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: 1. Client requests time from server 2. Server sends current time. 3. 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 1. A transmitter sends m reference packets 2. Each of the n receivers records the arrival times according to their local clock 3. The receivers exchange their observations 4. 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