Real-Time Embedded Systems Laboratory

Systematic Approaches for Real-Time Stream Data Services

Data-intensive real-time applications, such as transportation management, military surveillance, and network monitoring, need to handle massive amounts of stream data in a timely fashion. It is challenging to support real-time stream data services (RTSDS) due to stringent timing constraints, potentially unbounded continuous stream data, bursty stream data arrivals, and workload variations due to data value changes. This project will develop cost-effective methods and a runtime system for RTSDS. The project will systematically investigate methods and tools to support real-time continuous queries for RTSDS even in the presence of dynamic workloads. Specifically, the project will study a) real-time continuous query modeling, b) new performance metric design c) adaptive query scheduling design, d) tardiness control and load shedding, for both single node and clustered RTSDS. The project will also have prototype implementation and testbed evaluations. The results and findings of this project will advance and seamlessly integrate real-time computing and stream data management.




Real-Time Data Management

A number of e-commerce and real-time embedded applications, including traffic control, agile manufacturing, and defense applications, have a large volume of data. Without database support, real-time transactions should be manually handled increasing the difficulty of the application development. In these applications, it is essential for a database to process transactions within their deadlines using fresh temporal data reflecting the current real world status, e.g., the current traffic or battle field status. However, it is challenging to support the desired transaction timeliness and data freshness due to data/resource contention and dynamic workloads. To shed light on this problem, we are developing a novel theoretic framework. Furthermore, we are developing a real-time database system and benchmarks atop an open source embedded database Berkeley DB, which can be configured for a specific application and directly linked into the application, to experimentally validate the proposed QoS management techniques. We are also looking into e-commerce applications such as online stock trading, in which transactions should be finished within the desired delay bounds using the data representing the current market status.

Wireless Sensor Networks

Wireless Sensor Networks: Battery-powered small wireless sensors can be disseminated to the physical world, e.g., for habitat monitoring, rescue operations, home healthcare, and battle field monitoring. In this exciting new research area, we are currently investigating energy-efficient routing, medium access control, data aggregation, real-time scheduling, and security protocols to deliver important sensor data in a timely, energy-efficient, secure manner.

Selected Publications

These are just random samples of our recent papers. For all the papers from our group, please click here.

Real-Time Data Management

• Y. Zhou, K. D. Kang, "A Federated Approach to Increasing the Timely Throughput of Real-Time Data Services," In Proceedings of the 18th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '12), April 16 - 19, 2012, Beijing, China.
• K. D. Kang, Y. Zhou, J. Oh, "Estimating and Enhancing Real-Time Data Service Delays: Control Theoretic Approaches", IEEE Transactions on Knowledge and Data Engineering, Volume 23, Issue 4, pages 554 - 567, April 2011.
• Yan Zhou, Kyoung-Don Kang, "Deadline Assignment and Tardiness Control for Real-Time Data Services", In Proceedings of the 22nd Euromicro Conference on Real-Time Systems (ECRTS '10), Brussels, Belgium, July 6-9, 2010.
• Yan Zhou, Kyoung-Don Kang, "Integrating Proactive and Reactive Approaches for Robust Real-Time Data Services", In Proceedings of the 30th IEEE Real-Time Systems Symposium (RTSS '09), Dec. 1 - 4, 2009, Washington D.C., USA.
• K. D. Kang, J. Oh, Y. Zhou, "Backlog Estimation and Management for Real-Time Data Services", 20th Euromicro Conference on Real-Time Systems (ECRTS '08), July 2-4, Prague, Czech Republic.

Wireless Sensor Networks

• K. Kapitanova, S. H. Son, K. D. Kang, "Using Fuzzy Logic for Robust Event Detection in Wireless Sensor Networks", Ad Hoc Networks, Elsevier, To Appear.
• Lei Rao, Xue Liu, Kyoung-Don Kang, Wenyu Liu, Liang Liu, Ying Chen, "Optimal Joint Multi-path Routing and Sampling Rates Assignment for Real-Time Wireless Sensor Networks", IEEE International Conference on Communications (ICC AHSM '11), June 5 - 9, Kyoto, Japan.
• C. Basaran, K.D. Kang, M. H. Suzer, "Hop-by-Hop Congestion Control and Load Balancing in Wireless Sensor Networks", In Proceedings of the 35th IEEE Conference on Local Computer Networks (LCN 2010), Denver, Colorado, USA, Oct. 11 - 14.
• K. Kapitanova, S. H. Son, and K. D. Kang, "Event Detection in Wireless Sensor Networks - Can Fuzzy Values Be Accurate?", Second International Conference on Ad Hoc Networks (AdHocNets), August 18-20, 2010, Victoria, British Columbia, Canada.
• K. Liu, N. Abu-Ghazaleh, and K. D. Kang, "Exploiting slack time for just-in-time scheduling in wireless sensor networks", Real-Time Systems Journal, Volume 45, Numbers 1-2, June, 2010.