Assessing the Complexity and Real-Time Performance of Anomaly Detection Algorithms in Resource-Constrained Environments

In the context of Industrial Internet of Things, the deployment of AI-based anomaly detection algorithms in resource-constrained environments is critical. This paper systematically evaluates the complexity and real-time performance of widely used machine learning models-Random Forests, Support Vector Machines, Linear Regression, XGBoost, Neural Networks, and Autoencoders. Through comprehensive theoretical analysis and empirical measurements on four diverse datasets, we identify the strengths and limitations of each model in terms of computational demands and practical feasibility. Our findings highlight that while complex models like Neural Networks and Autoencoders offer high accuracy on time series datasets, their deployment on edge devices necessitates significant optimization for real-time performance. In contrast, simpler models, though less resource-intensive, may fall short in critical applications. This study provides a decision matrix and structured evaluation framework to guide the selection of efficient anomaly detection models tailored to specific operational constraints. These contributions aim to assist developers and system administrators in achieving balanced AI deployments that optimize performance, security, explainability, and resource consumption.