Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911187800686592 |
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| author | Delfani, Erfan Pappas, Nikolaos |
| author_facet | Delfani, Erfan Pappas, Nikolaos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00904 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches Delfani, Erfan Pappas, Nikolaos Networking and Internet Architecture Information Theory 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. |
| title | Optimizing Version AoI in Energy-Harvesting IoT: Model-Based and Learning-Based Approaches |
| topic | Networking and Internet Architecture Information Theory |
| url | https://arxiv.org/abs/2510.00904 |