

becomes a critical issue. The more accurate PM2.5 distributionthat can be obtained in a region, the more effective methodswe can find to deal with the air pollution.

PM2.5 monitoring can be summarized into two categories:• Ground stations: A few monitoring stations are set up on

dedicated sites in a city [1]. However, these fixed stationsonly provide coarse-grained 2D monitoring, with severalkilometers between two measurement spots. Existing studyshows that PM2.5 distribution has intrinsic variation withinmeters, and it is preferred to monitor PM2.5 in a specific3D space (e.g., surrounding an office building), rather thancity-wide [2]. The “meter-sliced” fine-grained PM2.5 distri-butions would be more desirable for urban residents.

• Mobile devices: Recent studies proposed to use phones,cars, balloons or drones [3] to monitor the 3D fine-grainedair quality. However, they cannot do long-term monitoringdue to the high energy consumption. Moreover, without 2Dground baseline, the control and operation becomes hard.In this demo, we present AQNet, an aerial-ground wireless

sensor network (WSN) system, to monitor and forecast fine-grained PM2.5 distribution in spatio-temporal perspectives.AQNet contains 200 on-ground devices for 2D baseline moni-toring, and a UAV for vertical distribution profiling. The aerial-ground WSN is a hybrid network that (1) leverages the datacollected from on-ground sensors to control and optimize theUAV’s operation; and meanwhile (2) extends the on-ground

monitoring to 3D space by aerial onboard sensors. To processreal-time data, we set up a central server, where a learning-based model is designed for PM2.5 estimation at unmeasuredlocations and future air quality prediction. Further, we alsodesign a website-based GUI to present the PM2.5 map, whichvisualizes historical, present and future air quality distribution.AQNet has been deployed on campus of Peking University,and can be easily extended to larger and more dedicated areasowing to the high scalability and energy-efficiency.

Sensor Nodes on

UAV and Ground

Aerial-Ground WSN

Monitoring System

Air Quality Data

Raw DatabaseOther Features

Collection (e.g. wind)

Air Pollution

Data Collection Layer

Select training

data in most

recent time stamps

S-kNN

T-kNN

Training Set

Spatial

Temporal

Euclidean Distance

Dynamic Time Warping

Top k at current

time stamp

Top k at previous

Tmin time stamps

Fe

atu

re

No

rma

liza

tio

n

Filling in Missing data

Data Visualization Layer Website Based GUI

UAV Control

& Operation

Deep

Neural

Networks

Outputs

Saliency AnalysisData Analysis Layer

Fig. 1. The overall architecture of AQNet.

II. SYSTEM OVERVIEW

Fig. 1 illustrates the three-layer architecture of AQNet: datacollection, data analysis, and data visualization.

A. Data Collection Layer

On-ground Nodes: On-ground PM2.5 monitoring is based on200 programmable monitoring devices, each of which containsa low-cost laser-based digital PM2.5 sensor, a two-layer circuitboard and a fixed shell structure. These devices can capturereal-time PM2.5 value within ±3% monitor error, and sendthem back to the central server for further data analysis. Torealize high energy-efficiency, the devices are programmed tosleep during most of the time and wake up for data collectionbased on adjustable time intervals that controlled by the server.Aerial Nodes: A UAV carrying sensors is used for verticalprofiling that extends 2D baseline monitoring into 3D space.

Hybrid Network: AQNet performs a hybrid network thatnot only extends the on-ground monitoring to 3D space byaerial onboard sensors, but also leverages the data collectedfrom on-ground sensors to control and optimize the UAV’soperation. Specifically, first-derivative saliency analysis [4] isimplemented to measure the contributions of each input fea-ture. The result is further used for UAV control and operation,which forms a hybrid aerial-ground WSN system.

