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What to do about the transitional region?

On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor Motes Yin Chen, Andreas Terzis November 2, 2011. Transitional region. Connected region. What to do about the transitional region?

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What to do about the transitional region?

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  1. On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor Motes Yin Chen, Andreas Terzis November 2, 2011

  2. Transitional region Connected region • What to do about the transitional region? • Place motes in the transitional region vs in the connected region

  3. Our Proposal • Occupy the transitional region • Perform random trials to construct links with high PRR • Based on the Log-normal radio model

  4. Motivation: Placing Relay Nodes

  5. Outline • Introduce log-normal path loss model • Discuss pitfalls • Present the experimental results – reality check

  6. Log-normal Path Loss Model • Received signal strength at a distance is • , is a Gaussian random variable • Due to artifacts in the environment (occlusions, multipath, etc.) • Does not consider temporal variation distance Sender Receiver Power of the transmitted signal Path loss exponent Random variation Path loss at distance

  7. Three Regions of Radio Links • As the distance increases, we go through 3 regions • Connected: • Transitional: • Disconnected: • Observation • The packet reception ratio at any given location is random

  8. Connected Region • In connected region • PRR is very likely to be high • Trying one location will likely produce good link 5 meters Sender Receiver

  9. Transitional Region • In transitional region • PRR may or may not be high • Trying a few spots should yield a good link 15 meters Sender Receiver

  10. Disconnected Region • In disconnected region • PRR is very unlikely to be high • Trying multiple spots seems worthless 40 meters Sender Receiver

  11. Outline • Introduce log-normal path loss model • Discuss pitfalls • Present the experimental results – reality check

  12. Pitfalls • Log-normal path loss model is not perfect • The Gaussian variation in signal strength is a statistical observation • Signal strengths at nearby locations are correlated

  13. Reality Check • Verify log-normal path loss model • Quantify spatial correlations • Count number of trials to construct good links • Investigate temporal variations

  14. Experimental Setup • Devices • TelosB motes • iRobot with an Ebox-3854 running Linux • Environments • Outdoor parking lot • Lawn • Indoor hallway • Indoor testbed • Two forests

  15. Evaluations on the Log-normal Model • Holds well in all the environments • Example figure for the parking lot • We can subtract the solid line from the raw RSSI readings • The residual RSSI values are samples of the random variable :

  16. Q-Q Plot of the Residual RSSI Values

  17. Reality Check • Verify log-normal path loss model • Quantify spatial correlations • Count number of trials to construct good links • Investigate temporal variations

  18. Spatial Correlation • PRR measurements at a parking lot • iRobot moves in a 2-d plane (the ground) • Black cell : PRR below 85%; Gray cell : PRR above 85% • PRR are correlated • Trying two adjacent locations flipping two coins • In all of our experiments, 1 meteris sufficient to remove most correlation

  19. Reality Check • Verify log-normal path loss model • Quantify spatial correlations • Count number of trials to construct good links • Investigate temporal variations

  20. Number of Trials - Configuration 1 meter • Grid sampling • Bernoulli trials • Number of trials to find a good PRR is geometrically distributed distance

  21. Number of Trials - Results • Measure and compute the length of connected region • Place motes at distances longer than

  22. Number of Trials – Fitting Geometric Distribution Suggests that 1 meter ensures independent trials.

  23. Connecting Two Motes Relay TARB TAR TBR Mote A Mote B • TAR: number of trials to connect to A • TBR: number of trials to connect to B • TARB: number of trials to connect to both A and B

  24. Reality Check • Verify log-normal path loss model • Quantify spatial correlations • Count number of trials to construct good links • Investigate temporal variations

  25. Temporal Variation • Box plots of residual RSSI values for two forests

  26. Conclusion • Log-normal model fits sensornets • Signal correlation vanishes at 1 meter separation • Easy to find good links in the transitional region • Rule of thumb: at twice the length of connected region, number of trials is less than 5 with high probability

  27. Application – Placing Relay Nodes • Number of relay nodes at large scale • Place 120 sensor motes in an area of size 800m by 800m • Run Steiner Tree algorithm to place relay nodes

  28. Application – Mobile Sensor Networks • Mobile sink • If the current spot yields low PRR, move 1 meter • Minimize travel distance • Mobile motes Signal variation in the space domain Signal variation in the time domain

  29. Thank you! Questions?

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