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Time Synchronization (RBS, Elson et al.)

Time Synchronization (RBS, Elson et al.). Presenter: Peter Sibley. Traditional Synchronization Methods. Server sends messages to client, containing server’s current time. Common extension: Client requests time from server Server sends current time.

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Time Synchronization (RBS, Elson et al.)

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  1. Time Synchronization (RBS, Elson et al.) Presenter: Peter Sibley

  2. Traditional Synchronization Methods • Server sends messages to client, containing server’s current time. • Common extension: • Client requests time from server • Server sends current time. • Client estimates one-way latency from the round-trip time.

  3. NTP • (1-50ms) accuracy, most common time protocol. • Uses hierarchy attached to a external clock. • At the LAN level, workstations may use information from peers . Reference Clock: GPS ,Atomic Clock Stratum 1 Stratum 2 … Stratum 15 See: http://www.eecis.udel.edu/~mills/ntp.html

  4. Sources of Error • Send Time • Constructing message • Variable OS delays in moving message to the interface • Access Time • Waiting to transmit message. (depends on MAC) • Propagation Time • To time get to receiver’s interface • Receive Time • Time for interface to generate a message reception signal

  5. Observations (Elson et al.) • Try to remove send/access time errors. • Synchronize among receivers. • Relative time is more important. • Latency is less of an issue, determinism is what matters.

  6. Example Phase Est. • Node i at (0,0) is triggered at t=4. • Node j at (0,10) is triggered at t=5. • The moving object has velocity (0,10). • Notice, no reference to a global time scale.

  7. Estimation of Phase • A transmitter sends m reference packets • Each of the n receivers records the arrival times according to their local clock • The receivers exchange their observations • Receiver i computes phase offset to another other receiver j as average offsets.

  8. Phase-Estimation Simulation Results

  9. Estimation of Clock Skew • Each device’s crystal oscillator, has slightly different frequency. • Frequency of each oscillator varies over time. • Use Least-Squares fit, instead of averaging phase offsets. • Assumes phase error changes at a constant rate

  10. Implementations • Mote • Tested 5 motes, with periodic reference pulse. • 2 micro-sec resolution clock • Ipaq running linux 2.4, 802.11 wireless • Userspace Unix daemon. • Use UDP.

  11. Results (Mote)

  12. Results

  13. Multi-hop extension (example)

  14. Multi-hop algorithm

  15. Performance of multihop extension

  16. Information Driven Dynamic Sensor Collaboration for Tracking Applications, Zhao et al. Presenter: Peter Sibley

  17. Scenario

  18. Collaborative Tracking.

  19. Sequential Bayesian Estimation • Problem: Picking the next sensor, should be local choice. • Need to Pick the neighbor sensor that will improve the estimation the most. • Rephrase as an optimization problem, • Objective is Mixture of Information Gain and Cost

  20. Utility/Cost. • Different Utility functions can be used: • Mahalanobis Distance • Entropy Based • Estimated Likelihoods • (Depends on distributional assumptions) • Costs • Euclidean and weighted Euclidean distance from the leader node.

  21. Tracking Results

  22. Tracking Results

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