Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches
Abstract
Efficient data transmission in resource-constrained Internet of Things (IoT) systems requires semantics-aware management that maximizes the delivery of timely and informative data. This paper investigates the optimization of the semantic metric Version Age of Information (VAoI) in a status update system comprising an energy-harvesting (EH) sensor and a destination monitoring node. We consider three levels of knowledge about the system model -- fully known, partially known, and unknown -- and propose corresponding optimization strategies: model-based, estimation-based, and model-free methods. By employing Markov Decision Process (MDP) and Reinforcement Learning (RL) frameworks, we analyze performance trade-offs under varying degrees of model information. Our findings provide guidance for designing efficient and adaptive semantics-aware policies in both known and unknown IoT environments.
Keywords
Cite
@article{arxiv.2510.00904,
title = {Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches},
author = {Erfan Delfani and Nikolaos Pappas},
journal= {arXiv preprint arXiv:2510.00904},
year = {2025}
}