## 7 Conclusions and Future Work  
This paper presented and evaluated the two innovative RFR-based models for estimating RSSI in indoor LP-IoT networks. Our research showed that the proposed models RFR(F) and RFR(S) outperform ANN-based models (ANN(F) and ANN(S)) in terms of accuracy and efficiency. This research also presented a notable improvement compared to the state-of-the-art 'TinyDraGon' model. The experimental results indicated that the RFR(F) technique exhibits an approximately 39.62% improvement in MSE over the ANN(F) model and a 37.86% improvement in MSE compared to TinyDraGon. Next, the RFR(S) model was analysed, and the results underscored the MSE reduction of 24.9% (the average of the testing MSEs of eight selected sequences in Table 3) over the ANN(F) model and an 80.59% reduction over TinyDraGon. In addition, we also performed the complexity analysis of our models by deploying the models on Raspberry Pi 4 Model B. The reduced CPU utilisation and memory usage of our RFR-based models not only validate their superiority but also highlight their practicality. This research represents a significant step forward in the development of efficient, accurate, and resource-conscious predictive models. Future work will focus on conducting a detailed probability analysis of the estimation error of the proposed models and exploring their scalability for larger networks in more complex IoT environments. Furthermore, a hybrid approach that combines the strengths of the ANN model and the RFR model will also be explored as a potential pathway forward. The potential improvements in the models' application in different environmental characteristics will also be considered as a subsequent investigation topic to enhance the models' adaptability and effectiveness across diverse settings.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multicolumn{4}{c}{**LP-IoT Deployment Platform-Raspberry Pi Model B**} \\ \hline \multicolumn{4}{c}{**Analysis Phase**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} \\ \hline \multirow{3}{*}{**Complexity Parameters**} & **Memory Usage (MBs)** & **CPU Utilisation (\%)** & **Memory Usage (MBs)** & **CPU Utilisation (\%)** \\ \hline \multicolumn{4}{c}{Feature-based RFR Model} & 923 & 2.1 & 925 & 1.27 \\ \hline \multicolumn{4}{c}{Feature-based ANN Model} & 1095.68 & 99.25 & 1095.68 & 7.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity comparison on LP-IoT device between RFR(F) and ANN(F) model.  
Figure 15: Visualisation of CPU usage comparison of RFR(S) model with ANN(S) Model across different RSSI sequences.  
**Author Contributions:** Conceptualisation, S.A., M.A.K. and S.u.R.; methodology, S.A., M.A.K. and S.u.R.; formal analysis, S.A. and M.A.K.; investigation, S.A., M.A.K. and S.u.R.; data curation, S.A.; writing--original draft preparation, S.A. and M.A.K.; writing--review and editing, S.A., M.A.K. and S.u.R.; supervision, M.A.K. and S.u.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Australian Government Research Training Program (AGRTP) scholarship from Charles Sturt University, Australia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors acknowledge the support received from School of Computing, Mathematics, and Engineering at Charles Sturt university.  
**Conflicts of Interest:** The authors declare no conflicts of interest.