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Enhancing Wi-Fi-based Fingerprint Technique for Indoor Positioning System
(2024-01-01)
Nimnaul, Tanapol
;
Bureetes, Natchapong
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Siriwat, Siwat
;
Wongwirat, Olarn
This paper addresses the enhancement of the Wi-Fi-based fingerprint technique for an indoor positioning system applied in an experimental area. The conventional Wi-Fi-based fingerprint technique utilizes a k-nearest neighbor (k-NN) algorithm for position estimation. The k-NN algorithm is a simple and intuitive classification algorithm based on distance metric, i.e., Euclidean distance (ED), but often demonstrates limited accuracy. To mitigate this constraint and enhance positioning precision, advanced machine learning algorithms in artificial neural networks (ANNs) have been introduced. Although ANN algorithms are considered highly reliable, they are complex and resource-intensive algorithms, resulting in less suitable for a small-scale area that requires simple indoor positioning applications. In contrast, the random forest (RF) algorithm offers comparable positioning accuracy while being more computationally efficient, making it a favorable choice for such scenarios. The work in this paper enhances the accuracy of the Wi-Fi-based fingerprint technique for indoor positioning systems by adopting the RF algorithm over the k-NN alternative for position estimation accuracy. The number of received signal strength (RSS) data selected from appropriate access points (APs) in the area chosen by a feature selection method is a pivotal factor influencing accuracy improvements. The experimental results express the direct correlation between increased RSS data and accuracy improvement for both algorithms. Significantly, the application of the feature selection method using the information gain ratio augments the positioning accuracy specifically for the RF algorithm.
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Improving Machine Learning-Based Wi-Fi Fingerprint Technique with Feature Selection and Grid Search Methods
(2024-01-01)
Nimnaul, Tanapol
;
Wongwirat, Olarn
This paper presents the enhancement of the WiFi-based fingerprint technique for an indoor positioning system applied to a real experimental area. In typical Wi-Fi-based fingerprint techniques, classification algorithms such as k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) are used for position estimation. However, these algorithms do not perform well with high-dimensional and large datasets. They also face limitations related to overfitting and uninformative features in datasets. This paper overcomes these challenges by deploying feature selection based on filter methods. This process removes uninformative features from the datasets before feeding them to construct a training model. The grid search method is also employed to perform hyperparameter tuning, which is used to construct the outperforming models. The experimental setup involved collecting received signal strength indicators (RSSIs) from access points (APs) in the real indoor environment to create the radio map. The accuracy performance of the proposed methods was tested by employing the feature selection method on the radio map dataset and using the grid search method to find optimized hyperparameters for constructing the training models based on the k-NN, DT, and RF algorithms. The accuracy results were compared with those of non-feature selection and default hyperparameters used for the three algorithms. The computational results demonstrate that the RF algorithm outperforms the other two algorithms. Furthermore, performance is improved when using grid search and feature selection as proposed.
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A comparison of decision tree based techniques for indoor positioning system
(2018-04-19)
Chanama, Lummanee
;
Wongwirat, Olarn
Currently, an indoor positioning system based on a fingerprint technique for wireless networks under IEEE 802.11 standard uses a method to collect a received signal strengths (RSS) to create a radio map in an offline phase. Then, it detects the RSS in an online phase to compare and find the position. However, while detecting and gathering the RSS, there are some variations of the RSS that affect the accuracy of position estimation. Therefore, there are several methods used to estimate the position in order to improve the accuracy, but the one focused in this paper is a decision tree based classification. The decision tree based classification method is found that it can provide better improvement in accuracy than the others, e.g., K-Nearest Neighbor (K-NN), Bayesian, and Neural networks. However, the techniques used to construct the decision tree are varied depending on the algorithm used to implement. Therefore, this paper is a comparison of decision tree based techniques using typical decision tree (DT) and Gradient boosted tree algorithms for estimating the position indoor. In the study, the RSSs collected from access points in the experimental area are used as the training and testing data. The decision tree models are created by using typical DT and Gradient boosted algorithms based on the training data obtained. There are two factors to consider in the comparative study, i.e., the number of training data and the number of reference radio signals. The testing results from the experiment showed that the decision tree based on Gradient boosted algorithm yielded more accurate results than typical DT, where the amount of 19 reference radio signals and 50 samples of training data gave the best result.

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