Machine Learning Misclassification Within Status Update Optimization

This paper explores the optimization of status updates in sensing systems, focusing on misclassification in machine learning (ML) models. Previous research has primarily tackled the impact of different techniques throughout the communication layers on Age of Information (AoI), or alternatively studied the Age of Incorrect Information (AoII) as a flaw that can be counteracted by a more active transmission pattern. Our study presents analytical considerations, as well as simulation results from real datasets, with the original aspect that classification errors are not an externality, but are triggered by a fraction of the status updates themselves, which therefore ought to be kept under control. An excessively high number of transmission may be damaging the system, and the right balance needs to be found between prompt updating that lowers AoI, and accuracy to minimize AoII at the same time. In this sense, we offer a new standpoint for timely status update, where freshness of correct information is required for smart systems to make the best decision in real-time.