A novel hybrid model for bluetooth low energy-based indoor localization using machine learning in the internet of things

Year 2024, Volume: 30 Issue: 1, 36 - 43, 29.02.2024

Yasin Görmez , Halil Arslan , Yunus Emre Işık , Sercan Tomaç

Abstract

Indoor localization involves pinpointing the location of an object in an interior space and has several applications, including navigation, asset tracking, and shift management. However, this technology has not yet been perfected, and many methods, such as triangulation, Kalman filters, and machine learning models have been proposed to address indoor localization problems. Unfortunately, these methods still have a large degree of error that makes them ill-suited for difficult cases in real-time. In this study, we propose a hybrid model for Bluetooth low energy-based indoor localization. In this model, the triangulation method is combined with several machine learning methods (naïve Bayes, k-nearest neighbor, logistic regression, support vector machines, and artificial neural networks) that are optimized and tested in three different environments. In the experiment, the proposed model performed similarly to the solo triangulation model in easy and medium cases; however, the proposed model obtained a much smaller degree of error for hard cases than either solo triangulation or machine learning models alone.

Keywords

Internet of Things, Indoor localization, Bluetooth low energy, Machine learning, Triangulation

Nesnelerin internetinde iç mekân lokalizasyonu için makine öğrenimini kullanan bluetooth düşük enerji tabanlı özgün hibrit model

Abstract

İç mekân konumlandırma, bir nesnenin iç mekândaki konumunun tam olarak belirlenmesi olarak tanımlanabilir ve navigasyon, varlık takibi ve vardiya yönetimi olmak üzere bir çok uygulama alanı bulunmaktadır. İç mekân konumlandırma problemlerini çözmek için üçgenleme, Kalman filtreleri ve makine öğrenmesi modelleri gibi birçok yöntem önerilmiştir ancak hala istenilen başarı oranları elde edilememiştir. Bu yöntemler deney ortamlarında başarılı sonuçlar elde etse de, gerçek zamanlı durumlarda hata oranları çok fazla olabilmektedir. Bu çalışmada, Bluetooth düşük enerji tabanlı iç mekân konumlandırma için hibrit bir model önerilmiştir. Bu modelde, üçgenleme yöntemini, üç farklı ortamda optimize edilmiş ve test edilmiş birkaç makine öğrenmesi yöntemiyle (Naive Bayes, k-en yakın komşu, lojistik regresyon, destek vektör makineleri ve yapay sinir ağları) birleştiren hibrit bir yaklaşım kullanılmıştır. Çalışmada önerilen model, kolay ve orta durumlarda üçgenleme modeline benzer şekilde performans göstermiş; ancak önerilen model, zor durumlar için üçgenleme veya tek başına makine öğrenimi modellerinden çok daha küçük bir hata oranı elde etmiştir.

Keywords

Nesnelerin interneti, İç mekân konumlandırma, Buluetooth düşük enerji, Makine öğrenmesi, Üçgenleme

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