# Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches

arXiv: https://arxiv.org/abs/2510.00904 · HTML: https://mlsanity.com/paper/2510.00904/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control](https://mlsanity.com/paper/2601.18069/) (2026)

## Abstract (arXiv)

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.