1. 1 Research Profile Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University

2. 2 Background Education –Washington University in St. Louis, MO Master of Science in Computer Science, 2003 Doctor of Science in Computer Science, 2006, Advisor: Chenyang Lu –Xi’an JiaoTong University, Xi’an, China Master of Science in Computer Science, 2001 Bachelor of Science in Electrical Engineering, 1998 Work Experience –Assistant Professor, 8/2008 –, Department of Computer Science and Engineering, Michigan State University –Assistant Professor, 8/2006 – 8/2008, Department of Computer Science, City University of Hong Kong –Summer Research Intern, May – July 2004, System Practice Laboratory, Palo Alto Research Center (PARC), Palo Alto, CA

3. 3 Research Summary Systems –Wireless interference measurements and modeling –Unified power management architecture for wireless sensor networks –Real-time middleware for networked embedded systems Algorithms, protocols, and analyses –Mobility-assisted data collection and target detection –Holistic radio power management –Data-fusion based network design Publications –6 IEEE/ACM Transactions papers since 2005 –20+ conference/workshop papers –First-tier conference papers: MobiHoc (3), RTSS (2), ICDCS (2), INFOCOM (1), SenSys (1), IPSN (3), IWQoS (2) –The paper "Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks" was ranked the 23rd most cited articles among all papers of Computer Science published in 2003 –Total 780+ citations (Google Scholar, 2009 Jan.)

4. 4 Performance Requirements of Cyber-physical Systems Real-time, high confidence, reliability, robustness.. Re-configurability –Online re-tasking, minimum maintenance Challenges –Physical: environment evolution, noises, device deficits –Cyber: online workload variation –Complex and dynamic system behavior due to tight coupling between cyber- and physical- subsystems

5. 5 Point Solutions at Different Layers

6. 6 Holistic Performance Adaptation Data calibration: each sensor learns its local sensing model that correlate sampling rates, noise, signal decays and variation…. Model calibration: calibrate the parameters of local sensing models Resource allocation: CPU scheduling, frequency control Calibration feedback control loop

7. 7 Example Applications High-confidence pervasive surveillance systems –wireless cameras + low-cost sensors (acoustic and IR) + mobile –Positive sensor detections wake up cameras; image detections calibrate sensors –Variable workloads, sensor performance variations, complex terrain, noises… Holistic performance adaptation –"improve confidence level to 95%, reduce delay to 5s" –Control sampling rates, sensor thresholds, camera wakeup frequency, camera resolution, CPU task schedules –Minimize power and convergence period Other applications –Real-time sensor networks for data center monitoring

8. 8 Research Team Guoliang Xing, Department of Computer Science and Engineering, Michigan State University –System-level calibration in sensor networks –Mobile sensor scheduling –Target detection and data fusion –Power management in sensor networks Xiaorui (Ray) Wang, Electrical Engineering and Computer Science, the University of Tennessee, Knoxville –Power, thermal, and performance control for data centers [RTSS 08 best paper award] –Distributed real-time embedded systems, adaptive middleware –Real-time wireless sensor networks –Quality of Service (QoS) control in computing systems

9. 9 Mobile Data Access in Urban Sensor Networks: Planning, Caching, and Limits Urban sensor networks –Low-cost sensors deployed in metro areas –Monitor city-wide events or facilities –Applications Distributed traffic control Parking space monitoring and management Location-aware content distribution Mobile data access –Deliver data to mobile users in the right location at the right time

10. 10 Parking space monitoring and management "Send me the locations of vacant parking lots within 2 blocks from me every 10s" 0.1 0.3 0.1 0.2

11. 11 Research Tasks Network planning –Where to deploy sensors and base stations? Data caching –Where to cache data? –How long to cache data at each location? Performance limits –How does the performance scale with respect to size of network? Spatiotemporal constraints –Spatial constraints Existing infrastructure: light poles, power sources…. Statistical distribution of positions & speeds of users –Temporal constraint Mobility statistics