## 9 Conclusions and Future Work  
Indoor localization is an active and noble research area for its numerous applications in various areas of our daily lives. Within indoor environments, tracking people and localizing objects have become a necessity and thus motivates many researchers to tackle the challenges of IPS. In this article, the dimensionality reduction of MSSID's RM has been proposed. Reducing the computational cost of IPS is very important for real-time system applications because large data require a long time to be processed. The reduction comprises three parts: first, clustering AOI into subareas to confine the location process into a subset of RPs. Second, fusing N RM of MSSIDs to create a delegate RM, which is used to present N RM of MSSID in the positioning process. Third, the use of a maximum spatial variance to select the most informative APs. The proposed system is designed according to the behavior of MSSID signals in time and spatial domain. Fusing RM of MSSID mitigates the outlier's issue, which is created due to the multi-path phenomenon. The results show that the fusion of N RM of MSSID in both phases mitigates the effect of the fading issue and minimizes the length of fingerprinting vectors by using the maximum spatial variance selection APs. In addition, the results exhibit the ability of maximum spatial variance selection APs technique to enhance the precision of IPS and reduce the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No. SSIDs**} & \multicolumn{2}{c}{**Distance Error (m)**} & \multicolumn{2}{c}{**Distance Error (m)**} & \multirow{2}{*}{**Calculation Reduction \%**} \\  & **Traditional (K-Means)** & & **Proposed (C-Means)** & \\ \hline
**10** & 1.1 & 1.07 & 17\% \\
**17** & 0.9 & 0.84 & 12\% \\
**22** & 0.87 & 0.84 & 10\% \\
**28** & 0.85 & 0.83 & 5\% \\
**34** & 0.86 & 0.82 & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The Calculation reduction of positioning process versus number of SSIDs.  
Figure 17: The proposed technique performance with the Fuzzy C-mean and K-mean approaches.  
computational costs as well. Light algorithm approaches such as the proposed system are feasible for real-time tracking because the positioning process requires only a few calculations in the online phase to find the location of MS.  
Future work will include several tasks to extend the current proposed system. First, the behavior of the mean of MSSIDs signals is close to path loss, as shown in Figure 5, because averaging the MSSIDs' signals contributes to mitigating the effect of the multipath issue. Accordingly, it might be possible to use the fused MSSIDs' RSS-based path loss model to determine the user's location. In this case, knowing the coordinates of APs in the AOI is sufficient to determine the user's location without creating a radio map, which requires more time and labor costs.  
Second, it can be very fruitful to investigate the use of the 2.4 GHz and 5 GHz bands simultaneously with MSSID that are deployed on the same AP to enhance the WLAN-based IPS. The coverage area of 5 GHz is smaller than 2.4 GHz [46]. Therefore, we can use 5 GHz for coarse positioning and the 2.4 GHz for fine positioning.  
**Author Contributions:** A.A. and I.A.-Q. conceived the presented idea and developed the closed-form of each algorithm. Simulations and results presentation were conducted by A.A. All authors verified the analytical methods and discussed the results contributed to the final manuscript. Visualization and Supervision, I.A.-Q. Reviewing and Editing, A.A. and I.A.-Q.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.