RSSI-Based LoRa Simulation Environment: An MLP Application for Position Estimation

Abstract

In this study, an interactive simulation framework was developed to analyze the behavior of Received Signal Strength Indicator (RSSI) values in LoRa-based communication links. This Python-based framework was specifically designed to model a two-dimensional space containing fixed LoRa nodes, mobile targets, and multiple obstacles made of materials such as concrete, brick, wood, glass, and vegetation. Signal attenuation was calculated by combining the Free Space Path Loss (FSPL) model with material-based attenuation coefficients. The accuracy and functionality of the developed framework were validated through position estimation models based on Multi-Layer Perceptron (MLP) networks trained with simulated RSSI data. Nine models were constructed across three environmental scenarios and three sensor configurations to systematically evaluate the impact of sensor density and environmental complexity on positioning accuracy. The findings indicate that the proposed method demonstrates robust estimation performance even in environments with dense and complex obstacles. Furthermore, the ability of the system to simultaneously track multiple moving targets demonstrates the adaptability of the developed framework to practical applications. In conclusion, this study provides a flexible and reproducible research platform for evaluating RSSI-based position estimation algorithms and machine learning approaches.

Keywords

• LoRa
• RSSI
• Simulation Framework
• MLP
• Position Estimation

RSSI Tabanlı LoRa Simülasyon Ortamı: Konum Tahmini için Bir MLP Uygulaması

Abstract

Bu çalışmada, LoRa tabanlı haberleşme bağlantılarında RSSI (Received Signal Strength Indicator) değerlerinin davranışını analiz edebilmek amacıyla etkileşimli bir simülasyon altyapısı geliştirilmiştir. Python tabanlı olarak tasarlanan bu ortam, sabit LoRa düğümleri, hareketli hedefler ve beton, tuğla, ahşap, cam ile bitki örtüsü gibi farklı malzemelerden oluşan engelleri içeren iki boyutlu bir yapıya sahiptir. Sinyal zayıflaması, Serbest Uzay Yol Kaybı (Free Space Path Loss – FSPL) modeli ile malzeme tabanlı zayıflama katsayılarının birleşimi kullanılarak hesaplanmıştır. Geliştirilen altyapının doğruluğu ve işlevselliği, simülasyondan elde edilen RSSI verileriyle eğitilen Çok Katmanlı Algılayıcı (Multi-Layer Perceptron – MLP) tabanlı konum tahmin modelleri aracılığıyla doğrulanmıştır. Üç farklı çevresel senaryo ve üç farklı sensör konfigürasyonu altında oluşturulan dokuz model, sensör yoğunluğu ve çevresel karmaşıklığın konum doğruluğuna etkisini sistematik olarak değerlendirmiştir. Elde edilen sonuçlar, önerilen yaklaşımın karmaşık engel koşullarında dahi yüksek tahmin kararlılığı gösterdiğini ortaya koymuştur. Ayrıca, sistemin çoklu hareketli hedefleri eşzamanlı olarak takip edebilmesi, geliştirilmiş altyapının pratik uygulamalara uyarlanabilirliğini göstermektedir. Genel olarak bu çalışma, RSSI tabanlı konum tahmini algoritmalarının ve makine öğrenimi yaklaşımlarının test edilmesine olanak tanıyan esnek ve tekrarlanabilir bir araştırma platformu sunmaktadır.

Keywords

• LoRa
• RSSI
• Simülasyon altyapısı
• MLP
• Konum tahmini

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