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Locating Sensors in the Wild: Pursuit of Ranging Quality

Locating Sensors in the Wild: Pursuit of Ranging Quality. Wei Xi, Yuan He , Yunhao Liu, Jizhong Zhao, Lufeng Mo, Zheng Yang, Jiliang Wang, Xiangyang Li. Outline. Motivation Observation on GreenOrbs Design of CDL Evaluation Ongoing work of GreenOrbs. GreenOrbs. Existing approaches (1).

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Locating Sensors in the Wild: Pursuit of Ranging Quality

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  1. Locating Sensors in the Wild: Pursuit of Ranging Quality Wei Xi, Yuan He, Yunhao Liu, Jizhong Zhao, Lufeng Mo, Zheng Yang, Jiliang Wang, Xiangyang Li

  2. Outline • Motivation • Observation on GreenOrbs • Design of CDL • Evaluation • Ongoing work of GreenOrbs

  3. GreenOrbs

  4. Existing approaches (1) • GPS • Problems with tree covers • Range-Based Approaches • TOA, TDOA, AOA • Require extra hardware support • Expensive in manufactory cost and energy consumption • RSSI-based • Based on the log-normal shadowing model • Inaccurate due to channel noise, interference, attenuation, reflection, and environmental dynamics

  5. Existing approaches (2) • Range-Free Approaches • Rely on connectivity measurements • The accuracy is affected by node density and network conditions • RSD (SenSys’09) • Regulated signature distance • SISR (MobiCom’09) • Merely differentiate good and bad links DV-Hop

  6. Outline • Motivation • Observation on GreenOrbs • Design of CDL • Evaluation • Ongoing work of GreenOrbs

  7. Two-folded ranging quality Node location accuracy & range measurement accuracy Fine-grained differentiation is necessary! 1. Irregular 2. Dynamic 3. Susceptible to the environment 4. Ubiquitous diverse errors

  8. Outline • Motivation • Observation on GreenOrbs • Design of CDL • Evaluation • Ongoing work of GreenOrbs

  9. Design of CDL Range-free localization: virtual-hop Local filtration: two types of matching Calibration: weighted robust estimation

  10. DV-Hop r 4 When non-uniform deployment is present, nodes with equal hop- counts often have different distances to the landmark(s). 1 2 7 3 1 4 3 5 2 4 8 3 3 6 4 4 5

  11. Virtual-hop localization For a node, its number of previous-hop or next-hop neighbors reflects the relative distance from the node to its parent node.

  12. Virtual-hop vs. DV-hop Compared with DV-hop, Virtual-hop reduces the localization errors by 10%~99%.

  13. Local filtration (1) Indiscriminate calibration probably reduces localization accuracy.

  14. Local filtration (2) • Bad nodes exhibit more mismatches • Neighborhood hop-count matching • Compare the real hop-distance with the one calculated using estimated node coordinates (a) A good node with one bad neighbor (b) A bad node with six good neighbors

  15. Local filtration (3) • Neighborhood sequence matching Matching degree Compare RSSI sequence with estimated distance sequence

  16. Local filtration (4) • According to the matching degree, we sort nodes into three classes • Good • Bad • Undetermined

  17. Ranging-Quality Aware Calibration The basic objective function in LSE RQAC • Weight good nodes by good neighbors • Differentiates links with different ranging qualities

  18. Outline • Motivation • Observation on GreenOrbs • Design of CDL • Evaluation • Ongoing work of GreenOrbs

  19. Evaluation • Setup • Experiments • 100 GreenOrbs nodes (4 landmarks) • Simulations • Randomly deploy 200~1000 nodes • A 500*500m2 square region • Transmission range: 30m

  20. Comparison

  21. CDF of localization errors

  22. Efficiency of iteration The number of good nodes quickly increases as iterations go on.

  23. Impact of environmental factors Humidity has a positive impact on the localization accuracy of all the four approaches.

  24. Impact of system parameters Increasing node density or landmarks yields better localization accuracy.

  25. Summary of CDL GreenOrbs • A most challenging scenario of WSN localization Our belief: ranging quality is two-folded • The location accuracy of the reference nodes • The accuracy of range measurements Combined and Differentiated Localization • VH localization addresses non-uniform deployment • Filtration picks good nodes with good location accuracy • RQAC emphasizes the contribution of the best range measurements

  26. Outline • Motivation • Observation on GreenOrbs • Design of CDL • Evaluation • Ongoing work of GreenOrbs

  27. Ongoing work of GreenOrbs • New applications • Carbon sink/emissions measurements • Forest fire risk prediction • Research on WSN management

  28. Thanks!Q & A

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