Cyber Physical Systems Project

This page describes the research performed under NSF grant #1035565, "CPS: Medium: Collaborative Research: Enabling and Advancing Human and Probabilistic Context Awareness for Smart Facilities and Elder Care".


Principal Investigators:


The objective of this research is to enable cyber-physical systems (CPS) to be context-aware of people in the environment and to use data from real-world probabilistic sensors. The approach is (1) to use radio tomography (RT) and RFID to provide awareness (location and potential identification) of every person in a building or area, and (2) to develop new tools to enable context-aware computing systems to use probabilistic data, thus allowing new applications to exploit sometimes unreliable estimates of the environment.

This project impacts broadly the area of cyber-physical systems that reason about human presence and rely on noisy and potentially ambiguous (practical) sensors. The research has additional dramatic impact in: (1) smart facilities which automatically enforce safety, privacy, and security procedures, increasing the ability to respond in emergency situations and prevent accidents and sabotage; (2) elder care, to monitor for physical or social decline so that effective intervention can be implemented, extending the period elders can live in their own home, without pervasive video surveillance.

Research Activities

This project has made several accomplishments to this point in the following research domains:

  • Simultaneous localization of both radio tags worn by people and of people not wearing tags.
  • Noise reduction from intrinsic RSS noise, e.g., moving leaves or moving sensors.
  • Exploration of possible security attacks in DFL networks.
  • Development of improved imaging and tracking methods for radio tomography.
  • Tracking of multiple people.
  • Use of multiple channels in order to improve DFL performance.
  • Detection, rate estimation of the breathing of stationary people via measurements of RSS in a wireless network. Localization of the source of the breathing signal.
  • Development of improved multi-channel RTI algorithms.
  • Enabling long-term deployments of RTI systems without re-calibration.
  • Establishing bounds on the DFL coverage problem (placing sensors for coverage of an area).
  • Testing of our methods internationally against other research prototypes.
  • Development of line-crossing based tracking algorithms.
  • Exploration of the radio window attack and countermeasures to protect the location privacy of people in areas covered by a wireless network.
  • Development of fall detection algorithms.

Educational Goals:

  • Development and teaching of a course in RF-based device-free localization (DFL) methods.
  • Training of undergraduate and graduate students in DFL research.
  • Dissemination of research results to other researchers
  • Use of technology demonstrations in outreach and retention events.

MEB Deployment

We currently are deploying a 200 node sensor network in the UofU Merrill Engineering Building (MEB) for the purposes of device-free localization research. This site will be updated with current status and research progress from this large deployment.

The network is deployed in space used for other purposes by faculty, staff, graduate and undergraduate students. We collect data when the area is empty of other people, because other people at unknown locations would complicate our efforts to run controlled experiments. Due to its co-location with other people's offices, we have obtained IRB approval for this deployment and data collection, and we believe the system is both safe and preserves the privacy of the people whose offices it resides in.