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Research Profile. Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University. Background. Education Washington University in St. Louis, MO Master of Science in Computer Science , 2003 - PowerPoint PPT Presentation
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Research Profile
Guoliang XingAssistant Professor
Department of Computer Science and Engineering Michigan State University
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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
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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.)
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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
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Selected Projects on Sensor Networks
• Integrated Coverage and Connectivity Configuration
• Holistic power configuration
• Rendezvous-based data collection
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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
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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
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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
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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 States Transmission
Ptx
Reception
Prx
Idle
Pidle
Sleeping
Psleep
Power consumption (mw)
21.2~106.8 32 32 0.001
Power consumption of CC1000 Radio in different states
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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
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Power Control vs. Sleep Scheduling
Transmission power dominates: use low transmission power
Idle power dominates:use high transmission power since more nodes can sleep
)( caP
)( cbaP 3Pidle
2Pidle+Psleep
Pow
er C
onsu
mpt
ion
widthband
ratedata r0 1
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Problem Formulation
• Given comm. demands I={( si , ti , ri )} and G(V,E), find a sub-graph G´(V´, E´) minimizing
• Sleep scheduling
Irts
iii
iii
tsPr),,(
),(idlePV |'| idlePV |'| Irts
iii
iii
tsPr),,(
),(
sum of edge cost from si to ti in G´
independent of data rate!
• Sleep scheduling • Power control
• Sleep scheduling • Power control• Finding min-power backbone is NP-Hard
node cost
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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
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Data Transport using Mobiles
Base Station
500K bytes
100K bytes 100K bytes
150K bytes5 m
ins
10 mins
5 mins
• Analogy– What's best way to send 100
G data from HK to DC?
Networked Infomechanical Systems (NIMS) @ UCLA
Robomote @ USC
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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
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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), 2008IEEE Real-Time Systems Symposium (RTSS), 2007
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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
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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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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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