# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 7 Discussion
Finally, the last limitation involves the issue of cell sector reliability, which is why we carried out a separate analysis of the mobility issues in an NB-IoT localization context. Fingerprint-based localization approaches all share one major disadvantage: creating and maintaining a large-scale outdoor fingerprint database requires a significant amount of time and effort. For this reason, outdoor fingerprinting is not that popular as when compared to outdoor ranging or indoor fingerprinting. However, this issue is being tackled by crowd-sourcing initiatives such as _TheThingsNetwork_ ([[https://www.thethingsnetwork.org/](https://www.thethingsnetwork.org/)]([https://www.thethingsnetwork.org/](https://www.thethingsnetwork.org/))), which enable efficient worldwide collection of training data through sensors, chip sets and even smartphones. On the other hand, fingerprint-based approaches have several benefits over others. In contrast to ranging, the locations of the base stations do not have to be known. Furthermore, when collecting training data, the particularities of the signal strength are stored in the fingerprint database. For this reason, the proposed fingerprinting algorithm consistently yields the highest localization accuracy. In order to evaluate the localization accuracy of an RSS-based fingerprinting approach, the parameters of a \(k\)NN algorithm are optimized. While in a similar study, the exponential RSS representation led to the highest localization accuracy [18], the best results in this study were obtained using the lineal (i.e., positive and normalized) RSS representation. Furthermore, the optimal parameter set can be expressed by the Pearson \(\chi^{2}\) distance and \(k=2\). Since the 1307 measurements in the fingerprinting data set are spread over an area of \(53\,\mathrm{km}^{2}\), the distance between each measurement in the training set is quite large, which in turn leads to a smaller value of nearest neighbors \(k\). Finally, the optimal parameter configuration is validated by running the \(k\)NN algorithm with unseen data, leading to a mean and median location estimation error of \(204\,\mathrm{m}\) and \(132\,\mathrm{m}\), respectively. During the collection of real-world measurement data for the localization study, we encountered some mobility issues in the NB-IoT protocol. Therefore, we highlight these issues in this work and analyze the impact on the localization performance. One major problem we faced was the inability to use information of neighboring cells in our localization algorithms. While in LTE, this feature has been implemented for years, only the serving cell can be used for RSS-based localization in NB-IoT. On the one hand, recent studies speculate that this limitation might be on purpose, in order to increase scalability and decrease signaling overhead and power consumption [22; 23]. On the other hand, some speculate the feature will be included in a future release of NB-IoT [1]. Either way, multiple neighboring cell reports can definitely increase the accuracy of RSS-based localization. Therefore, the reception of multiple cells will be simulated in future work. The results of the mobility analysis in Figure 7 demonstrate the cell reselection process of NB-IoT. When moving at a constant speed of 7 km/h, the cell reselection process took 73 s on average. Important to note is that this cell reselection time depends on both the velocity of the UE and cell reselection parameters configured by the operator. Decreasing the update rate of the cell reselection increases battery lifetime but is unfavorable from a localization perspective, as this introduces latency and reduced location update rates. Additionally, we investigated the cell sector reliability. After each cell reselection process, most of the messages are located within the sector of the new serving cell. However, it is worth discussing two peculiarities that can be observed from Figure 7. First, the UE remains connected to the same serving cell after moving outside the serving cell sector.