Implementation of Wi-Fi Signal Sampling on an Android Smartphone for Indoor Positioning Systems

Abstract

Collecting and maintaining radio fingerprint for wireless indoor positioning systems involves considerable time and labor. We have proposed the quick radio fingerprint collection (QRFC) algorithm which employed the built-in accelerometer of Android smartphones to implement step detection in order to assist in collecting radio fingerprints. In the present study, we divided the algorithm into moving sampling (MS) and stepped MS (SMS), and describe the implementation of both algorithms and their comparison. Technical details and common errors concerning the use of Android smartphones to collect Wi-Fi radio beacons were surveyed and discussed. The results of signal sampling experiments performed in a hallway measuring 54 m in length showed that in terms of the amount of time required to complete collection of access point (AP) signals, static sampling (SS; a traditional procedure for collecting Wi-Fi signals) took at least 2 h, whereas MS and SMS took approximately 150 and 300 s, respectively. Notably, AP signals obtained through MS and SMS were comparable to those obtained through SS in terms of the distribution of received signal strength indicator (RSSI) and positioning accuracy. Therefore, MS and SMS are recommended instead of SS as signal sampling procedures for indoor positioning algorithms.

Indoor Position Detection Using Smartwatch and Beacons

“…First, floor and room type: Wang et al (25) proposed a novel positioning method called IWKNN (isomap-based weighted KNN) to measure the distance of RSSI vectors from beacons. Liu and Liu (26) implemented a distributed system for collecting radio fingerprints by a mobile device using iBeacon and WiFi APs to identify the location. Alletto et al (27) proposed an indoor location-aware system for an IoT-based smart museum using BLE signal strength and combined it with image recognition to provide contents related to the observed artwork.…”

“…The number of people living alone globally rose from 153.5 million in 1996 to 202.6 million in 2006 and is expected to further increase by around 80% by 2026. (1) One-person households account for 28.9% of all households in Western Europe, 26.7% in North America, and 25.7% in Australia. (2) In Japan, the most rapidly ageing society in the world, around 30000 people die alone each year.…”

A Smartphone Indoor Positioning System Using Hybrid Localization Technology

“…In research related to indoor positioning, ref. [29] showed that BLE beacon positioning was able to scan the signal once per second, while Wi-Fi positioning showed that it was able to scan the signal every 3-4 s. Also, ref. [30] showed that BLE beacon positioning had better signal resolution than Wi-Fi.…”

Optimization-Based Wi-Fi Radio Map Construction for Indoor Positioning Using Only Smart Phones

“…The authors in [ 6 ] put trajectories generated from a smart phone and some fingerprints at known positions (defined as reference points, RPs) into an optimization framework to establish the RM. The authors in [ 7 , 8 ] propose a quick radio fingerprint collection (QRFC) algorithm for sampling the Wi-Fi signals by clicking on the starting and ending points on the map of the indoor environment. Some methods [ 9 , 10 , 11 ] propose to sparsely collect fingerprints at some known positions and then reconstruct the RM.…”