Generative AI Proposed for Optimizing Wireless Power Transfer Scheduling in IoT Systems
Researchers propose using generative AI to improve scheduling decisions for radio frequency wireless power transfer (RF-WPT) systems that charge IoT devices without batteries. The approach treats GenAI as a decision-support tool that generates multiple plausible charging scenarios to handle uncertainty and incomplete information. This matters because efficient RF-WPT scheduling could enable large-scale IoT deployment while reducing battery waste.
A new arXiv paper presents generative artificial intelligence as a support layer for RF-WPT scheduling, addressing a key challenge in large-scale IoT deployment: deciding how much energy to deliver, when, and to whom under resource constraints and uncertain conditions. Rather than using GenAI as a standalone forecasting tool, the researchers propose it generate scenario-based inputs that preserve uncertainty for downstream decision-making. Their warehouse case study demonstrates that this uncertainty-aware approach improves charging decisions compared to deterministic prediction and non-learning baselines, particularly for risk-sensitive objectives. The paper identifies this as a promising direction for supporting uninterrupted IoT communications while mitigating battery replacement needs and environmental waste.
What's missing
The paper does not provide details on computational overhead, real-world deployment feasibility, or comparison with other uncertainty quantification methods beyond deterministic baselines. The warehouse case study scope and generalizability to other RF-WPT environments are not fully specified.
What different sources said
- arXiv cs.LGCenter
Toward Proactive RF Charging Scheduling: Generative AI for Decision Support
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