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
• Xian JiaoTong University, Xian, 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

Active nodes
Sensing range
Sleeping node
Communicating nodes
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 lt 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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Data Transport using Mobiles
Base Station
5 mins
150K bytes
Robomote _at_ USC
10 mins
500K bytes
5 mins
100K bytes
100K bytes
Networked Infomechanical Systems (NIMS) _at_ UCLA
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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
• 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
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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

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Improve Throughput by Concurrency
s1
s2
r1
r2

12
Received Signal Strength
Received Signal Strength (dBm)
Received Signal Strength (dBm)
Transmission Power Level
Transmission Power Level

• 18 Tmotes with Chipcon 2420 radio
• Near-linear RSSdBm vs. transmission power level
• Non-linear RSSdBm vs. log(dist), different from
  the classical model!

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Packet Reception Ratio vs. SINR
03 dB is "gray region"
Packet Reception Ratio ()
office, no interferer
parking lot, no interferer
office, 1 interferer

• Classical model doesn't capture the gray region

Received Signal Strength (RSS)


b
gt
Noise å Interference
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C-MAC Components
Power Control Model
Currency Check

Concurrent Transmission Engine
Handshaking
Online Model Estimation
Interference Model
Throughput Prediction
Throughput Prediction

• Implemented in TinyOS 1.x, evaluated on a 18-mote
  test-bed
• Performance gain over TinyOS default MAC is gt2X

To be presented at IEEE Infocom 2009
15
Performance Assurance in Crowded Spectrum

• Performance-sensitive wireless applications
• Patient monitoring with body sensor networks
• Home networking for Bluetooth headsets, 802.11
  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

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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

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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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HTPA in a Nutshell

• Body sensor networks for patient monitoring
• Bluetooth sensors and 802.11/Bluetooth base
  stations
• Spectrum profiler
• Bluetooth frequency hopping range, 802.11
  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 802.11

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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

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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

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Parking space monitoring and management

• "Send me the locations of vacant parking lots
  within 2 blocks from me every 10s"

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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