On distance estimation based on radio propagation models and outlier detection for indoor localization in Wireless Geosensor Networks

2010 ◽  
Author(s):  
Alexander Born ◽  
Mario Schwiede ◽  
Ralf Bill
Keyword(s):  
Outlier Detection ◽  
Indoor Localization ◽  
Distance Estimation ◽  
Radio Propagation ◽  
Propagation Models

scholarly journals CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar ◽  
Ki-Hyung Kim
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Principal Component ◽  
Nearest Neighbors ◽  
Signal Propagation ◽  
Location Based Services ◽  
Radio Propagation ◽  
Propagation Models ◽  
Related Information ◽  
Real Time Applications

Indoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-and-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K∗, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-time applications.

scholarly journals Wi-Fi Fingerprinting Based Room Level Indoor Localization Framework Using Ensemble Classifiers

2019 ◽  
Vol 38 (1) ◽  
pp. 151-174 ◽  
Author(s):  
Beenish Ayesha Akram ◽  
Ali Hammad Akbar
Keyword(s):  
Neural Network ◽  
Real World ◽  
Indoor Localization ◽  
Propagation Model ◽  
Radio Propagation ◽  
Ensemble Classifiers ◽  
Propagation Models ◽  
Nearest Neighbours ◽  
Set Up ◽  
Realistic Information

Over the past decennium, Wi-Fi fingerprinting based indoor localization has seized substantial attention. Room level indoor localization can enable numerous applications to increase their diversity by incorporating user location. Real-world commercial scale deployments have not been realized because of difficulty in capturing radio propagation models. In case of fingerprinting based approaches, radio propagation model is implicitly integrated in the gathered fingerprints providing more realistic information as compared to empirical propagation models. We propose ensemble classifiers based indoor localization using Wi-Fi fingerprints for room level prediction. The major advantages of the proposed framework are, ease of training, ease to set up framework providing high room-level accuracy with trifling response time making it viable and appropriate for real time applications. It performs well in comparison with recurrently used ANN (Artificial Neural Network) and kNN (k-Nearest Neighbours) based solutions. Experiments performed showed that on our real-world Wi-Fi fingerprint dataset, our proposed approach achieved 89% accuracy whereas neural network and kNN based best found configurations achieved 85 and 82% accuracy respectively.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals Outlier detection in indoor localization and Internet of Things (IoT) using machine learning

2020 ◽  
Vol 22 (3) ◽  
pp. 236-243 ◽  
Author(s):  
Mansoor Ahmed Bhatti ◽  
Rabia Riaz ◽  
Sanam Shahla Rizvi ◽  
Sana Shokat ◽  
Farina Riaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Outlier Detection ◽  
Indoor Localization

scholarly journals Distance Estimation based on 802.11 RTS/CTS Mechanism for Indoor Localization

10.5772/15670 ◽  
2011 ◽  
Author(s):  
Alfonso Bahillo ◽  
Patricia Fernndez ◽  
Javier Prieto ◽  
Santiago Mazuelas ◽  
Rubn M. ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Distance Estimation
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