# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 6 Conclusions
The experimental data collected using the above setup led to an average positioning error of about 15 m when adopting the data combination and preprocessing techniques described in Section 5.4, which is comparable to the error obtained when using GNSS receivers. * Challenge 2--Computational complexity and processing workload during the online phase: each of the two datasets used in this work led to the definition of a few thousand RPs, as detailed in Section 4.3. Considering, for example, the Oslo dataset, characterized by an average of 3600 RPs, the average time to serve a positioning request was 0.6 ms on a Dell Precision Tower 3640 workstation, equipped with an Intel Core i7-10700k processor and 32 GBs of RAM, allowing for about 1600 requests per second. Assuming the adoption of the same average distance between two RPs characterizing the datasets (about 3 m) on a regular grid without any gap, covering an area of 1 km\({}^{2}\) would require approximately 110,000 RPs, leading to about 0.8 s per request in the same setup, assuming an increase of the processing time quadratic with the number of RPs as indicated in Section 1. Extending the grid to an entire city might thus lead in theory to millions of RPs, with a potentially high processing load. However, several approaches can be adopted to address this issue: these include, among others, the introduction of a hierarchical approach based on clustering, as discussed for example in [27], the adoption of a machine learning algorithm in place of W\(k\)NN at the price of some loss of accuracy [7], or the introduction of dimension reduction algorithms such as the principal component analysis before running W\(k\)NN. Most importantly, it should be noted that although the total number of RPs may become very high for large urban coverage areas, it can be assumed that a device will use its active NB-IoT connection to transfer its fingerprint to the positioning server, automatically restricting the set of relevant RPs to those falling within the coverage area of the serving eNB. * Challenge 3--The number of NPCIs per point and impact on positioning accuracy: the results presented in Section 5 show that the density of eNBs and NPCIs in a commercial network deployment is sufficient to achieve a positioning accuracy comparable to the one provided by a GNSS system, in particular when broadcast signals transmitted by multiple operators are combined. In conclusion, the results presented in this work support the adoption of fingerprinting based on NB-IoT broadcast signals as a viable and feasible solution to provide accurate position information in IoT applications without the need for dedicated positioning hardware. Several future research directions can be identified based on the results obtained in this investigation. In particular, the results obtained using the total smoothing technique indicate that data processing in the online phase may markedly improve accuracy. While smoothing cannot be easily performed, a similar effect could be obtained by adopting metrics that combine multiple RF parameters in the definition of a fingerprint, performing thus a sort of averaging across parameters rather than in time (proper averaging) or space (smoothing) for a single parameter. Other future work items include the introduction of machine learning algorithms other than WNN based on singular vector machine and neural networks, and the comparison of OTDOA vs. fingerprinting on experimental NB-IoT data. Methodology, L.D.N. and G.C. ; software, L.D.N. ; validation, O.A., A.B., M.N. and M.-G.D.B; investigation, L.D.N. and G.C. ; resources, M.N. ; data curation, L.D.N. ; writing--original draft preparation, L.D.N. and G.C. ; writing--review and editing, O.A., A.B., M.N. and M.-G.D.B; visualization, L.D.N. and G.C. ; supervision, O.A., A.B. and M.-G.D.B. ; project administration, M.-G.D.B. ; funding acquisition, L.D.N., A.B. and M.-G.D.B. All authors have read and agreed to the published version of the manuscript.