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 Sleeping node Communicating nodes Active nodes Sensing range 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 Sleeping node Communicating nodes Active nodes Sensing range 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 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

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 Radio StatesTransmission P tx Reception P rx Idle P idle Sleeping P sleep Power consumption (mw) 21.2~ Power consumption of CC1000 Radio in different states

10 Example of Min-power Backbone 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 a c b

11 Power Control vs. Sleep Scheduling Transmission power dominates: use low transmission power Idle power dominates: use high transmission power since more nodes can sleep 3P idle 2P idle +P sleep Power Consumption r0r0 1

12 Problem Formulation Given comm. demands I={( s i, t i, r i )} and G(V,E), find a sub-graph G´(V´, E´) minimizing Sleep scheduling    Irts ii i iii ts P r ),,( ), ( idle PV|'| PV|'|   Irts ii i iii ts P r ),,( ), ( sum of edge cost from s i to t i in G´ independent of data rate! Sleep scheduling Power control Sleep scheduling Power control Finding min-power backbone is NP-Hard node cost

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(P rx +P tx -P idle )/P idle (≈ 5 on Mica2 motes) ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2005

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

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

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20 mobile rendezvous point Problem Formulation source node 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 base station

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