İHA Telemetri Verileri Üzerinde Sıralama Algoritmalarının Deneysel Performans Analizi

Öz

İnsansız Hava Araçları (İHA), uçuş operasyonları sırasında konum, hız, irtifa ve çeşitli sensör ölçümleri gibi yüksek hacimli telemetri verileri üretmektedir. Bu verilerin etkin bir şekilde işlenmesi, gerçek zamanlı karar verme süreçleri ve sistem performansı açısından kritik öneme sahiptir. Bu çalışmada, klasik sıralama algoritmalarının gerçek dünya İHA telemetri verileri üzerindeki performansı deneysel olarak değerlendirilmiştir. Deneyler, SurveilDrone-Net23 veri kümesi kullanılarak gerçekleştirilmiş ve üç farklı hesaplama platformunda incelenmiştir: Saf Python, NumPy tabanlı CPU ve GPU destekli PyTorch. Performans değerlendirmesinde temel ölçüt olarak çalışma süresi kullanılmıştır. Elde edilen sonuçlar, NumPy tabanlı sıralama uygulamasının verimli altyapı işlemleri sayesinde en iyi performansı sergilediğini göstermektedir. Ancak bu performans üstünlüğü tüm platformlarda aynı şekilde gözlenmemektedir. GPU tabanlı uygulamalarda, veri aktarımından kaynaklanan ek yük toplam çalışma süresi üzerinde önemli bir etkiye sahip olmakta ve paralel hesaplamanın sağladığı avantajları azaltabilmektedir. Genel olarak bulgular, sıralama performansının yalnızca algoritmik karmaşıklığa değil, aynı zamanda sistem ortamına ve veri işleme koşullarına da bağlı olduğunu ortaya koymaktadır. Sonuçlar, platform seçimi ve gerçek zamanlı İHA veri işleme sistemlerinin tasarımı açısından uygulamaya yönelik önemli çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Sıralama algoritmaları
• İHA telemetri verileri
• GPU hesaplama
• gerçek zamanlı veri işleme
• performans analizi

Etik Beyan

In the study, the authors declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Experimental Performance Analysis of Sorting Algorithms on UAV Telemetry Data

Öz

Unmanned Aerial Vehicles (UAVs) generate high-volume telemetry data during flight operations, including positional, speed, altitude, and various sensor measurements. Efficient processing of this data is critical for real-time decision-making and system performance. This study experimentally evaluates the performance of classical sorting algorithms on real-world UAV telemetry data. The experiments were conducted using the SurveilDrone-Net23 dataset and evaluated across three different computational platforms: Pure Python, NumPy-based CPU, and GPU-supported PyTorch. Execution time was used as the primary performance metric. The results indicate that the NumPy-based sorting implementation demonstrates the best performance due to efficient underlying operations. However, this performance advantage is not consistent across all platforms. In GPU-based implementations, data transfer overhead has a substantial impact on total execution time and may reduce the benefits of parallel computation. Overall, the findings highlight that sorting performance is influenced not only by algorithmic complexity but also by system environment and data processing conditions. The results provide practical insights for platform selection and real-time UAV data processing system design.

Anahtar Kelimeler

• Sorting algorithms
• UAV telemetry data
• GPU computing
• real-time data processing
• performance analysis

Etik Beyan

In the study, the authors declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Kaynakça

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