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

The personal data acquisition platform

The automotive data acquisition platform

The integrated data acquisition platform

Data from Accra, Ghana

This is a new project, so much of the work discussed here is still under development, and not available to the public. We will make our work publically available as soon as possible!

Platform

• The Data Acquisition Platform
  • A personal sensor platform: Designed to closely simulate the data that will be acquired by the integrated platform, the personal platform is a simple set of sensors which clips on to a user's belt. This platform consists of a GPS, a Carbon Monoxide Sensor/Data logger, and a NO2, SO2 and/or O3 sensor.
  • An automotive based platform: Designed to gather lots of data quickly, this variant contains the same sensors as the personal platform, but is enclosed in a tube which allows airflow through the ends. This enclosure allows the sensors to be easily mounted on a taxi, bus, or other car which moves about an urban area quickly and constantly.
• The Integrated Platform (v0.1)
  • The sensor board: a small circuit board containing an environmental sensor (in this case, a Carbon Monoxide sensor), an accelerometer, a temperature sensor, and a microcontroller (for communicating with the phone)
  • A CDMA mobile phone with AGPS and an accessible serial port: in this case, the LG VX9800
  • An enclosure: the enclosure (under development) will clip in to the battery well of the phone, and will contain both the battery and the sensor board.

Data Acquisition and Analysis

• Data Acquisition: the environment is sensed, the data is preprocessed on the phone, and then sent via SMS to a central database
• Data Analysis: the data in the database is interpolated, the miscalibration of each sensor is inferred
• Data Visualization: the data is visualized as a contour plot of sensed value vs. geographic location. This will eventually be overlaid with a map of the location.
• Context inference: the data is correlated with the users' contexts and activities: indoors, outdoors, walking, running, biking, driving, etc. Also (and more importantly), the phones' status': in the pocket, in the hand, etc.

Advanced Sensing

• We are developing a miniature, MEMS-based particulate mass sensor (focusing on pm2.5 particles) which will be small enough to embed in a phone. Eventually this sensor will be able to discriminate between different types of particles using interferometry.

Participatory Sensing

• In collaboration with Intel Research, particularly Intel Research Berkeley and Intel Research Seattle, we are investigating a more generic platform which will allow end users to create simple mashups using plug in sensors.
• We will focus on user privacy and end-user programming