Code In The Air: Simplifying Sensing on Smartphones
Modern smartphones are equipped with a wide variety of sensors including GPS, WiFi and cellular radios capable of positioning, accelerometers, magnetic compasses and gyroscopes, light and proximity sensors, and cameras. These sensors have made smartphones an attractive platform for collaborativ...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery
2011
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| Online Access: | http://hdl.handle.net/1721.1/62221 https://orcid.org/0000-0002-7470-3265 https://orcid.org/0000-0002-3831-8255 https://orcid.org/0000-0002-1455-9652 |
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| author | Kaler, Timothy Lynch, John Patrick Peng, Timothy Sivalingam, Lenin Ravindranth Thiagarajan, Arvind Balakrishnan, Hari Madden, Samuel R. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kaler, Timothy Lynch, John Patrick Peng, Timothy Sivalingam, Lenin Ravindranth Thiagarajan, Arvind Balakrishnan, Hari Madden, Samuel R. |
| author_sort | Kaler, Timothy |
| collection | MIT |
| description | Modern smartphones are equipped with a wide variety
of sensors including GPS, WiFi and cellular radios capable
of positioning, accelerometers, magnetic compasses and gyroscopes,
light and proximity sensors, and cameras. These
sensors have made smartphones an attractive platform for
collaborative sensing (aka crowdsourcing) applications where
phones cooperatively collect sensor data to perform various
tasks. Researchers and mobile application developers have
developed a wide variety of such applications. Examples of
such systems include BikeTastic [4] and BikeNet [1] which
allow bicyclists to collaboratively map and visualize biking
trails, SoundSense [3] for collecting and analyzing microphone
data, iCartel [2] which crowdsources driving tracks
from users to monitor road traffic in real time, and Transitgenie
[5], which cooperatively tracks buses and trains.
What do all these applications have in common? Today,
anyone who wants to develop a mobile phone crowdsourcing
application needs to:
1. Write and debug low-level application software for one
or more phone platforms (iPhone OS, Android, Symbian,
etc.).
2. Publish the application on an official distribution channel
like the iPhone App Store or the Android Market,
and incentivize enough volunteers with phones to use
the application, a challenging task.
3. Deal with issues of privacy, energy and intermittent network
connectivity. For example, a traffic monitoring
app that always collects GPS location samples once a
second would drain the battery, and users would not
want to install it.
4. Filter out irrelevant portions of sensor traces from
phones that do not apply to the problem at hand. For
example, Transitgenie, which cooperatively tracks public
transit, filters out location traces when the user is
stationary, walking or indoors.
What if we had a platform with a large pre-existing installed
base of phone users that enabled researchers and developers
to instantly develop and deploy their own applications
without having to worry about any of the above concerns?
To realise this vision, we are building Code in the
Air, a platform for developing mobile crowdsourcing applications
that deals with all the low-level details. |
| first_indexed | 2024-09-23T13:19:09Z |
| format | Article |
| id | mit-1721.1/62221 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:19:09Z |
| publishDate | 2011 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/622212022-09-28T13:22:02Z Code In The Air: Simplifying Sensing on Smartphones Kaler, Timothy Lynch, John Patrick Peng, Timothy Sivalingam, Lenin Ravindranth Thiagarajan, Arvind Balakrishnan, Hari Madden, Samuel R. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mathematics Madden, Samuel R. Kaler, Timothy Lynch, John Patrick Peng, Timothy Sivalingam, Lenin Ravindranth Thiagarajan, Arvind Balakrishnan, Hari Madden, Samuel R. Modern smartphones are equipped with a wide variety of sensors including GPS, WiFi and cellular radios capable of positioning, accelerometers, magnetic compasses and gyroscopes, light and proximity sensors, and cameras. These sensors have made smartphones an attractive platform for collaborative sensing (aka crowdsourcing) applications where phones cooperatively collect sensor data to perform various tasks. Researchers and mobile application developers have developed a wide variety of such applications. Examples of such systems include BikeTastic [4] and BikeNet [1] which allow bicyclists to collaboratively map and visualize biking trails, SoundSense [3] for collecting and analyzing microphone data, iCartel [2] which crowdsources driving tracks from users to monitor road traffic in real time, and Transitgenie [5], which cooperatively tracks buses and trains. What do all these applications have in common? Today, anyone who wants to develop a mobile phone crowdsourcing application needs to: 1. Write and debug low-level application software for one or more phone platforms (iPhone OS, Android, Symbian, etc.). 2. Publish the application on an official distribution channel like the iPhone App Store or the Android Market, and incentivize enough volunteers with phones to use the application, a challenging task. 3. Deal with issues of privacy, energy and intermittent network connectivity. For example, a traffic monitoring app that always collects GPS location samples once a second would drain the battery, and users would not want to install it. 4. Filter out irrelevant portions of sensor traces from phones that do not apply to the problem at hand. For example, Transitgenie, which cooperatively tracks public transit, filters out location traces when the user is stationary, walking or indoors. What if we had a platform with a large pre-existing installed base of phone users that enabled researchers and developers to instantly develop and deploy their own applications without having to worry about any of the above concerns? To realise this vision, we are building Code in the Air, a platform for developing mobile crowdsourcing applications that deals with all the low-level details. 2011-04-15T22:30:33Z 2011-04-15T22:30:33Z 2010-01 Article http://purl.org/eprint/type/ConferencePaper 978-1-4503-0344-6 http://hdl.handle.net/1721.1/62221 Kaler, Tim et al. “Code in the Air.” Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems - SenSys ’10. Zurich, Switzerland, 2010. 407. ©2010 ACM https://orcid.org/0000-0002-7470-3265 https://orcid.org/0000-0002-3831-8255 https://orcid.org/0000-0002-1455-9652 en_US http://dx.doi.org/10.1145/1869983.1870046 ACM International Conference on Embedded Networked Sensor Systems (SENSYS) Proceedings Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computing Machinery MIT web domain |
| spellingShingle | Kaler, Timothy Lynch, John Patrick Peng, Timothy Sivalingam, Lenin Ravindranth Thiagarajan, Arvind Balakrishnan, Hari Madden, Samuel R. Code In The Air: Simplifying Sensing on Smartphones |
| title | Code In The Air: Simplifying Sensing on Smartphones |
| title_full | Code In The Air: Simplifying Sensing on Smartphones |
| title_fullStr | Code In The Air: Simplifying Sensing on Smartphones |
| title_full_unstemmed | Code In The Air: Simplifying Sensing on Smartphones |
| title_short | Code In The Air: Simplifying Sensing on Smartphones |
| title_sort | code in the air simplifying sensing on smartphones |
| url | http://hdl.handle.net/1721.1/62221 https://orcid.org/0000-0002-7470-3265 https://orcid.org/0000-0002-3831-8255 https://orcid.org/0000-0002-1455-9652 |
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