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Published bySybil Burns Modified over 9 years ago
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Krishna Chintalapudi Anand Padmanabha Iyer Venkata N. Padmanabhan ——presented by Xu Jia-xing
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Motivation Main idea of EZ Optimization Experiment Conclusion
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Motivation Main idea of EZ Optimization Experiment Conclusion
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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
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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?
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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
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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.
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Motivation Main idea of EZ Optimization Experiment Conclusion
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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
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Motivation Main idea of EZ Optimization Experiment Conclusion
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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
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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
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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.
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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.
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There are gain differences among different device. Introduce an additional unkown parameter G
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Calculate △Gk 1 k 2 is possible: ◦ represent all RSS from a device with a vector If “Close”
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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.
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Motivation Main idea of EZ Optimization Experiment Conclusion
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Normal accuracy.
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More training data greater accuracy.
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Great performance. Different devices are better.
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The same as one device.
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Great improvement.
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Motivation Main idea of EZ Optimization Experiment Conclusion
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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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