B. Data Analysis LayerWe propose a learning-based model that aims to estimate

the whole PM2.5 distribution map in real-time and forecast thefuture distribution in the target space, based on the collectedspatio-temporal data and other urban dynamics scraped online.Feature Selection: Each PM2.5 data received by central serveris accompanied by four features (xk, yk, zk, T ), i.e., 3D loca-tion and current time stamp. We also use other features (e.g.,wind) by scraping real-time official data online.Spatial kNN: k neighbors with shortest Euclidean distancespatially are searched first of an unmeasured location.Temporal kNN: k neighbors that have most similar changetrend temporally are also searched, by comparing sequencesof PM2.5 values during certain consecutive time stamps usingDynamic Time Warping (DTW). Temporal trends can helppredict the PM2.5 distribution in future time stamps.Deep Neural Network: We do feature normalization to rescaleall real-valued data into [0, 1] (time lags into [−1, 0]), and usethe feature vectors to train a DNN. The output is the estimatedPM2.5 value. Our model can also forecast future distribution.

We validate the accuracy of our model by comparing it withother state-of-the-art methods, on both our database and a fine-grained 3D dataset [5]. The results are shown in Table I.

TABLE IESTIMATION ERRORS BY DIFFERENT LEARNING APPROACHES

ApproachesRMSE ξ

2D Scenario 3D Scenariok-Nearest Neighbors 6.093 8.410

Deep Neural Networks 7.174 13.789Multi-variable Linear Regression 7.217 11.504

Classification and Regression Tree 6.632 8.519Support Vector Regression 9.767 15.282

Proposed Scheme 4.493 6.481

C. Data Visualization LayerWe design a website-based GUI to present the PM2.5 dis-

tribution map at different time stamps (e.g., previous, presentand future). Moreover, historical data is also categorized intodifferent statistical charts for further visualization 1.

III. DEMO DESCRIPTION

AQNet has been deployed on campus of Peking Universityfor daily monitoring and forecasting, which facilitates andimproves the life quality in campus. For demonstrations, wechoose a typical scenario (a 2D space, then extended into a3D space) to evaluate the system operation and performance.A demo video is also developed for better illustration 2.

1Website-based GUI: http://pkuair.com:20182Demo Video: https://youtu.be/hbJ5JJWhQtc

Fig. 2. The demonstration of AQNet in 2D and 3D scenario, respectively.

Scenario Description: The typical scenario can be describedas a 2D roadside park with only on-ground devices. It can alsobe extended into a 3D monitoring scenario when adding UAVfor height profiling, as shown in Fig. 2(a)(b).Data Visualization: The predicted results are shown on awebsite-based GUI in real-time. Data collected by devices ispresented with their locations as the original data, as shown inFig. 2(c)(d). The proposed learning model is then leveraged todo estimations and generate the real-time PM2.5 distributionmap, as shown in Fig. 2(e)(f). Further, as we consider thetemporal dimension as a feature, we can also forecast the nearfuture distribution over the same area (e.g., after several hoursor several days), as illustrated in Fig. 2(g)(h).Application Prospects: AQNet generates the real-time andthe forecasted fine-grained PM2.5 maps that help people copewith the pollution in advance, e.g., wearing masks. In officebuilding scenarios, AQNet can tell whether to open windowsor turn on air purifiers. It can also help design the ventilationsystems, and recommend suitable sensor deployments [5].

REFERENCES[1] B. Zou et al., “Air pollution exposure assessment methods utilized in

epidemiological studies,” J. Environ. Monit., vol. 11, pp. 475-490, 2009.[2] T. Quang et al., “Vertical particle concentration profiles around urban

office buildings,” Atmos. Chem. Phys., vol. 12, pp. 5017-5030. 2012.[3] Y. Yang et al., “Arms: a fine-grained 3D AQI realtime monitoring system

by UAV,” Proc. of IEEE Globecom’17, Singapore, Dec. 2017.[4] J. Li et al., “Visualizing and understanding neural models in NLP,” in

HLT-NAACL, San Diego, CA, Jun. 2016.[5] Y. Yang, Z. Zheng, K. Bian, L. Song, and Z. Han, “Real-time profiling

of fine-grained air quality index distribution using UAV sensing,” IEEEInternet of Things Journal, vol. 5, no. 1, pp. 186-198, Feb. 2018.