研究人员为无电池物联网（IoT）设备开发了新颖的硬件无关调度策略。这些方法，包括一个强化学习（RL）代理和一个近似预测（AP）方法，可以在没有事先能源信息的情况下管理不可预测的工作负载。使用真实太阳能数据和LoRa传输配置文件的评估显示了明显的权衡：AP提供高吞吐量，RL提供可调的平衡，而AsTAR在节奏控制方面表现出色。研究表明，这些先进的策略对于具有小能量缓冲区的系统至关重要，而较大的缓冲区可以使用更简单的静态策略。 AI
影响 这些方法可以使能源受限的物联网设备上实现更可靠和更复杂的应用。
排序理由 该集群包含一篇学术论文，详细介绍了管理无电池物联网设备任务执行的新颖方法。
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arXiv:2606.24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored e…
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy. As edge applications grow in complexity, …
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy. As edge applications grow in complexity, …