Automatic Detection of Missing Access Points in Indoor Positioning System

Rafał Górak , Marcin Luckner


The paper presents a Wi-Fi-based indoor localisation system. It consists of two main parts, the localisation model and an Access Points (APs) detection module. The system uses a received signal strength (RSS) gathered by multiple mobile terminals to detect which AP should be included in the localisation model and whether the model needs to be updated (rebuilt). The rebuilding of the localisation model prevents the localisation system from a significant loss of accuracy. The proposed automatic detection of missing APs has a universal character and it can be applied to any Wi-Fi localisation model which was created using the fingerprinting method. The paper considers the localisation model based on the Random Forest algorithm. The system was tested on data collected inside a multi-floor academic building. The proposed implementation reduced the mean horizontal error by 5.5 m and the classification error for the floor’s prediction by 0.26 in case of a serious malfunction of a Wi-Fi infrastructure. Several simulations were performed, taking into account different occupancy scenarios as well as different numbers of missing APs. The simulations proved that the system correctly detects missing and present APs in the Wi-Fi infrastructure.
Author Rafał Górak (FMIS / DFG)
Rafał Górak,,
- Department of Foundations Geometry
, Marcin Luckner (FMIS / DSMKP)
Marcin Luckner,,
- Department of Structural Methods for Knowledge Processing
Journal seriesSensors, [SENSORS-BASEL], ISSN 1424-8220
Issue year2018
Publication size in sheets1.2
Keywords in PolishLokalizacja wewnątrz budynkowa, fingerprinting, rozwój i utrzymanie systemu
Keywords in Englishindoor localisation system; fingerprinting; system deployment and maintenance
ASJC Classification2208 Electrical and Electronic Engineering; 1303 Biochemistry; 3107 Atomic and Molecular Physics, and Optics; 1602 Analytical Chemistry
Abstract in PolishUtworzono system do nadzorowania lokalizacji wewnątrzbudynkowej opartej na Wi-FI. System pozwala wykryć niedziałające punkty dostępowe i automatycznie przebudować model lokalizujący, aby uniknąć błędów lokalizacyjnych wynikających z braku punktów dostępowych. System był testowany na rzeczywistych danych. Stosując poprawki zaproponowane przez system zredukowano średni błąd lokalizacji o 5.5m, a błąd klasykikacji piętra o 26 procent.
Languageen angielski
Score (nominal)30
Score sourcejournalList
ScoreMinisterial score = 30.0, 16-06-2020, ArticleFromJournal
Publication indicators WoS Citations = 1; Scopus Citations = 2; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.393; WoS Impact Factor: 2018 = 3.031 (2) - 2018=3.302 (5)
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