Fingerprint-based location estimation with virtual access points

Location fingerprinting techniques generally make use of existing wireless network infrastructure. Consequently, the positions of the access points (APs), which constitute an integral part of a location system, will invariably be dictated by the network administrator's convenience regarding data communication. But the localization accuracy of fingerprint-based solutions is largely dependent on the APs' placements over the area. In this paper, we developed the idea of virtual access point (VAP), where one can have AP's functionality at a desired position for localization purpose, without physically placing an AP there. We argue that, placing VAPs at favorable positions helps to improve localization accuracy. VAP also serves the purpose of virtually increasing the number of APs over the localization area, which according to previous works should enhance the localization accuracy further. We test the feasibility of our VAP idea both analytically and experimentally. Finally, we present our results using a well-known localization algorithm, namely, k-nearest neighbor, when our VAP idea is implemented. The findings are quite encouraging, which report significant improvement in the localization accuracy

Improving accuracy and simplifying training in fingerprinting-based indoor location algorithms at room level

Fingerprinting-based algorithms are popular in indoor location systems based on mobile devices. Comparing the RSSI (Received Signal Strength Indicator) from different radio wave transmitters, such asWi-Fi access points, with prerecorded fingerprints from located points (using different artificial intelligence algorithms), fingerprinting-based systems can locate unknown points with a fewmeters resolution.However, training the system with already located fingerprints tends to be an expensive task both in time and in resources, especially if large areas are to be considered. Moreover, the decision algorithms tend to be of high memory and CPU consuming in such cases and so does the required time for obtaining the estimated location for a new fingerprint. In this paper, we study, propose, and validate a way to select the locations for the training fingerprints which reduces the amount of required points while improving the accuracy of the algorithms when locating points at room level resolution.We present a comparison of different artificial intelligence decision algorithms and select those with better results. We do a comparison with other systems in the literature and draw conclusions about the improvements obtained in our proposal.Moreover, some techniques such as filtering nonstable access points for improving accuracy are introduced, studied, and validated.The research leading to these results has received funding from the “HERMES-SMART DRIVER” Project TIN2013-46801-C4-2-R within the Spanish “Plan Nacional de I+D+I” funded by the Spanish Ministerio de Economía y Competitividad, from the Grant PRX15/00036 for the “Estancias de Movilidad de Profesores e Investigadores Seniores en Centros Extranjeros de Enseñanza Superior e Investigación,” from the Ministerio de Educación Cultura y Deporte, and from the Spanish Ministerio de Economía y Competitividad funded projects (cofinanced by the Fondo Europeo de Desarrollo Regional (FEDER)), IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000), and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program

Cell identification based on received signal strength fingerprints: concept and application towards energy saving in cellular networks

The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated