# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
## 2 Methods and Materials
### Wi-Fi Databases
When combining the databases, the mean location error is 125 m, the median location error is 39 m, and an estimate can be found at all 36 test locations. If we select the four BSSIDs with the highest RSSI, which requires us to use two Sigfox messages and increases the ten minutes update period to twenty minutes, the location error improves only

Figure 2:8 Median location error at each test location for both the WiGLE and LocationAPI databases, and for their combination. The location error is not consequently larger in less populated areas. See Figure S2 for an interactive map; its color legend is the same as in this figure. slightly. In the WiGLE database, the mean location error becomes 56.11 km, due to outliers. The median location error becomes 63 m, which is an improvement of only 3 m. However, no location estimate can be found at only 2 test locations. In the LocationAPI database, the mean location error is 71 m, and the median location error is 31 m. This is a larger improvement than in the case of WiGLE. A location estimate can be found at all 36 test locations. Because the best combination favored the LocationAPI estimate, the results for the combination are equal to those for LocationAPI. Thus, while faults in the results can be alleviated partly by sending two Sigfox messages rather than one, the improvement in location error is not so large as to make it an attractive option. Additionally, the reliability of the databases can definitely be questioned. For example, anyone can anonymously upload data to the databases. Hopefully, there is a kind of check before the data is actually made available to other users. Otherwise users can enter wrong data into the database, for any number of reasons. The managers of the WiGLE and LocationAPI database admit that there could be geographical errors in their databases. However, an administrator of the LocationAPI website stated the following [29]:

\"A lot of effort has gone into enabling our algorithms to auto-adapt based on the type of geography requested, correct for unclean data-sets (in case of crowd-sourced data) by intense cleansing of both contributor and customer data.\"

Since it is not possible for companies like WiGLE of UnwiredLabs to verify each coordinate of every uploaded wireless access point, a localization application should have some built-in control mechanisms. Imagine, for example, a user uploading 100 coordinates of access points in a specific, relatively small region. If 99 of the coordinates are only a few meters away from each other, and there is one coordinate a few kilometers further, this could indicate that this single coordinate is the result of the bad resolving in the database. The information of this access point should be left out of the data, to avoid sending a combination with the coordinate of that 'corrupt' BSSID. Another problem which must be tackled is that, even though Media Access Control (MAC) addresses are unique worldwide, there will always be MAC spoofers. MAC spoofers manipulate their MAC addresses for any number of reasons. Therefore, there should be a Duplicate Address Detection (DAD) mechanism in the localization application. Using DAD, it becomes possible to eliminate the entry with the 'false location' from the database if the'real' location is already registered multiple times in the database. Furthermore, it needs to be mentioned that there could be small distance errors, due to the limitations of the hardware. The GPS and the Wi-Fi adapter of the laptop have a certain refresh speed, so the user location or list of wireless access points is not instantly updated when moving. ### Population Density

Usually, a lot of Wi-Fi networks can be discovered in the heart of a city like Antwerp. In the countryside or in open fields or parks, it is harder to detect a Wi-Fi network, because there are just less WAPs available in such areas. Therefore, it is interesting to investigate whether or not there is a relationship between the kind of area and the distance error.