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Research Profile

Research Profile

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Research Profile

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

  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. 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. Methodology • Explore fundamental network design issues • Address multi-dimensional performance requirements by a holistic approach • High-throughput and power-efficiency • Sensing coverage and comm. performance • Exploit realistic system & platform models • Combine theory and system design

  5. Outline • Selected projects on sensor networks • Integrated Coverage and Connectivity Configuration • Rendezvous-based data collection • Model-driven concurrent medium access control • Pending proposal • Holistic transparent performance assurance • Proposals in preparation

  6. Coverage + Connectivity • Select a set of nodes to achieve: • K-coverage: every point is monitored by at least K active sensors • N-connectivity: network is still connected if N-1 active nodes fail Active nodes Sensing range Sleeping node Communicating nodes A network with 1-coverage and 1-connectivity

  7. Connectivity vs. Coverage: Analytical Results • Network connectivity does not guarantee coverage • Connectivity only concerns with node locations • Coverage concerns with all locations in a region • If Rc/Rs 2 • K-coverage  K-connectivity • Implication: given requirements of K-coverage and N-connectivity, only needs to satisfy max(K, N)-coverage • Solution: Coverage Configuration Protocol (CCP) • If Rc/Rs< 2 • CCP + connectivity mountainous protocols ACM Transactions on Sensor Networks, Vol. 1 (1), 2005. First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003

  8. Data Transport using Mobiles Base Station 5 mins 150K bytes Robomote @ USC 10 mins 500K bytes 5 mins 100K bytes 100K bytes Networked Infomechanical Systems (NIMS) @ UCLA

  9. Rendezvous-based Data Transport • Some nodes serve as “rendezvous points” (RPs) • Other nodes send data to the closest RP • Mobiles visit RPs and transport data to base station • Advantages • Combine In-network caching and controlled mobility • Mobiles can collect a large volume of data at a time • Minimize disruptions due to mobility • Achieve desirable balance between latency and network power consumption • Online algorithms for fixed and free mobile trails ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2008 IEEE Real-Time Systems Symposium (RTSS), 2007

  10. Outline • Selected projects on sensor networks • Integrated Coverage and Connectivity Configuration • Rendezvous-based data collection • Model-driven concurrent medium access control • Pending proposal • Holistic transparent performance assurance • Proposals in preparation

  11. Improve Throughput by Concurrency s1 s2 r1 r2 +

  12. Received Signal Strength • 18 Tmotes with Chipcon 2420 radio • Near-linear RSSdBm vs. transmission power level • Non-linear RSSdBm vs. log(dist), different from the classical model! Received Signal Strength (dBm) Received Signal Strength (dBm) Transmission Power Level Transmission Power Level

  13. Packet Reception Ratio vs. SINR • Classical model doesn't capture the gray region 0~3 dB is "gray region" Packet Reception Ratio (%) parking lot, no interferer office, no interferer office, 1 interferer Received Signal Strength (RSS) > b Noise +å Interference

  14. C-MAC Components Power Control Model Currency Check Concurrent Transmission Engine Handshaking Online Model Estimation Interference Model Throughput Prediction Throughput Prediction • Implemented in TinyOS 1.x, evaluated on a 18-mote test-bed • Performance gain over TinyOS default MAC is >2X To be presented at IEEE Infocom 2009

  15. Performance Assurance in Crowded Spectrum • Performance-sensitive wireless applications • Patient monitoring with body sensor networks • Home networking for Bluetooth headsets, 802.11 PDAs, and ZigBee remote controls. • Challenges • Stringent requirements on delay, throughput… • Many COTS devices use 2.4 GHz spectrum • Significant performance variation due to noise, inter-, and intra-platform interference

  16. State of the Art • Point solutions at different layers • PHY: cognitive radio, frequency hopping • MAC: CSMA, TDMA, channel assignment… • QoS control at upper layers • Issues • System-level performance is not addressed • Tightly coupled with radio platforms and MACs

  17. Holistic Transparent Performance Assurance (HSPA) • Integrate local interference mitigation solutions coherently to ensure system performance • Spectrum profiler • Models the interferences of various sources (external, intra- and inter-platform) • Virtual MAC • Unified abstractions that separate HPTA from native MACs, transparently monitor, and schedule resources • System and stream performance assurance • Holistic performance tradeoff and control • “control knobs” for network designers and end users

  18. HTPA in a Nutshell • Body sensor networks for patient monitoring • Bluetooth sensors and 802.11/Bluetooth base stations • Spectrum profiler • Bluetooth frequency hopping range, 802.11 channels, power, noise… • System/stream performance assurance • Assure per-stream delay and total system data rate • Choose frequency hopping range of BT and the transmit power/channel of 802.11

  19. Research Team • Gang Zhou, Computer Science, College of College of William and Mary • Guoliang Xing, Computer Science and Engineering, Michigan State University • Expertise • Measurement-based radio Interference characterization • Multi-channel MAC design and implementation • Power management architecture and protocols • Reliable and real-time communication • Quality-of-service in sensor network applications and systems

  20. 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

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

  22. 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