scholarly journals Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 146395-146407
Author(s):  
Allan Dos S. Braga ◽  
Hugo A. O. Da Cruz ◽  
Leslye E. C. Eras ◽  
Jasmine P. L. Araujo ◽  
Miercio C. A. Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geometric Parameters ◽  
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.

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