The rapid expansion of the Internet of Things (IoT) has led to an unprecedented increase in data generation, necessitating efficient and scalable processing solutions. Traditional cloud-centric data analytics approaches face limitations such as high latency, bandwidth constraints, and potential security risks. This study explores the potential of distributed data analytic models for IoT edge computing networks, where data processing is performed closer to the source of data generation. By leveraging edge devices' computational capabilities, distributed models can reduce latency, enhance real-time data processing, and improve network efficiency. We present a framework for implementing distributed data analytics at the edge, highlighting key challenges such as resource management, model accuracy, and data security. Through simulations and real-world deployments, the proposed framework demonstrates significant improvements in performance, scalability, and data privacy. This research underscores the transformative potential of distributed analytics in optimizing IoT edge networks, paving the way for more intelligent, responsive, and resilient IoT systems.

IoT, edge computing, real-time data processing

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