IT Governance In Complex Systems: A Computational Framework For System Control And Reliability

Abstract

The increasing complexity of smart city infrastructures creates significant challenges for effective Information Technology (IT) governance, system reliability, and adaptive control. Smart city environments integrate heterogeneous components, such as Internet of Things (IoT) gadgets, communication networks, and cloud-based systems, with real-time analytics, thereby creating highly dynamic and interdependent systems. Conventional governance models often lack the computational intelligence and scalability required to manage large-scale, data-intensive environments. An Intelligent Control and Reliability-based IT Governance (ICR-IT Gov) model is proposed to enhance governance efficient with operational reliability in smart city ecosystems through the integration of data-driven intelligence and adaptive control mechanisms. Data collection is performed using distributed sources such as IoT sensors, network logs, and urban service platforms. Z-Score Normalization was employed for Data preprocessing to improve data quality and consistency. Independent Component Analysis (ICA) is employed for feature extraction to identify statistically independent patterns associated with anomalies and system reliability. The Modified Equilibrium Optimizer (ModEO) is utilized for feature selection and hyperparameter optimization, while Extreme Gradient Boosting (XGBoost) performs anomaly detection and risk prediction. The proposed model is implemented using the Python programming environment with machine learning and data analytics libraries for intelligent governance analysis and model optimization. A feedback-driven control mechanism enables dynamic policy adaptation and system stabilization. Experimental results demonstrate superior governance performance, achieving 96.8% prediction accuracy, 94.2% precision, and 96.7% recall, along with improved anomaly detection efficiency in smart city environments