Determination of beacon distance values according to ambient conditions by polynomial interpolation and artificial neural networks

Recep Çakır; Serkan Doğanalp

Makale detayları

Determination of beacon distance values according to ambient conditions by polynomial interpolation and artificial neural networks

Yazarlar:

Recep Çakır | Serkan Doğanalp

Yayıncı:

Pamukkale University

Ana Başlıklar:

Mimari Mühendislik

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.31 Issue 3, pp.351-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001352056

pISSN:

2147-5881

Anahtar kelimeler:

Özet:

 Beacon devices, which have Bluetooth infrastructure, can be turned on and off with a single button, are small and portable, and can operate for long periods of time with low energy, have become very popular in recent years. It is used in many areas, especially in promotion and location-based projects. In particular, the distance between the Beacon device and the user can be determined by utilizing the change in the strength of the Bluetooth signals sent by the Beacons. This distance information is used as a basic parameter in positioning algorithms. The distance between the Beacon and the user is automatically presented to the users by using the data obtained by the Beacon manufacturers with standard ambient conditions. The user can also calculate this distance by using the data created by the user according to the ambient conditions. In this study, Bluetooth signals from Beacons in an indoor environment were recorded and the distance between the Beacon and the user was calculated by polynomial interpolation and artificial neural network methods. The results obtained were compared with the distance information automatically presented to the user by the Beacons. The best result was achieved by 10th order polynomial interpolation, which was 1.21m better than the distance information automatically received from the Beacons.

 Beacon devices, which have Bluetooth infrastructure, can be turned on and off with a single button, are small and portable, and can operate for long periods of time with low energy, have become very popular in recent years. It is used in many areas, especially in promotion and location-based projects. In particular, the distance between the Beacon device and the user can be determined by utilizing the change in the strength of the Bluetooth signals sent by the Beacons. This distance information is used as a basic parameter in positioning algorithms. The distance between the Beacon and the user is automatically presented to the users by using the data obtained by the Beacon manufacturers with standard ambient conditions. The user can also calculate this distance by using the data created by the user according to the ambient conditions. In this study, Bluetooth signals from Beacons in an indoor environment were recorded and the distance between the Beacon and the user was calculated by polynomial interpolation and artificial neural network methods. The results obtained were compared with the distance information automatically presented to the user by the Beacons. The best result was achieved by 10th order polynomial interpolation, which was 1.21m better than the distance information automatically received from the Beacons.

 Beacon devices, which have Bluetooth infrastructure, can be turned on and off with a single button, are small and portable, and can operate for long periods of time with low energy, have become very popular in recent years. It is used in many areas, especially in promotion and location-based projects. In particular, the distance between the Beacon device and the user can be determined by utilizing the change in the strength of the Bluetooth signals sent by the Beacons. This distance information is used as a basic parameter in positioning algorithms. The distance between the Beacon and the user is automatically presented to the users by using the data obtained by the Beacon manufacturers with standard ambient conditions. The user can also calculate this distance by using the data created by the user according to the ambient conditions. In this study, Bluetooth signals from Beacons in an indoor environment were recorded and the distance between the Beacon and the user was calculated by polynomial interpolation and artificial neural network methods. The results obtained were compared with the distance information automatically presented to the user by the Beacons. The best result was achieved by 10th order polynomial interpolation, which was 1.21m better than the distance information automatically received from the Beacons.

 Bluetooth alt yapısına sahip olan, tek bir tuşla açılıp kapatılabilen, küçük boyutlu ve taşınabilen, düşük enerji ile uzun süreler çalışabilen Beacon cihazları son yıllarda oldukça popüler hale geldi. Tanıtım ve konum bazlı projeler başta olmak üzere birçok alanda kullanılmaktadır. Özellikle Beaconlar tarafından gönderilen Bluetooth sinyallerinin gücündeki değişimden yararlanılarak Beacon cihazı ile kullanıcı arasındaki mesafe belirlenebilmektedir. Bu mesafe bilgisi ise konumlandırma algoritmalarında temel parametre olarak kullanılmaktadır. Beacon üreticisi firmalar tarafından, standart ortam koşulları ile elde edilen veriler kullanılarak Beacon ile kullanıcı arasındaki mesafe otomatik olarak kullanıcılara sunulmaktadır. Kullanıcı bu mesafeyi ortam koşullarına göre kendi oluşturduğu verileri kullanarak da hesaplayabilmektedir. Bu çalışmada kapalı bir alanda Beaconlardan gelen Bluetooth sinyalleri kaydedilerek ortam koşullarına göre Beacon ile kullanıcı arasındaki mesafe polinom enterpolasyonu ve yapay sinir ağları yöntemleriyle hesaplanmıştır. Elde edilen sonuçlar, Beaconlar tarafından kullanıcıya otomatik olarak sunulan mesafe bilgisi ile karşılaştırılmıştır. Çalışmada en iyi sonucu, Beaconlardan otomatik olarak alınan mesafe bilgisinden 1.21m daha iyi sonuç veren 10. dereceden polinom enterpolasyonu sağlamıştır.

Makale detayları

Determination of beacon distance values according to ambient conditions by polynomial interpolation and artificial neural networks

Yazarlar:

Recep Çakır | Serkan Doğanalp

Yayıncı:

Pamukkale University

Ana Başlıklar:

Mimari Mühendislik

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.31 Issue 3, pp.351-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001352056

pISSN:

2147-5881

Beacon

Bluetooth

Artificial neural networks

Polynomial interpolationBeacon

Bluetooth

Yapay sinir ağları

Polinom enterpolasyonu

Sayı

Makale detayları

Yazarlar: Recep Çakır | Serkan Doğanalp

Yayıncı: Pamukkale University

Ana Başlıklar: Mimari Mühendislik

Dergi: Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.31 Issue 3, pp.351-356

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001352056

pISSN: 2147-5881

Sayı

Yazarlar:

Recep Çakır | Serkan Doğanalp

Yayıncı:

Pamukkale University

Ana Başlıklar:

Mimari Mühendislik

Dergi:

Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi Vol.31 Issue 3, pp.351-356

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001352056

pISSN:

2147-5881