QoS Management for Data Services in Real-Time Embedded Systems and Sensor Networks

RT-QoS Project

The RT-QoS Project is developing technology required for real-time data services with emphasis on adding value along four dimensions: real-time, security, power-awareness, and Quality of Service (QoS). Real-time data services are essential when transactions have deadlines and where data is valid only for a period of time, e.g., it could be data from sensors or derived from sensors. Applications include Internet services, defense applications, and embedded systems such as sensor networks.

Many applications require the ability to provide real-time data services and connect to sensors and actuators. RT-QoS is investigating many research issues related to supporting this requirement. We are studying how to support various types of Quality-of-Service (QoS) for both transaction timeliness and data freshness in real-time data services. We are also developing feedback controllers to enforce different guarantees for different classes of service. This is sometimes called a differentiated service model.

The project has also developed algorithms for embedded systems using flash memory as storage devices and techniques to use time signatures for QoS management in secure applications.

Our project is also investigating the QoS issues for data services in large scale sensornets. Here many of the same issues investigated on traditional real-time databases appear, but there are additional severe constraints due to the small size of the devices, the power requirements of the devices, and the high failure rates in communications and the devices themselves. We currently have results on a data caching algorithm for sensornets and are implementing our solutions on a wireless network of motes.

In this project, we have been collaborating with Carnegie Mellon University (SEI), State University on New York (SUNY Binghamton), Linkoping University and University of Skovde in Sweden, City University of Hong Kong, and Sogang University in Korea.


The material on this web page is based upon work supported by the National Science Foundation under Grants IIS-0208758, CCR-0329609, CNS-0614886, and CNS-0614773. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Svience Foundation.

Principal Investigators

Sang H. Son
Jack Stankovic

Current and Recent Graduate Students

Woochul Kang
Vibha Prasad
Liang Hong
KD Kang
Joyce Li
Yuan Wei
Binjia Jiao
Damon Jo
Ying Lin

Current and Recent Visiting Scholars and Postdocs

Hyoung-Jun Kim, ETRI, Korea
SuHee Kim, Hoseo University, Korea
Seog Park, Sogang University, Korea
Mehdi Amirijoo, Linkoping University, Sweden
Won Jay Song, Information and Communication University, Korea
Sooyeon Kim, Seoul National University, Korea
Alex Buchmann, Darmstadt University, Germany
Victor Lee, City University of Hong Kong, China
Jin-Chun Kim, Kyungsung University, Korea
LihChyun Shu, National Cheng Kung University, Taiwan
Jorgen Hansson, Linkoping University, Sweden

Selected Publications