Research Profile

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 • 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. Selected Projects on Sensor Networks • Integrated Coverage and Connectivity Configuration • Holistic power configuration • Rendezvous-based data collection

6. Coverage + Connectivity • Select a subset of sensors 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. 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

8. 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

9. Understanding Radio Power Cost • Sleeping consumes much less power than idle listening • Motivate sleep scheduling[Polastre et al. 04, Ye et al. 04] • Transmission consumes most power • Motivate transmission power control[Singh et al. 98,Li et al. 01,Li and Hou 03] • None of existing schemes minimizes the total energy consumption in all radio states Power consumption of CC1000 Radio in different states

10. a sends to c at normalized rate of r = Data Rate / Bandwidth Nodes on backbone remain active Backbone 1: a→b→c Backbone 2: a→c, b sleeps Example of Min-power Backbone c b a

11. Power Control vs. Sleep Scheduling Transmission power dominates: use low transmission power Power Consumption 3Pidle 2Pidle+Psleep 1 r0 Idle power dominates: use high transmission power since more nodes can sleep

12. å å | V ' | P r r ( ( s s t t ) ) P P , , idle i i i i i i Î Î ( ( , , , , ) ) s s t t r r I I i i i i i i | V ' | P idle Problem Formulation • Given comm. demands I={( si , ti , ri )} and G(V,E), find a sub-graph G´(V´, E´) minimizing + sum of edge cost from si to ti in G´ node cost independent of data rate! • Sleep scheduling • Power control • Sleep scheduling • Sleep scheduling • Power control • Finding min-power backbone is NP-Hard

13. Two Online Algorithms • Incremental Shortest-path Tree Heuristic • Known approx. ratio is O(k) • Adapt to dynamic network workloads and different radio characteristics • Minimum Steiner Tree Heuristic • Approx. ratio is 1.5(Prx+Ptx-Pidle)/Pidle (≈ 5 on Mica2 motes) ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2005

14. Data Transport using Mobiles • Analogy • What's best way to send 100 G data from HK to DC? Base Station 5 mins 150K bytes Robomote @ USC 10 mins 500K bytes 5 mins 100K bytes 100K bytes Networked Infomechanical Systems (NIMS) @ UCLA

15. 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

16. Summary of Solutions • Fixed mobile trails • Without data aggregation, an optimal algorithm • With data aggregation, NP-Hard, a constant-ratio approx. algorithm • Free mobile trails w/o data aggregation • Without data aggregation, NP-Hard, an efficient greedy heuristic • With data aggregation, NP-Hard, a constant-ratio approx. algorithm • Mobility-assisted data transport protocol • Robust to unexpected comm./movement delays ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2008 IEEE Real-Time Systems Symposium (RTSS), 2007

17. Impact of Data Fusion on Network Performance • Data fusion in sensor networks • Combine data from multiple sources to achieve inferences • Value fusion, decision fusion, hybrid fusion… • Enable collaboration among resource-limited sensors • Fusion architecture in wireless sensor networks • Sensors close to each other participate in fusion • Fusion is confined to geographic proximity • Impact on network-wide performance • Capability of sensors is limited to local fusion groups • Complicate system behavior • Modeling, calibration, mobility etc. becomes challenging

18. Our Work on Data Fusion • Virtual fusion grids • Dynamic fusion groups for effective sensor collaboration • Sensor deployment • Controlled mobility in fusion-based target detection • System-level calibration in fusion-based sensornet • Project ideas • Focus on fundamental impact of data fusion

20. Problem Formulation base station • Constraint: • Mobiles must visit all RPs within a delay bound • Objective • Minimize energy of transmitting data from sources to RPs • Approach • Joint optimization of positions of RPs, mobile motion paths and data routes mobile rendezvous point source node