Paper Title
Challenges in Statistical Validation of Iot Machine Learning Models: A Methodological Study

Abstract
The Internet of Things (IoT) has led to an exponential increase in devices that can generate and transmit data. This data is increasingly being used to train machine learning models for various prediction and decision-making tasks. However, the statistical validation of these IoT machine learning models presents several unique challenges that need to be addressed. This paper reviews the key challenges in validating IoT machine learning models, specifically focusing on data quality, data distribution shifts, concept drift, explainability, and edge deployment constraints. A detailed methodological framework is presented to tackle these challenges which includes data pre-processing, advanced validation schema, controlled simulations, explainable AI techniques and optimized edge deployment strategies. The paper concludes with a discussion on open research problems and future directions in this emerging field. Keywords - Internet of Things; Machine Learning; Statistical Validation; Data Quality; Concept Drift