Eclipse: An End-to-End Platform for Low-Cost, Hyperlocal Environmental Sensing in Cities

被引:18
作者
Daepp, Madeleine I. G. [1 ]
Cabral, Alex [2 ]
Ranganathan, Vaishnavi [1 ]
Iyer, Vikram [3 ]
Counts, Scott [1 ]
Johns, Paul [1 ]
Roseway, Asta [1 ]
Catlett, Charlie [4 ]
Jancke, Gavin [1 ]
Gehring, Darren [1 ]
Needham, Chuck [1 ]
von Veh, Curtis [1 ]
Tran, Tracy [1 ]
Story, Lex [1 ]
D'Amone, Gabriele [1 ]
Nguyen, Bichlien H. [1 ]
机构
[1] Microsoft Res, Redmond, WA 98052 USA
[2] Harvard SEAS, Cambridge, MA USA
[3] Univ Washington, Seattle, WA 98195 USA
[4] Univ Illinois, Discovery Partners Inst, Chicago, IL USA
来源
2022 21ST ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2022) | 2022年
关键词
Internet of Things; Air Pollution; Smart Cities; Sensor Networks; URBAN AIR-POLLUTION; PARTICULATE MATTER; HUMAN HEALTH; QUALITY; EXPOSURE; PM2.5; CALIBRATION; SENSORS; DESIGN; SYSTEM;
D O I
10.1109/IPSN54338.2022.00010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pressure. A modular design further includes four low-cost gas sensors - Ozone (O-3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 - 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5 and O-3) or education (NO2 and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.
引用
收藏
页码:28 / 40
页数:13
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