Locating smartphone users will enable numerous potential applications such as monitoring customers in shopping malls. However, conventional received signal strength (RSS)-based room-level localization methods are not likely to distinguish neighboring zones accurately due to similar RSS fingerprints. We solve this problem by proposing a system called feature-based room-level localization (FRL). FRL is based on an observation that different rooms vary in internal structures and human activities which can be reflected by RSS fluctuation ranges and user dwell time respectively. These two features combing with RSS can be exploited to improve the localization accuracy. To enable localization of unmodified smartphones, FRL utilizes probe requests, which are periodically broadcast by smartphones to discover nearby access points (APs). Experiments indicate that FRL can reliably locate users in neighboring zones and achieve a 10% accuracy gain, compared with conventional methods like the histogram method.
|Title of host publication||Smart City 360 - 1st EAI International Summit, Smart City 360, Revised Selected Papers|
|Editors||Veronika KRUTILOVA, Dagmar CAGÁŇOVÁ, Daniela ŠPIRKOVÁ, Julius GOLEJ, Kim NGUYEN, Radim LENORT, David HOLMAN, David STAŠ, Pavel WICHER, Alberto LEON-GARCIA|
|Number of pages||12|
|Publication status||E-pub ahead of print - 29 Jun 2016|
|Event||SmartCity 360: International Summit Smart City 360° - Bratislava, Slovakia|
Duration: 13 Oct 2015 → 16 Oct 2015
|Name||Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering|
|Conference||SmartCity 360: International Summit Smart City 360°|
|Period||13/10/15 → 16/10/15|
Bibliographical noteThe research was partially supported by NSFC/RGC Joint Research Scheme under Grant N_PolyU519/12, and NSFC under Grant 61332004.
- Room-level localization