Krishna Chintalapudi Anand Padmanabha Iyer Venkata N. Padmanabhan ——presented by Xu Jia-xing

 Motivation  Main idea of EZ  Optimization  Experiment  Conclusion

 Motivation  Main idea of EZ  Optimization  Experiment  Conclusion

 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

 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?

 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

 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.

 Motivation  Main idea of EZ  Optimization  Experiment  Conclusion

 x j : the j th location  c i : the i th AP’s location  P i : the power of the i th AP  p ij : the RSS received by mobile in the j th location form the i th AP  r i : the rate of fall of RSS in the vicinity of the i th AP

 Motivation  Main idea of EZ  Optimization  Experiment  Conclusion

 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 P i and r i ) Manner

 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

 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 four fixed 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 r i, there exist only two possible solutions for the three parameters of the AP.

 R4 : If an AP can be seen from two fixed locations, randomly pick P i and r i, 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.

 There are gain differences among different device.  Introduce an additional unkown parameter G

 Calculate △Gk 1 k 2 is possible: ◦ represent all RSS from a device with a vector If “Close”

Common MethodsAPSelect algorithm  Wide coverage  Low standard deviation in RSS  High average signal strength  Select each AP to provide information that other selected AP do not 1.Normalize p ij 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.

 Motivation  Main idea of EZ  Optimization  Experiment  Conclusion

Normal accuracy.

More training data greater accuracy.

Great performance. Different devices are better.

The same as one device.

Great improvement.

 Motivation  Main idea of EZ  Optimization  Experiment  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.