# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 2 Related Works
proposed a deep learning-based method for indoor localization in multi-floor environments. In this research work, they presented an improved algorithm based on deep learning, namely CNNLoc, a Convolutional Neural Network (CNN)-based indoor localization system with WiFi fingerprints for multi-story buildings and multi-floor localization. The authors contributed explicitly towards creating a novel classification model by fusing a one-dimensional CNN and a stacked auto-encoder (SAE). While the CNN was trained to attain high accuracy rates in the positioning phase efficiently, the SAE was used to precisely extract critical characteristics from sparse Received Signal Strength (RSS) data. Further, they assessed the proposed system using several cutting-edge techniques on the UJIndoorLoc and Tampere datasets. The findings demonstrated that the CNNLoc outperformed the existing methods, with success rates of 100% for building-level localization and also 95% for floor-level localization, respectively, where the results showed very high accuracy. Further, in [22], a framework for indoor localization with NN based on smartphones was proposed. Table 1 shows the comparison study of the existing works and finding. All the above works provided the potential to use ML techniques in the localization problem. Yet, the comparative analysis of the performance of multiple algorithms, linearization of RSSI signals, and hyperparameter tuning was lacking in the present works. Further, the performance of the model also highly depends on the dataset, wireless technology used, filter techniques, and linearization used. Advanced ML techniques, such as Deep Learning, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proposed** & **Machine Learning** & **The Complexity** & **Remarks** \\
**Localization** & **Algorithms Used** & **of the Algorithm** & **Algorithm** \\ \hline Localization & Convolutional Neural & Moderate & \begin{tabular}{c} Giving good accuracy; signal filtering and linearization not investigated \\ \end{tabular} \\ \hline Human Localization [15] & Neural Networks & Moderate & 77\% accuracy, no comparison with other algorithms \\ \hline Localization based & & & 78\% accuracy, only NN was investigated and no \\ on NNs [16] & Neural Networks & High & \begin{tabular}{c} 78\% accuracy, only NN was investigated and no \\ comparison with other types of ML techniques \\ \end{tabular} \\ \hline Localization & & & 82\% accuracy, high power consumption \\ using SVM [17] & Support Vector Machine & Moderate & \begin{tabular}{c} 82\% accuracy, high power consumption \\ as Wi-Fi was used \\ \end{tabular} \\ \hline Underwater Surveillance System [18] & Decision Tree-Based Localization & Low & \
\begin{tabular}{c} Decent accuracy of DT, no hyperparameter tuning \\ for DT algorithm \\ \end{tabular} \\ \hline Distributed & Self-Organizing Map & Moderate & Fairly good accuracy provided. Wi-Fi was used \\ \hline Soft Localization [20] & Neural Networks & Moderate &
\begin{tabular}{c} Providing a low accuracy. No comparison with other \\ ML models. None of the linearization techniques \\ were investigated. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison study of the existing works and to finding. require a large dataset for more accurate results. Additionally, the nature of the dataset causes overfitting and underfitting in ML models.