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**Statistical learning theory for location fingerprinting in wireless LANs.**
*(English)*
Zbl 1067.68511

Summary: In this paper, techniques and algorithms developed in the framework of statistical learning theory are applied to the problem of determining the location of a wireless device by measuring the signal strength values from a set of access points (location fingerprinting). Statistical learning theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no special-purpose hardware is required.

The proposed techniques, based on the support vector machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.

The proposed techniques, based on the support vector machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in scientific literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.

### MSC:

68M10 | Network design and communication in computer systems |

68T05 | Learning and adaptive systems in artificial intelligence |