Generative AI Proposed for Optimizing Wireless Power Transfer Scheduling in IoT Systems
A new preprint from arXiv proposes using generative artificial intelligence to help schedule radio frequency wireless power transfer (RF-WPT) in large-scale Internet of Things deployments. RF-WPT allows devices to receive power wirelessly, reducing battery replacement needs, but efficiently allocating limited charging resources under uncertain conditions remains a key challenge. The research argues that GenAI's ability to generate multiple plausible future scenarios—rather than single deterministic predictions—can lead to more robust and risk-aware charging decisions.
Researchers have submitted a preprint to arXiv proposing that generative AI (GenAI) serve as an uncertainty-aware decision-support layer for radio frequency wireless power transfer (RF-WPT) schedulers in IoT systems. RF-WPT is seen as a promising technology for eliminating the need for frequent battery replacements in large sensor and device networks, but scaling it up requires sophisticated scheduling: a transmitter must decide how much energy to send, to which devices, and when, all under incomplete information and fluctuating conditions. Rather than using GenAI as a standalone forecasting or control system, the authors position it as a tool that generates multiple plausible charging scenarios conditioned on available context, which are then fed into downstream optimization tasks. A warehouse-style simulation case study is presented, demonstrating that this scenario-sampling approach outperforms deterministic prediction methods and simple non-learning baselines, particularly when the objective is risk-sensitive. The paper also surveys how different families of generative models—such as diffusion models, variational autoencoders, and others—can each contribute to uncertainty-aware scheduling. The authors conclude by outlining open research challenges, including scalability, real-time inference constraints, and integration with existing network management systems.
What's missing
As a preprint, this work has not yet undergone peer review. The warehouse case study is simulated rather than validated on real-world RF-WPT hardware deployments, and the generalizability of results to diverse IoT environments remains untested.
What different sources said
- arXiv cs.LGCenter
Toward Proactive RF Charging Scheduling: Generative AI for Decision Support
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