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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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