# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 2 Related Works  
This section provides an overview of related studies carried out on Indoor Localization estimation techniques with different machine-learning algorithms. [PERSON] et al. employed an ML classifier-based approach for indoor localization. The experiment tested the RSSI data collected from mobile devices (Sony Xperia XA1) and BLE product iTAG sent-out signals. The location column showed the iTAG at the building's entrance. Students assisted in the collection of the dataset. Three groups of twelve students each had an iTAG device formed. They entered the constrained space with their iTAGs turned on. Three marked locations represented the building's entrance--inside, in the vestibule, and outside--on an 18.35 m \(\times\) 3 m long hallway. Signals were collected using two Sony Xperia XA1 smartphones, which were located at the beginning and end of the 2.35 m long \"in vestibule\" space. The collection of RSSIs lasted for twenty minutes. The raw RSSIs and filtered RSSIs were the two datasets. Actual RSSIs from a smartphone are known as raw RSSIs. To smooth RSSIs, a feedback filter is used. The filtered data were trained using Naive Bayes and Support Vector Machine algorithms, and their results showed 0.95 accuracy for SVM when it had four vectors. [PERSON] et al. contributed to investigating mobile phone-based indoor localization using ML techniques. They set up an indoor experimental testbed, gathered 3110 RSSI data points, and assessed the effectiveness of many machine learning methods. The algorithm was tested with kNN, RFR, Multilayer and Perceptron, and ZeroR. It was concluded that the proposed methods surpassed other indoor localization methods, with a mean error as exact as 0.76 m. The study also suggested a hybrid instance-based strategy that, compared to conventional instance-based methods, accelerates performance by a factor of ten without sacrificing accuracy. Furthermore, the proposed study was examined on its precision, crucial usage in real-world settings, and how it was affected by less-dense datasets. Finally, by analyzing their performance in an online, in-motion experiment, they showed that these techniques are suitable for real-world deployment [20]. [PERSON] et al. investigated RSSI- and ML-based approaches for indoor localization. In this study, the researchers used Neural Networks to carry out localization strategies based on the RSSI measurements. The RSSI dataset was collected from the experiment in [14], and the position estimate outcomes were compared with two approaches (the ANN and the Decision Tree). The proposed study first employed an Artificial Neural Network with three inputs to estimate the position of each RSSI's triplet. Then, it calculated the mean's error value for each position acquired. A similar process was performed for the ANN design with four inputs, where they estimated the position for each of the four inputs and determined the mean's error value for these estimates. In summary, the tested algorithms achieved better precision than the Decision Tree algorithm [21]. [PERSON] et al. contributed by applying a weighted least-squares Techniques to RSSI-based localization to improve its accuracy. The study employed a weighted multilateration approach to make it more resilient to errors and less dependent on an ideal channel model. Two weighted least-squares techniques based on the conventional hyperbolic and circular positioning algorithms were used to estimate the precise location. These methods specifically took the accuracy of the various data into account. Through extensive real-world testing on different wireless network types and numerical simulations, these methodologies were contrasted against the conventional hyperbolic and circular positioning methods (a wireless sensor network, a Wi-Fi network, and a Bluetooth network). The methods yielded an increased tolerance to environmental changes as well as improved localization results, with very little additional processing expense [14]. [PERSON] et al.