CitiSense and MetaSense

MetaSense — Calibration of Personal Air Quality Sensors in the Field - Coping with Noise and Extending Capabilities

All cyber-physical systems (CPS) depend on properly calibrated sensors to sense the surrounding environment. Unfortunately, the current state of the art is that calibration is often a manual and expensive operation; moreover, many types of sensors, especially economical ones, must be recalibrated often. This is typically costly, performed in a lab environment, requiring that sensors be removed from service. In our experience with CitiSense we experienced that the cost of scaling un the amount of sensors in the field is prohibitive without solving the issue of recalibrate sensors automatically. This is the motivation for MetaSense.
The basic idea is that if two sensors are co-located, then they should report similar values; if they do not, the least-recently-calibrated sensor is suspect. Building on this idea, this project will provide an autonomous system and a set of algorithms that will automate the detection of calibration issues and preform recalibration of sensors in the field, removing the need to take sensors offline and send them to a laboratory for calibration.
MetaSense will change the way sensors are engineered and deployed, increasing the scale of sensor network deployment. This in turn will increase the availability of environmental data for research, medical, personal, and business use. MetaSense researchers will leverage this new data to provide early warning for factors that could negatively affect health.

MetaSense Repositories

MetaSense System Overview

       This project is supported by NSF Cyber-Physical Systems Grant CNS-1446912.

CitiSense  Adaptive Services for Community-Driven Behavioral and Environmental Monitoring to Induce Change

The environmental impacts of our daily activities are largely invisible to us - Carbon dioxide from our cars, fertilizers from our lawns, environmental noise and human stress from driving - yet the impact on our long-term health is inevitable. By pervasively monitoring ourselves and our immediate environs, aggregating the data for analysis, and reflecting the results back to us quickly, we can avoid toxic locales, appreciate the consequences of our individual behaviors, and together seek a mandate for change. Today, the infrastructure of our regulatory institutions is inadequate for the cause: sensors are few, often far from where we live, and the results are slow to come to us. What about the air quality on your jogging route or commute? Can you be told when it matters most?

With the proliferation of personal mobile computing via mobile phones and the advent of cheap, small sensors, we propose that a new kind of "citizen infrastructure", CitiSense, can be made pervasive at low cost and high value. Though challenges abound in mobile power management, data security, privacy, inference with commodity sensors, and "polite" user notification, the overriding challenge lies in the integration of the parts into a seamless yet modular whole that can make the most of each piece of the solution at every point in time through dynamic adaptation. Using existing integration methodologies would cause components to hide essential information from each other, limiting optimization possibilities. Emphasizing seamlessness and information sharing, on the other hand, would result in a monolithic solution that could not be modularly configured, adapted, maintained, or upgraded.

       This project supported by NSF Cyber-Physical Systems Grant CNS-0932403, with additional support from the NIH and a gift from Qualcomm.