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 Sleeping node Communicating nodes Active nodes Sensing range 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 R c / R s  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 R c / R s < 2 –CCP + connectivity mountainous protocols ACM Transactions on Sensor Networks, Vol. 1 (1), First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003

8 Data Transport using Mobiles Base Station 500K bytes 100K bytes 150K bytes 5 mins 10 mins 5 mins Networked Infomechanical Systems UCLA USC

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 s1s1 r1r1 s2s2 r2r2 +

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

13 Packet Reception Ratio vs. SINR Classical model doesn't capture the gray region office, no interfererparking lot, no interfereroffice, 1 interferer  Noise +  Interference Received Signal Strength (RSS) 0~3 dB is "gray region" Packet Reception Ratio (%)

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

15 Performance Assurance in Crowded Spectrum Performance-sensitive wireless applications –Patient monitoring with body sensor networks –Home networking for Bluetooth headsets, 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

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19 HTPA in a Nutshell Body sensor networks for patient monitoring –Bluetooth sensors and /Bluetooth base stations Spectrum profiler –Bluetooth frequency hopping range, 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

20 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

21 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

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

23 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