arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1902.02850cs.NI
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使用深度Q学习延长相关物联网设备的使用寿命

Using Deep Q-learning To Prolong the Lifetime of Correlated Internet of Things Devices

Jernej Hribar, Andrei Marinescu, George A. Ropokis, Luiz A. DaSilva

摘要:

部署在物联网(IoT)中的电池供电传感器需要节能解决方案来延长其使用寿命。当这些传感器观测到一个在空间中分布并随时间演变的物理现象时,所收集的观测结果预计是相关的。我们利用所展示的相关性,并提出了一种采用深度Q学习的更新机制。我们的机制能够确定传感器应该传输其更新的频率,同时考虑到不断变化的环境。我们使用在实际部署中获得的观察结果来评估我们的解决方案,并表明我们提出的机制能够显着延长电池供电的传感器的寿命,而不会影响提供给物联网服务的观察结果的准确性。

英文摘要:

Battery-powered sensors deployed in the Internet of Things (IoT) require energy-efficient solutions to prolong their lifetime. When these sensors observe a physical phenomenon distributed in space and evolving in time, the collected observations are expected to be correlated. We take advantage of the exhibited correlation and propose an updating mechanism that employs deep Q-learning. Our mechanism is capable of determining the frequency with which sensors should transmit their updates while taking into the consideration an ever-changing environment. We evaluate our solution using observations obtained in a real deployment, and show that our proposed mechanism is capable of significantly extending battery-powered sensors' lifetime without compromising the accuracy of the observations provided to the IoT service.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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