Details of a Digital Twin for a LoRa Based Forest Fire Management System

Yıl 2025, Cilt: 2 Sayı: 1, 27 - 36, 28.03.2025

Buğra Aydın , Sema Oktuğ

Öz

Early detection of forest fires is vital for ecosystems. For this purpose, sensor networks collect data such as temperature and humidity and monitor changes in forests. Long-range and low-energy communication technologies such as LoRa are especially widely used in these networks. However, the management of these networks can be complicated since each forest has different requirements. Digital twin technology allows the simulation of different scenarios and optimization systems by creating virtual copies of physical systems to solve this problem. However, the relational structure of computer networks can be challenging for some artificial intelligence models used in digital twins. Graph neural networks help digital twins to understand and optimize the complicated structure of networks. In addition, it is not feasible for Internet of Things networks to meet digital twins’ two-way and continuous communication demand. Therefore, in this study, a forecaster model is designed to facilitate the integration of digital twins into these networks. The forecaster provides the data the digital twin needs by predicting the network’s future states from its past states. The first results of the study are promising, especially for small-scale networks. However, as the scale of the network grows, the errors made by the system also increase.

Anahtar Kelimeler

Digital Twin, IoT, Graph Neural Networks, Forest Fire Detection

LoRa Tabanlı Bir Orman Yangını Yönetim Sistemi Dijital İkizinin Ayrıntıları

Öz

Orman yangınlarının erken tespiti, ekosistemler için hayati önem taşır. Bu amaçla sensör ağları, sıcaklık ve nem gibi verileri toplayarak ormanlardaki değişiklikleri izler. Özellikle LoRa gibi uzun menzilli ve düşük enerjili iletişim teknolojileri, bu ağlarda yaygın olarak kullanılır. Ancak bu ağların yönetimi, her bir ormanın farklı gereksinimleri olduğundan karmaşık olabilir. Dijital ikiz teknolojisi, bu sorunu çözmek için fiziksel sistemlerin sanal kopyalarını oluşturarak, farklı senaryoları simüle etmeye ve sistemleri optimize etmeye olanak tanır. Lakin bilgisayar ağlarının ilişkisel yapısı dijital ikizde kullanılan bazı yapay zeka modelleri için zorlayıcı olabilir. Grafik sinir ağları ise dijital ikizlerin, ağların karmaşık yapısını anlamasına ve optimize etmesine yardımcı olur. Ayrıca, nesnelerin interneti ağlarının, dijital ikizlerin iki yönlü ve sürekli iletişim talebini karşılaması uygulanabilir değildir. Bu nedenle, bu çalışmada dijital ikizlerin bu ağlara entegrasyonunu kolaylaştıracak bir tahminci modeli tasarlanmıştır. Tahminci ağın geçmiş durumlarından gelecek durumlarını tahmin ederek dijital ikizin ihtiyacı olan veriyi sağlar. Çalışmanın ilk sonuçları özellikle küçük ölçekli ağlar için umut vericidir. Ancak ağın ölçeği büyüdükçe sistemin yaptığı hatalar da artmaktadır.

Anahtar Kelimeler

Dijital İkiz, Nesnelerin İnterneti, Grafik Sinir Ağları, Orman Yangını Tespiti

Kaynakça

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