Sezgisel Optimizasyon Kullanan Ağırlıklı Görünürlük Grafı Tabanlı Kapalı Alan WiFi Konumlandırma Yöntemi

Year 2024, Volume: 19 Issue: 1, 133 - 145, 28.03.2024

Turan Göktuğ Altundoğan , Mehmet Karaköse

https://doi.org/10.55525/tjst.1254099

Abstract

Kablosuz iletişim teknolojilerinin ve IoT uygulamalarının yaygınlaşmasıyla birlikte araştırmacılar, WiFi sinyallerini iç mekân konum tespiti için kullanan yaklaşımlar geliştirmektedir. Bu çalışmada WiFi sinyal gücü (RSSI) değerlerine dayalı erişim noktalarının ağırlıklı görünürlük matrisleri oluşturularak sezgisel optimizasyon tabanlı yöntemlere dayalı iç mekan konumlandırma işlemi gerçekleştirilmiştir. Önerilen yöntemde, PSO ve GA yaklaşımları, görünürlük ağırlık matrislerine dayalı ortak bir uygunluk fonksiyonu kullanarak mobil kullanıcının konumunu belirler. Önerilen yöntem, RSSI aralıklarına dayalı konum aralıklarının belirlendiği sanal bir senaryo üzerinde test edilmiştir. Her iki sezgisel optimizasyon yöntemi farklı kriterlere göre karşılaştırılmış ve GA tabanlı yöntem için maksimum 3m, PSO tabanlı yöntem için maksimum 1,5m hata ile konumlandırma işlemi gerçekleştirilmiştir.

Keywords

WiFi, Kapalı Alan Konumlandırma, Görünürlük Grafları, PSO, GA

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

Abstract

With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method.

Keywords

WiFi Indoor Positioning, Visibility Graphs, PSO, GA

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