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Indoor Localization Without the Pain

Indoor Localization Without the Pain. Krishna Chintalapudi Anand Padmanabha Iyer Venkata N. Padmanabhan. ——presented by Xu Jia-xing. Outline. Motivation Main idea of EZ Optimization Experiment Conclusion. Outline. Motivation Main idea of EZ Optimization Experiment Conclusion.

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Indoor Localization Without the Pain

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  1. Indoor Localization Without the Pain Krishna Chintalapudi Anand Padmanabha Iyer Venkata N. Padmanabhan ——presented by XuJia-xing

  2. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  3. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  4. Motivation-Related Work(1) • Schemes that require specialized infrastructure.  requires infrastructure deployment • Schemes that build RF signal maps.  takes too much time • Model-Based Techniques.  much less efforts than RF map; but still need a lot of work to fit the models

  5. Motivation-Related Work(2) • Localization in Indoor Robotics.  requires special sensors and maps • Ad-Hoc localization.  requires enough node density to enable multi-hopping Can we do indoor localization without such pre-deployments or limitations?

  6. Motivation-EZ(1) • Works with existing WiFi infrastructure only • Does not require knowledge of Aps(placement, power,etc) • Even work with measurements by a single device • Does not require any explicit user participation

  7. Motivation-EZ(2) • There are enough WiFi APs to provide excellent coverage throughout the indoor environment • Users carry mobile devices, such as smartphones and netbooks, equipped with WiFi • Occasionally a mobile device obtains an absolute location fix, say by obtaining a GPS lock at the edges of the indoor environment, such as at the entrance or near a window. Assumptions

  8. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  9. Main idea of EZ-LDPL equations

  10. Main idea of EZ • xj: the jth location • ci: the ith AP’s location • Pi: the power of the ith AP • pij: the RSS received by mobile in the jth location form the ith AP • ri: the rate of fall of RSS in the vicinity of the ith AP

  11. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  12. Optimization-GA • 10% of the solutions with the highest fitness are retained. • 10% of the solutions are randomly generated. • 60% of the solutions are generated by crossover. • The remaining 20% solutions are generated by randomly picking a solution from the previous generation and perturbing it(Only Pi and ri) Manner

  13. Optimization-Reducing the Search Space • Randomly pick Pi and ri with boundaries • Use the LDPL equation : if there are m APs and n locations then reduce from 4m+2n to 4m

  14. Optimization-Reducing the Search Space • R1 : If an AP can be seen from five or more fixed (or determined)locations, then all four of its parameters can be uniquely solved. • R2 : If an AP can be seen from fourfixed locations, there exist only two possible solutions for the four parameters of the AP. • R3 : If an AP can be seen from three fixed locations, randomly pick ri, there exist only two possible solutions for the three parameters of the AP.

  15. Optimization-Reducing the Search Space • R4 : If an AP can be seen from two fixed locations, randomly pick Pi and ri, there exist only two possible solutions for the two parameters of the AP. • R5 : If an AP can be seen from one fixed location, randomly pick all parameters. • R6 : If the parameters for three (or more) APs have been fixed, then all unknown locations that see all these APs can be exactly determined using trilateration.

  16. Optimization-Reducing the Search Space

  17. Optimization-Relative Gain Estimation Algorithm • There are gain differences among different device. • Introduce an additional unkown parameter G

  18. Optimization-Relative Gain Estimation Algorithm • Calculate △Gk1k2 is possible: • represent all RSS from a device with a vector If “Close”

  19. Optimization-APSelect algorithm 1.Normalize pij into range(0,1) 2.Let 3.Cluster APs one by one by 入 4.Select the AP which can be seen by most known locations. • Wide coverage • Low standard deviation in RSS • High average signal strength • Select each AP to provide information that other selected AP do not Common Methods APSelect algorithm

  20. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  21. Experiment-Performance

  22. Experiment-Performance Normal accuracy.

  23. Experiment-Training Data More training data greater accuracy.

  24. Experiment-new mobile Great performance. Different devices are better.

  25. Experiment-Multiple devices training The same as one device.

  26. Experiment-APSelect and LocSelect Great improvement.

  27. Outline • Motivation • Main idea of EZ • Optimization • Experiment • Conclusion

  28. Conclusion • The idea is good. It’s different from traditional methods. • The optimization is functional. • The LDPL Model is not perfect. • Does not mention how to refresh the RSS Model.

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