Wireless Sensor Networks (WSN)

Cyber Physical Systems (CPS)

Internet of Things (IOT)

Smart Cities

Smart and Connected Health and Wellness

 

John Stankovic, BP America Professor, Director of the Link Lab

 

 

 

Current Post Docs

None.

Current Graduate Students

Meiyi Ma
Winston Chen
Sirat Samyoun
Lahiru Nuwan
Ashley Gao
Arif Rahman

Research Partners Outside UVA


Karen Rose, Ohio State
Kristina Gordon, Univ. of Tennessee
Shan Lin, StonyBrook
John Lach, GWU
Donna Spruijt-Metz, USC
Kayla de la Haye, USC
Hengchang Liu, Univ. of Electronic Science and Technology, China
Niki Trigoni, Oxford
Gaia Maselli, Univ. of Rome
Desheng Zhang, Rutgers

It is now possible to develop large numbers of small smart components that combine computing power, wireless communication capabilities, and specialized sensors and actuators. These components or nodes may be deployed in thousands to achieve a common mission or in smaller numbers be deployed as wearables. They may be used to monitor poorly accessible or dangerous environments such as the ocean floor, neighborhoods of volcanic activities, hostile territories (e.g., behind enemy lines), disaster areas, and nuclearly active fields. They may also be deployed to accomplish interactive tasks such as finding and detonating enemy mines, looking for survivors of natural disasters, or containing and isolating oil spills to protect a nearby coastline. These wireless sensor devices are also useful for environmental monitoring, medical applications and smart homes, buildings, or cities. The new technology creates a different set of challenges arising from the fact that:

Nodes are embedded into a geographic landscape and interact tightly with a complicated and noisy physical environment in real-time. They may also be monitoring human physiology and behaviors.
Nodes are smaller and less reliable than traditional devices.
Nodes operate under severe constraints on power, computation, bandwidth and memory.
Nodes may be mobile.
Nodes may be subject to security attacks.
Luster and SeeMote.

Our more recent work is in smart and connected health and smart cities areas. In the smart health arena we built a testbed to emulate WSNs in assisted living facilities. This system is called AlarmNet and extended in a systems called Empath. We worked with the UVA medical school and Harvard. See also Wireless Networking for Assisted Living and Other Medical Applications. See also Hardware We Developed for Medical Applications.

In smart health we also have developed many solutions based on wearables. Various projects included: (i) a complete redesign of body sensor networks from silicon to the user (joint with the University of Michigan), (ii) a BSN for fall detetcion based on a context free grammar and area context sensors, (iii) a home health care system for early detection of depression, (iv) the Musical Heart smartphone system for controlling heart rate with music, (v) a general sound engine and app development environment (API) for smartphones, (vi) robust activity recognition that accounts for overlapped activities and missing sensor readings, (vii) runtime assurance techniques, (viii) use of multiple classifiers to detect faults and minimize the number of repair dispatches and nodes repaired, (ix) a system capable of inferring water events in a home, (x) various medical applications using the Kinect system, (xi) classifiers for detetcing mood from voice at a distance, (xii) a medical reminder system using a smart watch, (xiii) detecting eating events using a smart watch in the wild, (xiv) a quality of handwashing detection system based on an Apple Watch, and (xv) a home system to monitor caregiver-alzheimer patient interactions and provide personalized recommendations to reduce the stress of the caregiver. Papers below cover many aspects of these projects. We have also significant results for smart cities. We developed techniques to detect conflicts across smart city services and resolve them We also are integrating formal methods with machine learning to monitor city states and determine if they meet requirements as specified in STL. We have enhanced STL for smart cities with three extensions: SaSTL, STLnet and STL-U. Papers on these are listed below.

All this material is based, in part, upon work supported by the National Science Foundation under Grants No. CNS-0614870, CNS-0614773, CNS-0626616, and CNS-0626632.

Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).
 
 

Best Paper Awards and Runner Ups



 
 

Other Awards

  • Entrepreneurial Vision Award, 4th Annual S.E.E.D. Spirit of Entrepreneurship and Enterprise Development - Venture Forum, Santa Barbara, Calif., Team Leader, Johns Hopkins, Team Members, Harvard and UVA.
  • Award, 2nd Best Emerging Company Investment Opportunity, 4th Annual S.E.E.D. Spirit of Entrepreneurship and Enterprise Development - Venture Forum, Santa Barbara, Calif., Team Leader, Johns Hopkins, Team Members, Harvard and UVA.
  • Best Demo Award, ACM Sensys, Nov. 2020.


 
 

Publicity (Partial List)


Selected Publications