# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
## 2 Methods and Materials
### Wi-Fi Databases
Of the 36 locations, there are six locations which can be categorized as less densely populated areas: locations 6, 9, 10, 23, 14, and 32. These locations are indicated in Figure 2:9

There could be a relationship between the size of the median error and the kind of area where the measurement took place. Three out of the five biggest median errors were measured at a less densely populated location. However, the largest error was measured at a densely populated location and there are also errors in less densely populated areas that are smaller than the mean error. So, we cannot be sure if there is a relationship between the distance error and the kind of location. Despite the limitation of using only two BSSIDs, the amount of access points in the vicinity does play a role. The chance of having no match in the database increases in less populated areas from 13% to 20%. ## 5 Conclusions

We wanted to know how accurately we can localize a mobile device that communicates over Sigfox by observing nearby Wi-Fi access points. We used two online databases, WiGLE and LocationAPI, of Wi-Fi access points locations to search for the access points found by the mobile device. Because of the limitations in Sigfox communication, only two BSSIDs can be searched for in a single transmission, with a maximum of 140 transmissions per day. Using the WiGLE database, we achieved a median location error of 45 m, with a 86.8% chance of finding the combination of BSSIDs transmitted by the device. Using the LocationAPI database, we achieved a median location error of 23 m, with a 87.5% chance of finding the combination transmitted by the device. Both databases have outliers well above 100 km. However, only in the WiGLE database the 75 th percentile location error is larger than 1 km, and this only at two of the test locations. To increase the chance of finding the combination of BSSIDs transmitted by the device, we combined the results of both databases. Since the LocationAPI database is more accurate than the WiGLE database, we chose to combine them by only using the WiGLE database when the combination of BSSIDs was not found in the LocationAPI database. With this method, we achieved a median location error of 40 m, with a 98.1% chance of finding the combination transmitted by the device. Since only 140 transmissions are allowed per day, with only 6 per hour, and only two BSSIDs can be sent in a single transmission, we additionally explored a strategy for selecting the combination of BSSIDs. The strategy consists of selecting the two BSSIDs with the highest received signal strength. Using our combination of both databases, the median location error is 39 m, and this combination could be found on all 36 test locations. Moreover, we found that using two messages to transmit four BSSIDs does not decrease the location error significantly. After this experiment with real data, we want to increase the reliability by building a device that can both sense the Wi-Fi access points and transmit two BSSIDs over Sigfox. This will not only enable us to confirm our results in a closed experiment, but also allow us to measure how much energy is exactly required for this type of localization, so that we know if it is a viable way to track and trace assets in an Internet of Things scenario. The following are available online at [[http://www.mdpi.com/2076-3417/7/9/936/s1](http://www.mdpi.com/2076-3417/7/9/936/s1)]([http://www.mdpi.com/2076-3417/7/9/936/s1](http://www.mdpi.com/2076-3417/7/9/936/s1)), Figure S1: Dynamic map of test locations, Figure S2: Dynamic map of median error at the test locations. Results for WiGLE, LocationAPI, and the combination are colored as in the other figures: WiGLE is blue, LocationAPI is red, and the combination is yellow. Part of this research was funded by the Flemish FWO SBO 5004017N IDEAL-IoT (Intelligent DEnse And Longe range IoT networks) project, and by the ICON project MuSCLe-IoT. MuSCLe-IoT is a project realized in collaboration with imec, with project support from VLAIO (Flanders Innovation and Entrepreneurship).