Intelligent Data Management in IoT: A Machine Learning-Based Approach

Abstract

With the increasing adoption of Internet of Things (IoT) devices, the volume of generated data has surged, creating challenges in data classification and management. This study introduces a machine learning-based approach to improve the organization and utilization of IoT data. By implementing the Random Forest algorithm alongside the Synthetic Minority Over-sampling Technique (SMOTE), the study demonstrates how these methods enhance classification accuracy and data balance. The practical applications of these techniques in various fields, including smart cities, healthcare, and industrial automation, are examined. The findings highlight the importance of scalable, intelligent data management strategies in optimizing IoT ecosystems and ensuring efficient decision-making processes

Keywords

• IoT Data Management
• Machine Learning
• Data Classification
• Random Forest
• SMOTE
• Real-World Applications

IoT'de Akıllı Veri Yönetimi: Makine Öğrenmesi Tabanlı Bir Yaklaşım

Abstract

Nesnelerin İnterneti (IoT) cihazlarının giderek daha fazla benimsenmesiyle birlikte, üretilen veri hacmi artmış, bu da veri sınıflandırma ve yönetiminde zorluklar yaratmıştır. Bu çalışma, IoT verilerinin organizasyonunu ve kullanımını iyileştirmek için makine öğrenimi tabanlı bir yaklaşım sunmaktadır. Çalışma, Sentetik Azınlık Aşırı Örnekleme Tekniği (SMOTE) ile birlikte Rastgele Orman algoritmasını uygulayarak, bu yöntemlerin sınıflandırma doğruluğunu ve veri dengesini nasıl geliştirdiğini göstermektedir. Bu tekniklerin akıllı şehirler, sağlık hizmetleri ve endüstriyel otomasyon gibi çeşitli alanlardaki pratik uygulamaları incelenmiştir. Bulgular, IoT ekosistemlerini optimize etmede ve verimli karar alma süreçleri sağlamada ölçeklenebilir, akıllı veri yönetimi stratejilerinin önemini vurgulamaktadır

Keywords

• IoT Veri Yönetimi
• Makine Öğrenimi
• Veri Sınıflandırma
• Rastgele Orman
• SMOTE
• Gerçek Dünya Uygulamaları

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