Localization Accuracy Under Age of Information Influence for Industrial IoT

Providing fundamental support for the industrial Internet of Things (IIoT), location awareness plays a significant role in integrated sensing and communication (ISAC). This paper introduces an unusual perspective that assesses the localization accuracy for a mobile target, taking into consideration the timeliness of location estimates based on the age of information (AoI). We propose a novel metric, aging error of localization (AEoL), to evaluate how the accuracy of positioning changes during the lifespan of data packets. By bridging a research gap that has predominantly focused on geographical precision alone, our metric offers valuable insights into the dynamic nature of spatial characteristics for moving targets. In this paper, we specifically examine AEoL in the context of a perfect localization algorithm. By incorporating the uniform rectilinear motion (URM) for an automated guided vehicle (AGV) and considering various queuing models for transmitting radio signals, we derive closed-form expressions to determine the time-average AEoL under different update rates. Through analytical derivations and numerical simulations, we validate the accuracy of our findings and demonstrate the practical implications of the AEoL metric.