İHA Destekli Nesnelerin İnterneti Ağlarında Kümeleme Tabanlı Bilgi Yaşı Minimizasyonu

Öz

İnsansız Hava Araçları (İHA’lar) büyük ölçekli Nesnelerin İnterneti (IoT) ağlarında veri toplama amacıyla büyük ilgi görmektedir. Ancak, enerji kısıtları altında Bilgi Yaşı’nı (AoI) en aza indirme hesaplama açısından zor bir problemdir. Klonal Seçim Algoritması (CSA) gibi meta-sezgisel algoritmalar seyrek IoT ağları için optimale yakın çözümler sağlamalarına rağmen ağdaki IoT düğümlerinin sayısı arttıkça ölçeklenebilirlikleri hızla azalmaktadır. Bu sınırlamayı aşmak için bu makale, UAV destekli IoT ağlarında ölçeklenebilir AoI minimizasyonu için kümeleme tabanlı bir yöntem önermektedir. Önerilen yaklaşımda, IoT düğümleri, Silhouette Skor yöntemiyle belirlenen optimal küme boyutu kullanılarak K-means kümeleme yöntemiyle kümelere ayrılmaktadır. Her küme, CSA kullanılarak bağımsız bir şekilde çözülmekte ve sonuçlar birleştirilerek küresel bir çözüm oluşturulmaktadır. Bu yaklaşım çözüm kalitesini korurken hesaplama karmaşıklığını önemli ölçüde azaltmaktadır. Ayrıca, birden fazla kümenin aynı anda optimize edilmesi için paralel işlem kullanılmaktadır. Simülasyon sonuçları, önerilen kümeleme tabanlı yaklaşımın, ortalama AoI ve enerji verimliliğini korurken, temel CSA’ya kıyasla hesaplama süresini %90,54 oranında azalttığını göstermektedir. Böylece bu yöntem, yüzlerce düğüm içeren büyük ölçekli UAV destekli IoT ağlarında AoI farkındalıklı veri toplamayı mümkün kılmaktadır.

Anahtar Kelimeler

• Bilgi Yaşı
• İnsansız Hava Aracı
• Nesnelerin İnterneti
• Kümeleme
• Klonal Seçim Algoritması
• Meta-sezgisel Algoritmalar

Proje Numarası

TÜBİTAK 3501 (124E544)

Cluster-Based Age of Information Minimization in UAV-Assisted IoT Networks

Öz

Unmanned Aerial Vehicles (UAVs) have attracted attention for collecting data in large-scale Internet of Things (IoT) networks. However, the Age of Information (AoI) minimization under energy constraints remains a computationally challenging problem. Although metaheuristic algorithms such as the Clonal Selection Algorithm (CSA) provide near-optimal solutions for sparse IoT networks, their scalability rapidly decreases with increasing numbers of IoT nodes in the network. To overcome this limitation, this work proposes a cluster-based method for scalable AoI minimization in UAV-assisted IoT networks. In the proposed approach, IoT nodes are clustered by using K-means clustering with optimal cluster size determination via the Silhouette Score method. Each cluster is solved independently using the CSA, and the results are merged to construct a global solution. This approach significantly reduces computational complexity while preserving the quality of the solution. Furthermore, parallel processing is used to optimize multiple clusters simultaneously. The results illustrate that the proposed cluster-based approach reduces the computation time by 90.54% compared to the baseline CSA while maintaining a comparable average AoI and energy efficiency. The method thus enables AoI-aware data collection in large-scale UAV-assisted IoT networks with hundreds of nodes.

Anahtar Kelimeler

• Age of Information
• Unmanned Aerial Vehicles
• Internet of Things
• Clustering
• Clonal Selection Algorithm
• Meta-heuristic Algorithms

Proje Numarası

TÜBİTAK 3501 (124E544)

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