Derin Öğrenme Kullanılarak Oda Seviyesinde Wi-Fi Parmak izi Tabanlı İç Ortam Konumlandırma

Kablosuz iletişim teknolojisi ve akıllı cep telefonlarının gelişimi ile Wi-Fi ve cep telefonlarına dayalı konumlandırma hizmetlerine talep gün geçtikçe artmaktadır. Dış ortamlarda insanların veya nesnelerin konumlandırılması için GPS gibi küresel konumlandırma sistemleri kullanılırken iç ortamlarda duvar, kapı gibi engellerden dolayı uydu bağlantısı yeterli olmadığı için iç ortam konumlandırma yöntemleri tercih edilmektedir. İç ortam konumlandırma için önerilen birçok yöntem içerisinden parmak izi yöntemi günlük hayatta mevcut olan sinyal kaynaklarını kullanabildiğinden ve bu sinyalleri ekstra bir donanıma gerek duymadan cep telefonları ile elde edilebildiğinden diğer yöntemlere göre daha avantajlı bir hale gelmektedir. Bu çalışmada odaları birbirinden ayırt etmek amacı ile ev ortamında 6 farklı odadan alınan Wi-Fi sinyalleri ile oluşturulan veri kümesi, klasik bazı makine öğrenmesi yöntemleri ve derin öğrenme yaklaşımı uygulanarak oda seviyesinde sınıflandırılmıştır. Derin öğrenme uygulanması sonucunda makine öğrenmesi yöntemlerinden en yüksek sınıflandırma doğruluğuna sahip olan Rastgele Orman’ a göre %8 daha yüksek doğruluk oranı elde edilmiştir. Kendi veri kümemizin yanı sıra farklı sayıda veri ve özniteliklere sahip veri kümelerinde (WASP ve WILDS) de makine öğrenmesi yöntemleri ile derin öğrenmenin bir yöntemi olan Evrişimsel Sinir Ağı (ESA) karşılaştırılmış ve ESA’ nın %98 doğruluğa ulaştığı görülmüştür. Çalışma sonucunda ESA ile uygulanan derin öğrenmenin veri sayısı fazla ve etiket sayısı az olan veri kümelerinde daha iyi performans gösterdiği gözlemlenmiştir.

Room-Level Wi-Fi Fingerprinting Based Indoor Localization Using Deep Learning

With the development of wireless communication technology and smartphones, demand for Wi-Fi and mobile phone-based positioning services is increasing day by day. While the global positioning systems such as GPS are used to detect the position of people or objects in outdoor environments, indoor localization methods are preferred because satellite connections are not sufficient due to obstacles such as walls and doors. The fingerprint method is more advantageous than the other methods since it can use the signal sources available in daily life from many recommended methods for indoor localization and these signals can be obtained with mobile phones without the need for extra equipment. In this study, the data set using Wi-Fi signals obtained from 6 different rooms in the home environment to differentiate the rooms is classified in the room level by using some classical machine learning methods and deep learning approach. As a result of the deep learning, 8% higher accuracy than the Random Forest which has the highest classification accuracy has been obtained from the machine learning methods. In addition to our own data set, data sets with different numbers of data and attributes (WASP and WILDS) are compared with the methods of machine learning, and the Convolutional Neural Network (ESA) which is a method of deep learning, , and it is observed that the ESA reached the highest accuracy rate with %98. As a result of the study, it is observed that deep learning with ESA performed better in data sets with a high number of data and a low number of labels.

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