# A REM Update Methodology Based on Clustering and Random Forest
## 5 Conclusions  
In this paper, we proposed a REM update methodology based on clustering and the RF algorithm. The proposed approach divides the area of interest into several clusters by using the \(K\)-means algorithm, and it trains one RF model per cluster based on real data measurements assigned to the corresponding clusters. A mobile robot was used to collect the RSSI measurements, which can reduce the risk of human error while improving the accuracy and reliability of the measurements. Next, only the RF models for clusters with enough measurement samples were updated when newly collected measurements became available. As time passed, the proposed scheme could update the whole REM by sector while reducing the error each time. Simulation results proved the superior performance of the proposed scheme compared to several well-known ML models, as well as the conventional case without clustering, in various scenarios, including the presence of obstacles and AP relocation. Subsequently, the proposed framework will be very useful for REM management in wireless scenarios where the physical element distribution constantly changes. For future research directions, an exciting topic is the utilization of information from the REM to optimize the allocation of resources in wireless networks, including spectrum, power, and antennas. This can improve overall system performance by leveraging the knowledge of the wireless propagation environment provided by the REM. For instance, the development of schemes to optimize the placement and the reflection properties of an RIS based on the information contained in a REM can help achieve desired wireless communication performance.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \multicolumn{3}{c}{**4 Clusters**} & \multicolumn{3}{c}{**Without Clustering**} \\ \hline
**Error metric** & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{t_{3}}\) & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{t_{3}}\) \\ \hline MAPE & 7.11\% & 4.24\% & 2.19\% & 8.61\% & 7.45\% & 5.96\% \\ RMSE & 5.806 & 4.018 & 2.346 & 6.317 & 6.422 & 4.749 \\ R2 score & 0.044 & 0.255 & 0.826 & \(-\)0.132 & \(-\)0.902 & 0.288 \\ \hline \end{tabular}
\end{table}
Table 4: Error evaluations from newly collected measurements after the relocation of the AP.  
**Author Contributions:** Conceptualization, M.R.C., C.E.G. and I.K.; methodology, M.R.C.; software, M.R.C., C.E.G. and T.H.; validation, C.E.G., T.H. and I.K.; formal analysis, M.R.C.; investigation, M.R.C. and C.E.G.; resources, T.H. and I.K.; data curation, M.R.C. and T.H.; writing--original draft preparation, M.R.C.; writing--review and editing, C.E.G., T.H. and I.K.; visualization, M.R.C. and T.H.; supervision, I.K.; project administration, I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government's Ministry of Science and ICT (MSIT) under Grant NRF-2021R1A2B5 B01001721, and in part by the Regional Innovation Strategy (RIS) through the NRF funded by the Ministry of Education (MOE) under Grant 2021 RIS-003.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.