Advanced Ensemble Learning Methods for High Volume IoT Data
Verma Hema · 2024
The proliferation of IOT devices has resulted in an unprecedented influx of high-volume and high-velocity data, necessitating advanced analytical methods to extract actionable insights. Ensemble learning, a powerful technique that combines multiple models to enhance predictive performance and robustness, has emerged as a critical approach for managing the complexities of IoT data. This chapter provides a comprehensive examination of advanced ensemble learning methods tailored to the unique challenges of IoT environments. Key topics include the fundamentals of ensemble learning, addressing scalability and real-time processing issues, and the application of error reduction techniques. The chapter also explores the integration of ensemble methods in diverse IoT applications such as energy management, predictive maintenance, and anomaly detection. By analyzing state-of-the-art techniques and identifying current research gaps, this chapter offers valuable insights into the evolving landscape of ensemble learning in IoT. The discussion includes advancements in hybrid and dynamic ensembles, meta-learning strategies, and future directions for leveraging these methodologies to improve accuracy, efficiency, and scalability in IoT systems.