# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 6 Conclusions  
This paper presents a set of positioning strategies of fingerprinting in NB-IoT networks. The strategies, based on the W\(k\)NN algorithm, determine the position of a target device by combining information about network coverage and signal parameters from multiple cells. The use of multiple cells was made possible by the adoption of the Rohde & Schwarz TSMA6 mobile network scanner for data collection. The TSMA6 can in fact simultaneously collect information on all cells detected at a given location, overcoming the limitations of commercial NB-IoT modems, that typically only provide information on the cell that the device is associated to. The performances of the proposed strategies, in combination with the four parameters available for a NB-IoT signal--that is, RSSI, RSRP, RSRQ and SINR--were evaluated on the experimental data collected in two different cities: Oslo, Norway, and Rome, Italy. The proposed strategies were compared against state-of-the-art algorithms \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{3}{c}{**Minimum Average Positioning Error [m]**} \\ \cline{2-5} \multicolumn{1}{c}{\multirow{-2}{*}{Rome (RP Smoothing)}} & Op. 1 & Op. 10 & Op. 88 & Combined \\ \cline{2-5}  & 34.2 & 26.8 & 27.1 & 15.2 \\ \hline \multirow{-2}{*}{Rome (Total Smoothing)} & Op. 1 & Op. 10 & Op. 88 & Combined \\ \cline{2-5}  & 25.5 & 21.9 & 21.1 & 14.9 \\ \hline \multirow{2}{*}{Oslo (RP Smoothing)} & Op. 1 & Op. 2 & - & Combined \\ \cline{2-5}  & 19.0 & 18.5 & - & 15.7 \\ \hline \multirow{2}{*}{Oslo (Total Smoothing)} & Op. 1 & Op. 2 & - & Combined \\ \cline{2-5}  & 13.8 & 14.5 & - & 14.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Minimum average positioning error in meters using the “RP smoothing” and “total smoothing” approaches for the single-operator and combined-operators data in the Oslo and Rome datasets for the _weighted coverage_ strategy in combination with RSRQ (Oslo) and RSRP (Rome). that use single cell and multicell information. The results show that the proposed strategies outperform the previous approaches. The impact of both spatial density in the offline fingerprinting database and of network density was evaluated, and new approaches to improve positioning accuracy based on combination of data across operators and data processing were proposed and evaluated. The results highlighted the fact that the _weighted coverage_ strategy, which combines RF information and network availability, is overall the best choice across the four proposed strategies; it combines the high accuracy guaranteed under conditions of complete information by the _Coverage_ strategy only using RF information with the robustness shown by the _Net_ strategy only using network availability when information is scarce or incomplete. The results of the performance evaluation outlined in Section 5 can be used to assess how a fingerprinting system designed according to the proposed positioning strategies meets the three challenges identified in Section 1. * Challenge 1--Trade-off between efforts in the offline phase and accuracy: the data used in this work were collected during driving runs, with no run repetitions and without introducing any form of repeated measurements at each point. This is arguably the most straightforward measurement setup in an outdoor scenario and requires an effort comparable to other outdoor data measurement campaigns associated, for example, to services like Google Street View [49] and is thus definitely feasible for a company (e.g., an operator) willing to build its own positioning service. In addition, the performance improvement obtained by introducing interpolation reported in Section 5.3 suggests that the effort can be further reduced by increasing the spatial density using synthetic fingerprints, thus reducing the number of actual fingerprints collected during the runs.