Economic Decision-Making in Sensor Software Systems Using Predictive AI Approaches

In the current context of the rapid development of sensor-based software systems, economic decision-making has become essential for optimizing resources and reducing operational costs. This paper analyzes the applicability of AI-based predictive methods in supporting the economic decision-making process in sensor-based software systems. Using machine learning models, resource consumption can be forecasted, operational costs can be estimated, and system performance can be evaluated under different conditions. The main objective of the research is to facilitate informed decision-making regarding the configuration, scaling, and maintenance of software systems that manage sensor networks, in order to achieve increased economic efficiency. This study refers to a theoretical analysis which details the methodological principles, but also the design rationale underlying predictive AI models applied in economic decision-making. The research results highlight the benefits of applying AI to anticipate system behavior and reduce costs associated with overprovisioning, excessive energy consumption, or reactive maintenance. The conclusions of the paper highlight the real potential of artificial intelligence in transforming sensor-based software systems into more economically sustainable infrastructures, while providing a valuable tool for decision-makers in industrial, urban or research contexts.