# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
## 1 Introduction
Wi-Fi fingerprinting is excellently reviewed by [PERSON] and [PERSON] [15]. It consists of an offline and online phase. During the offline phase, a site survey is conducted that results in a fingerprint database. During the online phase a mobile unit is localized by comparing its fingerprint measurements with the database. A fingerprint, in the case of Wi-Fi, is a list of all nearby Wi-Fi access points, paired with their received signal strength at the mobile unit. One thing to consider in Wi-Fi fingerprinting is how to compare the fingerprint created by the mobile unit with the fingerprints in the database. [PERSON] et al. [16] provide an overview of 51 distance metrics that could be used for this comparison. Another consideration in Wi-Fi fingerprinting is the challenge of constructing the fingerprint database. While the traditional approach is to collect the fingerprints during a labor intensive site survey, some researchers have looked into crowd sourcing the database construction [17]. Since Sigfox is an ultra-narrowband technology, localization based on its communication signal is very challenging. [PERSON] et al. [18] show that the Cramer-Rao Lower Bound (CRLB) for estimating the range between mobile device and base station through the time delay of the signal is reversely proportional to the bandwidth. They also indicate that the CRLB for estimating that range through the received signal strength is proportional to the distance between the mobile unit and the base station; since Sigfox is a wide area network, we can assume generally that the CRLB will be rather high. [PERSON] et al. [19] demonstrate a classification or fingerprinting solution to Sigfox localization, employing GPS enabled devices to create the fingerprints. They achieve a classification accuracy of 78% in fingerprints with a radius of 150 m, separated by 2.5 times this distance from other fingerprints. In this article, we present our approach to Sigfox localization, based on sensing nearby Wi-Fi access points. Due to the limitations of Sigfox, we can transmit only two Basic Service Set Identifiers (BSSID), which are six bytes each. This leaves no payload space to transmit any received signal strength information. While Wi-Fi fingerprinting is well studied, we investigate for the first time the location error when reducing the available information to only two BSSIDs, when usually anywhere between ten to sixty BSteSIDs can be seen, including a Received Signal Strength Indicator (RSSI) value to indicate if it is nearby or far away. Although more information can be transmitted over Sigfox by using multiple messages, it still has a limit of six messages per hour. Thus, using two messages instead of one increases the update period from ten minutes to twenty minutes. This is why we are interested in using just a single Sigfox message. Additionally, we will use online crowd sourced Wi-Fi access point databases, which are inherently less accurate than the carefully constructed databases in common research. While crowd sourced services have obvious drawbacks, especially being noisy, [PERSON] et al. [20] achieved a 15 m median location error for 73% of their test scans. The aim of the research in this article is to establish the expected location error for our approach. The article continues as follows. Section 2 outlines the localization approach, discusses the online Wi-Fi databases, and presents the measurement locations. Section 3 describes the localization results, and the frequency of missing an access point. Section 4 analyses and discusses those results. Finally, Section 5 draws the conclusion.