# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
## 2 Methods and Materials
### Wi-Fi Databases  
There is a variety of fingerprinting databases to work with. Some companies send their so called wardrivers all over the world to gather information about Wi-Fi access points. However, there is a trend to create more crowd-sourced databases [20]. People can create a free account and upload data of the access points in their neighborhood. The most popular and largest databases are summarized in Table 1.  
Databases with information about Wi-Fi access points are growing at a significant rate. This can be stated by observing that, for example, the Navizon database is growing at a rate of 500,000 data points every day [27]. It is interesting to note that this database also keeps track of the amount of cell phone towers. One of the many reasons of this significant rate is the Internet of Things (IoT) and the world of ubiquitous computing we are living in today. Navizon even pays people [28] to contribute to the database. The one million registered users can just download the app, drive around in their car and earn a pay-out of $15 for every 10,000 access points. One database used for this research is called WiGLE [26]. WiGLE is short for Wireless Geographic Logging Engine. The website is an open source platform; everyone can view registered BSSIDs around the world on a map. If a free account is created, requests can be made and data can be uploaded to the database. At this moment, WiGLE wardrivers have discovered over 4.5 billion Wi-Fi access points around the world. This database is chosen because it is easy to work with and has a decent amount of access points. Another database used for this research is LocationAPI [23]. LocationAPI is a website hosted by UnwiredLabs (Hyderabad, India), an Indian corporation focusing on positioning systems. At this moment, LocationAPI wardrivers have discovered over 1.5 billion Wi-Fi access points worldwide. Its database contains a large number of wireless access points in Belgium. To obtain an objective look at the error or accuracy of Wi-Fi localization through a Sigfox network, the characteristics of both databases are compared. WiGLE has a larger worldwide database. This does not mean that the WiGLE results are more accurate, because there are more access points discovered in the USA than in Belgium. WiGLE claims wardrivers have discovered 1.7 million WAPs in Belgium. On the other hand, while LocationAPI contains a smaller amount of access points, this database includes 11 million WAPs in Belgium. A request to the WiGLE database results in a string with a lot of information. It returns the latitude and longitude, but also data like security details and uptime indicators, see Figure 1. LocationAPI returns less information, but it is important to note that the data it does return is more useful: each result includes the mean coordinate of multiple BSSIDs, plus the estimated accuracy in meters. A nice extra feature is the 'balance' parameter. This informs us on how many successful requests we have left, see Figure 2. However, it is not possible to request the location of a Wi-Fi access point in LocationAPI. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Database Name** & **Amount of Wi-Fi Access Points** & **Reference** \\ \hline Combain Positioning Service & 1,296,000,000 & [22] \\ LocationAPI by UnwiredLabs & 1,500,000,000 & [23] \\ Mozilla Location Service & 30,000,000,000 & [24] \\ Navizon & unknown & [25] \\ WiGLE & 4,750,076,107 & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of largest Wi-Fi fingerprinting databases.