Chenda Liao and Prabir Barooh Time-Synchronization in Mobile Sensor Networks from Difference Measurements Distributed Control System Lab Dept. of Mechanical and Aerospace Eng. University of Florida, Gainesville, FL 49th IEEE Conference on Decision and Control Dec, 15 th , 2010 Atlanta, Georgia, USA
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Estimation in Networks From Relative Measurementsplaza.ufl.edu/cdliao/document/Presentation/cdliao_CDC2010.pdf · Convergence Analysis Theorem: mean square convergent • is entry-wise
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Chenda Liao and Prabir Barooh
Time-Synchronization in Mobile Sensor
Networks from Difference Measurements
Distributed Control System Lab
Dept. of Mechanical and Aerospace Eng.
University of Florida, Gainesville, FL
49th IEEE Conference on Decision and Control
Dec, 15th, 2010
Atlanta, Georgia, USA
Sensor Networks
• Environment/Structure Monitoring
• Event/Fault Detection
• Home/office Automation
• Healthcare
• Industrial Automation
• Military Application
Limited power
=global/reference time =local time
=skew =offset
Time Synchronization in Sensor Network
Time synchronization problem is equivalent to determining and ,
Motivation:
Meaning of Sync. :
Global time
u
ref
v
Local time:
Literature review
Static sensor network
• Elson et al., Fine-grained network time synchronization using reference broadcasts (RBS), 2002
• Ganeriwal et al., Timing-Sync Protocol for Sensor Network (TPSN), 2003
• Barooah et al., Distributed optimal estimation from relative measurements for localization and time synchronization, 2006
• Suyong Yoon et al., Tiny-Sync: Tight Time Synchronization for Wireless Sensor Networks, 2007
Mobile sensor network
• Miklós et al., Flooding Time Synchronization Protocol (FTSP), 2004
• Su et.al., Time-Diffusion Synchronization Protocol for Wireless Sensor Networks (TDP), 2005
Noisy measurement of the
relative skews and offsets
for pairs of nodes
Time Sync on Mobile Sensor Network
Goal:
To estimate the skews and offsets of clocks of all the nodes with respect to an reference clock in mobile sensor network.
Algorithm:
• Model the time variation of the network (graph) as a Markov chain.
• Prove the mean square convergence of the estimation error (Markov jump linear system).
• Corroborate the predictions using Monte Carlo simulations.