TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform

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来源：

IEEE

全文链接：

//agri.nais.net.cn/topic/downloadFile/0c49afd5-a1b9-47a7-b807-e7d1019ab70f

来源地址：

https://ieeexplore.ieee.org/document/9829675

资源所属：

智慧农业发展战略专题

类型：

会议论文

语种：

英语

原文发布日期：

2022-07-05

摘要：

Smart agriculture researchers bring numerous tools and prospects to the farm ecosystem to improve its productivity and, mainly, its sustainability. Artificial Intelligence (AI) is widely used in precision agriculture as Internet of Things (IoT) technologies have brought a huge volume of data to exploit to provide useful insights for farmers such as weather prediction, pest development detection, or harvest time estimation. AI algorithms are mostly executed in the cloud due to their inherent computing constraints, thus requiring the different sensors to offload their data to the appropriate server. Depending on the amount and volume of data exchanged, the need for computer offloading may induce privacy, security, and latency issues in addition to weighting on the sensor’s battery consumption as wireless transmission methods have a high-energy demand. To overcome this difficulty, recent research has tried to bring AI computation closer to the end device with edge or fog computing and more recently with the Tiny Machine Learning (TinyML) paradigm that aims to embed the AI algorithm directly into the sensor’s microcontroller. In that context, this paper proposes a prototype of smart sensor capable of detecting fruits presence with TinyML. We then study the energy consumption of our system in different IoT scenarios.