The CAMALIOT mobile app for crowdsourcing is used to collect data transmitted by the Global Satellite Navigation System (GNSS) for scientific applications such as weather forecasting and improving positional accuracy

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The CAMALIOT mobile app was developed at IIASA as part of the CAMALIOT project, which was funded under the European Space Agency’s NAVISP Element 1 program and led by the Space Geodesy group at ETH Zurich. Data from the GNSS are primarily used for navigation purposes, and many systems around the world now rely on the large constellation of satellites (including GPS and Galileo) for location-based services. The GNSS receiver in your smartphone can receive signals from these satellites and can be used in many apps, e.g., Google Maps for navigating from A to B.     

The idea behind the CAMALIOT mobile app is to collect the raw information being transmitted by these GNSS satellites and then use them for scientific research in applications that go beyond navigation purposes. Although there is a large network of geodetic stations around the world collecting this information on a regular basis, there are large gaps in coverage. Hence the purpose of the CAMALIOT mobile app is to use crowdsourcing to fill in these gaps.

phones © CAMALIOT

The app can be used by anyone with an Android mobile phone (operating system version 7 and higher) since access to the raw data from the GNSS is only possible for these types of phones. In addition, some Android mobile phones have dual receiver chipsets, which means they can receive information from the GNSS at two frequencies, providing much more information than a single receiver smartphone. The app can be downloaded from the Google Playstore or through a direct link from the CAMALIOT website.

The app shows the satellites from which it is currently collecting data and it also provides a quality indicator. The best quality data will come from a phone that is horizontal and outside in a location that has a clear view of the sky. However, the app works in ‘continuous mode’ so you can have the app run in the background and send data to the server on a regular basis so there will be times when the data are of higher quality than other times.

IIASA ran a crowdsourcing campaign from March to July 2022, which collected 131 billion measurements from more than 12,000 users around the world. We had great participation from many places, but you can see from the map that the app was popular in Brazil!

CAMALIOT map © CAMALIOT

We are running more campaigns and would encourage you to take part. Check out the CAMALIOT website for information on any ongoing campaigns. Note that you can also download your own data from the app and use it for your own purposes if you are a GNSS researcher or interested in working with GNSS data.

CAMALIOT

Get it on Google Play

PUBLICATIONS

Crocetti, L., Soja, B., Kłopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). Machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological data. In: 1st Workshop on Data Science for GNSS Remote Sensing, 13-15 June 2022, Potsdam, Germany.

Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., & Sturn, T. (2022). Machine learning based modelling of tropospheric parameters with GNSS enhanced by meteorological data. In: ESA Living Planet Symposium, 23-27 May 2022, Bonn, Germany.

Crocetti, L., Soja, B., Klopotek, G., Awadaljeed, M., Rothacher, M., See, L. , Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). Machine learning and meteorological data for spatio-temporal prediction of tropospheric parameters. In: EGU General Assembly 2022, 23-27 May 2022, Vienna.

Klopotek, G., Soja, B., Awadaljeed, M., Crocetti, L., Rothacher, M., See, L. , Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). Total Electron Content Monitoring Complemented with Crowdsourced GNSS Observations. In: EGU General Assembly 2022, 23-27 May 2022, Vienna.

See, L. , Soja, B., Kłopotek, G., Awadaljeed, M., Crocetti, L., Pan, Y., Rothacher, M., Weinacker, R., Sturn, T., McCallum, I. , & Navarro, V. (2022). The CAMALIOT project. In: FFG Informationsveranstaltung zu NAVISP, 10 May, 2022, Vienna, Austria.

Navarro, V., Grieco, R., Soja, B., Nugnes, M., Klopotek, G., Tagliaferro, G., See, L. , Falzarano, R., Weinacker, R., & VenturaTraveset, J. (2021). Data Fusion and Machine Learning for Innovative GNSS Science Use Cases. Proceedings of the 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021) 2656-2669. 10.33012/2021.18115.