1 / 26

Efficient Network Flooding and Time Synchronization with Glossy

Efficient Network Flooding and Time Synchronization with Glossy. Federico Ferrari, Marco Zimmerling , Lothar Thiele, and Olga Saukh ETH Zurich IPSN 2011 Best Paper Award. Presenter: SY. Outline. Introduction Design Evaluation Conclusion. Flooding.

lucky
Download Presentation

Efficient Network Flooding and Time Synchronization with Glossy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Efficient Network Flooding and Time Synchronization with Glossy Federico Ferrari, Marco Zimmerling, LotharThiele, and Olga Saukh ETH Zurich IPSN 2011 Best Paper Award Presenter: SY

  2. Outline • Introduction • Design • Evaluation • Conclusion

  3. Flooding • Packet transmission from one node to all other • Challenges • Packet loss • Delay • Flooding storm

  4. Glossy • Flooding for wireless sensor networks • Fast: 94 nodes within 2.39ms • Reliable: 99.99% • Scalable • Time synchronization at no additional cost

  5. Interference • Capture effect • Two signals interfere which other • If one is stronger that the other • Or received significantly earlier than the others • Receiver might still receive the packet • Constructive interference • Identical packet • Small Δ Δ

  6. Generating Constructive Interference • Matlabsimulations

  7. Related Works • Capture effect • Backcast: Duttaet al. 2008 • Concurrent ACK transmission • A-MAC: Duttaet al. 2010 • Receiver-initiated link layer protocol

  8. Outline • Introduction • Design • Evaluation • Conclusion

  9. Overview • Decouples flooding • Concurrent transmission • Constant slot length

  10. Glossy in Detail

  11. Timeline

  12. Implementation • Platform • Tmote Sky = Taroko • MSP430F1611 + CC2420 • MCU and timer source by DCO • temperature and voltage drifts of -0.38%/◦C and 5%/V • Challenges • Deterministic execution timing • Start execution at same time • Compensate for hardware variations

  13. Deterministic execution timing • Start reading content while receiving • Immediately trigger transmission

  14. Start execution at same time • SFD interrupt • Variable delay in serving interrupt • Execute NOPs determined at runtime

  15. Compensate for hardware variations • Synchronizes the DCO every time Glossy starts • with respect to 32.768KHz crystal • Software delay uncertainty

  16. Outline • Introduction • Design • Evaluation • Conclusion

  17. Theoretical Analysis • Scenario • Worst-Case Drift of Radio Clock • Assume an upper/lower bound of radio clock drift • Worst-case scenario: • one path at highest clock drift, another at lowest • Model worst-case transmission time uncertainty • Worst-case temporal displacement • Uncertainty on pair of radio and MCU clock • Worst-case scenario: • one path at minimum variation, another at maximum • Worst-case temporal displacement Δ

  18. Results • Network size • Node density

  19. Controlled Experiments • Setup 1 • One initiator, two receivers • Delay one receiver by [0,8]us • Non-delay receiver@-20dBm, delayed@-13dBm

  20. Controlled Experiments • Setup 2 • One initiator, variable # of recievers • No delay

  21. Controlled Experiments • Setup 3 • One initiator, four receivers • Start a Glossy phase, computes reference time • Schedules next phase • All nodes activate an external pin when a phase start

  22. Testbed Experiments • Testbed • Motelab: 94 nodes over three floors • Twist: 92 nodes • Local: 39 nodes • Metrics • Flooding latency L • Flooding reliability R • Radio on time T

  23. Results • Node density no noticeable dependency • Performance depends on network size • Increase N significantly enhances flooding reliability

  24. Performance on Twist • Larger size, higher latency • 80% of nodes has 99.99% reliability even with lowest power • Radio on time increase with network size

  25. Maximum Number of Transmissions • Vary N

  26. Conclusion • Flooding and time sync are two important services • Well written, systematically analysis • Promising results • Detailed implementation • Testbed evaluation • Integrate with application might not be easy

More Related