Modular IoT Ecosystems with Self-Learning Anomaly Detection Pipelines: Advancing Reliability in Distributed Cloud Systems

The proliferation of Internet of Things (IoT) devices has led to the creation of vast, heterogeneous data ecosystems integrated with distributed cloud systems. This complexity introduces significant challenges in maintaining system reliability, security, and performance due to the dynamic nature of failures and anomalies. Traditional monolithic monitoring and static rule-based detection systems are ill-suited for the scale, diversity, and evolution of these environments. This paper proposes a novel architectural framework that synergizes a modular IoT ecosystem with self-learning anomaly detection pipelines. Our approach decomposes the IoT-cloud continuum into discrete, interoperable modules for data ingestion, preprocessing, feature extraction, and analysis. At its core is a self-learning pipeline that employs ensemble machine learning techniques, including semi-supervised learning and incremental deep learning models, to continuously adapt to new data patterns and emerging anomaly types without requiring complete retraining. The pipeline features a feedback loop where detection outcomes are automatically evaluated and used to refine model parameters and decision thresholds. We validate our framework through a simulated smart city environment, demonstrating a 23.8% increase in anomaly detection precision and a 31.5% reduction in false positive rates compared to static benchmark models, while also improving system resilience to novel attack vectors and operational failures. This research contributes a scalable, maintainable blueprint for enhancing the reliability of next-generation distributed cloud systems underpinned by IoT data.