A
low-cost airborne sensor mote has been designed for deployment en masse to
characterize atmospheric conditions. The designed environmental sensing mote,
or eMote, was inspired by the natural shape of auto-rotating maple seeds to
fall slowly and gather data along its descent. The eMotes measure and transmit
temperature, air pressure, relative humidity, and wind speed estimates
alongside GPS coordinates and timestamps. Up to 2080 eMotes can be deployed
simultaneously with a 1 Hz sampling rate, but the system capacity increases by
2600 eMotes for every second added between samples. All measured and reported
data falls within accuracy requirements for reporting with both the World
Meteorological Organization (WMO) and the National Oceanic and Atmospheric Administration (NOAA). This paper presents the
design and validation of the eMote system alongside discussions on the
implementation of a large-scale, low-cost sensor network. The eMote represents unprecedented in-situ atmospheric measurement
capabilities with the ability to deploy more than 260 times the number of
sensing units as the most comparable commercially available dropsonde.
References
[1]
Li, N., et al. (2017) The Assessment of Ground-Based Weather Radar Data by Comparison with TRMM PR. IEEE Geoscience and Remote Sensing Letters, 14, 72-76. https://doi.org/10.1109/LGRS.2016.2626320
[2]
Durden S.L. and Perkovic-Martin, D. (2017) The RapidScat Ocean Winds Scatterometer: A Radar System Engineering Perspective. IEEE Geoscience and Remote Sensing Letters, 5, 36-43. https://doi.org/10.1109/MGRS.2017.2678999
[3]
Veefkind, J., et al. (2012) TROPOMI on the ESA Sentinel-5 Precursor: A GMES Mission for Global Observations of the Atmospheric Composition for Climate, Air Quality and Ozone Layer Applications. Remote Sensing of Environment, 120, 70-83.
https://doi.org/10.1016/j.rse.2011.09.027
[4]
Zorer, R., et al. (2013) Daily MODIS Land Surface Temperature Data for the Analysis of the Heat Requirements of Grapevine Varieties. IEEE Transactions on Geoscience and Remote Sensing, 51, 2128-2135.
https://doi.org/10.1109/TGRS.2012.2226465
[5]
Galvin, J.F.P. (2003) Back to Basics: Radiosondes: Part 2-Using and Interpreting the Data. Weather, 58, 387-395. https://doi.org/10.1256/wea.126.02B
Moninger, W.R., Mamrosh, R.D. and Pauley, P.M. (2003) Automated Meteorological Reports from Commercial Aircraft. Bulletin of the American Meteorological Society, 84, 203-216. https://doi.org/10.1175/BAMS-84-2-203
[8]
Liu, Z., Wong, M.S., Nichol, J. and Chan, P.W. (2013) A Multi-Sensor Study of Water Vapour from Radiosonde, MODIS and AERONET: A Case Study of Hong Kong. International Journal of Climatology, 33, 109-120.
https://doi.org/10.1002/joc.3412
[9]
Xinhua, F., Jun, S., Beiguo, L. and Yonggang, T. (2013) Design and Implementation of Dropsonde Wind Measurement System, 2013 IEEE 11th International Conference on Electronic Measurement Instruments, Harbin, 16-19 August 2013, 166-169.
https://doi.org/10.1109/ICEMI.2013.6743007
[10]
Busen, R. (2000) The Release of Dropsondes: A Hazard for Commercial Air Traffic? Air Traffic Control Quarterly, 8, 155-171. https://doi.org/10.2514/atcq.8.2.155
[11]
Lazo, J.K., Lawson, M., Larsen, P.H. and Waldman, D.M. (2011) U.S. Economic Sensitivity to Weather Variability. Bulletin of the American Meteorological Society, 92, 709-720. https://doi.org/10.1175/2011BAMS2928.1
[12]
Xu, B., Zheng, J. and Wang, Q. (2016) Analysis and Design of Real-Time Micro-Environmental Parameter Monitoring System Based on Internet of Things. 2016 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, 15-18 December 2016, 368-371.
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.87
[13]
Ahmed, M.M., Banu, S. and Paul, B. (2017) Real-Time Air Quality Monitoring System for Bangladesh’s Perspective Based on Internet of Things. 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, 7-9 December 2017, 1-5. https://doi.org/10.1109/EICT.2017.8275161
[14]
Mukherji, S.V., Sinha, R., Basak, S. and Kar, S.P. (2019) Smart Agriculture Using Internet of Things and MQTT Protocol. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, 14-16 February 2019, 14-16. https://doi.org/10.1109/COMITCon.2019.8862233
[15]
Mania, F., Santos, C.H.S. and Alvaro, A. (2014) Outlining Low Costs and Open Embedded Systems for RFID in Internet of Things Applications. 2014 IEEE Brasil RFID, Sao Paulo, 25 September 2014, 16-18.
https://doi.org/10.1109/BrasilRFID.2014.7128954
Pounds, P., et al. (2016) Automatic Distribution of Disposable Self-Deploying Sensor Modules. Experimental Robotics, 109, 535-543.
https://doi.org/10.1007/978-3-319-23778-7_35
[19]
Stevenson, R.A., Evangelista, D. and Looy, C.V. (2015) When Conifers Took Flight: A Biomechanical Evaluation of an Imperfect Evolutionary Takeoff. Paleobiology, 41, 205-225. https://doi.org/10.1017/pab.2014.18
[20]
Norberg, R.A. (1973) Autorotation, Self-Stability, and Structure of Single-Winged Fruits and Seeds (Samaras) with Comparative Remark on Animal Flight. Biological Reviews, 48, 561-596. https://doi.org/10.1111/j.1469-185X.1973.tb01569.x
[21]
Lentik, D., Dickson, W.B., Van Leeuwen, J.L. and Dickinson, M.H. (2009) Leading-Edge Vortices Elevate Lift of Autorotating Plant Seeds. Science, 324, 1438-1440.
https://doi.org/10.1126/science.1174196
[22]
World Meteorological Organization (2017) Observing Systems Capability Analysis and Review Tool. https://www.wmo-sat.info/oscar/requirements
[23]
National Oceanic and Atmospheric Administration (2017) NWS Directives System. http://www.nws.noaa.gov/directives/010/010.php
[24]
Hoel, R. (2007) Design Note DN504: FEC Implementation.
http://www.ti.com/lit/an/swra113a/swra113a.pdf