Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


2008 Research Summary

N-SMARTS: Networked Suite of Mobile Atmospheric Real-Time Sensors (NSMARTS)

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Richard Edward Honicky, Eric Brewer, Richard M. White, Albert Pisano, Alexander William Elium and Justin Black

While industry analysts predict that cell phones will become the "next PC," we believe that the cell phone has the power to become something much more than a scaled down, connected IO and processing device. In addition to these standard PC traits, a cell phone is situated in an environment, mobile, and typically co-located with a user. These traits make the cell phone ideally suited to track and understand the impact that the environment has on individuals, communities, cities, and on a global scale, as well as understanding how humans affect their environment.

By attaching sensors to GPS-enabled cell phones, we can gather the raw data necessary to begin to understand how, for example, urban air pollution impacts both individuals and communities. While integrating a sensor into a phone and transmitting the data that it gathers to a database is not very difficult, doing so at low cost, on a societal scale, with millions of phones of phones providing a data from hundreds of networks spread throughout the world makes the problem much more tricky.

On top of the systems challenges, understanding the raw data gathered from a network of cell phone-attached sensors presents significant challenges as well. Cell phone users are mobile, are unlikely to ever explicitly calibrate their sensors, typically put their phone in their pocket or handbag (thus obstructing the sensor from airflow), spend significant time indoors or in cars, and typically charge their phone at most once per day, often much less frequently.

Thus the N-SMARTS project focuses on:

  • Developing a platform to understand the real-world challenges of sensing on a mobile phone, and to provide other researchers, both within and outside of computer science, with a platform for their own experiments;

  • Building a system architecture that can scale to millions of phones;
  • Designing algorithms to scalably provide accurate estimates of pollution levels and other sensed data;

  • Designing algorithms to detect and account for the user's behaviors; and

  • Assembling and building a suite of useful sensors to integrate.

    R. J. Honicky, Automatic Calibration of Sensor-Phones Using Gaussian Processes, UC Berkeley EECS Department Technical Report No. UCB/EECS-2007-34, March 20, 2007.