Code In The Air: Simplifying Sensing on Smartphones
Name
Madden_Code in.pdf
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481.6 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Kaler, Timothy
Lynch, John Patrick
Peng, Timothy
Sivalingam, Lenin Ravindranth
Thiagarajan, Arvind
Balakrishnan, Hari
Madden, Samuel R.
Date Issued
January 2010
Journal
ACM International Conference on Embedded Networked Sensor Systems (SENSYS) Proceedings
Publisher
Association for Computing Machinery
Citation
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
Version
Author's final manuscript
Abstract
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.
MIT Department
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
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/1869983.1870046
