# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 7 Discussion  
Large-scale outdoor localization applications have been relying on GNSS-based solutions for years. However, with the emergence of IoT devices, we observe a growing need for alternative localization solutions that fulfill the requirements of long range and low power consumption. LPWANs provide a modern solution for both communication and localization of IoT devices. It is important not to look at GNSS-based and LPWAN-based solutions as competitors. In contrast, they can be complementary to each other, leading to combined positioning solutions such as Ubiscale ([[https://ubiscale.com/geo-iot/](https://ubiscale.com/geo-iot/)]([https://ubiscale.com/geo-iot/](https://ubiscale.com/geo-iot/))). While OTDoA in NB-IoT networks promises high-level accuracy, the technology is still in its infancy. As synchronizing eNBs in an NB-IoT network is a challenging and costly task, few operators already deployed this feature in their networks. Moreover, a location server is required in an OTDoA-capable network, which reports on neighboring cells. Furthermore, a highly accurate clock required in the UE leads to additional device complexity, which is often not desired. In this work, RSS-based localization has been studied using real-world NB-IoT measurements in a city-scale environment. Three RSS-based GPS-less algorithms are evaluated in terms of localization accuracy. The mean location estimation error varies from 340 m with a basic proximity algorithm to 204 m with an optimized fingerprinting approach. Set side by side, our previous research using multiple Sigfox base stations in the same environment resulted in a mean location estimation error of 340 m in the most optimal fingerprinting approach [18]. Moreover, we studied RSS-based localization with LoRaWAN, which coincidentally lead to an accuracy of 340 m as well, using another fingerprinting  
Figure 7: Visualization of NB-IoT cell reselection when moving at a constant speed around an eNB with three cell sectors. The color and pattern of the line connecting the GPS location of the UE to the eNB indicates the currently serving cell. The cell sector boundaries are indicated with thicker lines. approach and multiple base stations [20]. Even though there are more NB-IoT base stations than Sigfox and LoRaWAN base stations located in the test environment, only a single base station antenna was used per measurement in the NB-IoT experiments. Contrarily, in the LoRaWAN experiments, up to 10 gateways were taken into account to estimate the location of the transmitting device. In the Sigfox experiments, the maximum number of receiving gateways increases to 48. From this point of view, the ability to reach multiple NB-IoT base stations promises to increase the localization accuracy significantly, outperforming other LPWAN technologies. While the proximity algorithm results in a mean accuracy of \(340\,\mathrm{m}\), the localization accuracy of the RSS-based ranging algorithm increases only slightly to \(320\,\mathrm{m}\). This result can be explained by discussing three limitations of the proposed ranging algorithm. First, estimating the path loss in an urban but heterogeneous environment is not straightforward. Therefore, finding an optimal path loss model is a challenging task. Even if the path loss model estimates all distances very well, the average localization error highly depends on the sector size. This is due to the fact that all locations of UEs sending from within a certain cell sector and distance \(d\) to the eNB are estimated at the same centered location of that sector. A second limitation in the ranging algorithm is the fact that only a single directional cell antenna can be used to estimate the position of a UE. This is in contrast to Sigfox and LoRaWAN, where multiple receiving gateways yield more accurate results by performing multilateration. This argument indicates the growing need for multiple receiving cells in NB-IoT.