Alexis DUQUE

This talk will present the result of our experimentation done at i.Lab Wireless Testbed in Ghent, in the context of FED4Fire+ H2020 project. Our project aims to collect raw radio frequency (RF) signals of widely used radio protocols for Internet of Things (IoT) devices in the 2.4GHz ISM bandwidth, such as Bluetooth Low Energy and LoRa, using software-defined radio (SDR). This will allow us collecting a large, reliable and reproducible dataset of RF fingerprint. This dataset will be further used to develop deep learning algorithms for IoT device fingerprinting and localization. Our use case is the authentication of autonomous vehicles or robots in a building according to their localization, without any over-the-air key exchange algorithm.

While Machine Learning is usually deployed in the cloud, lightweight versions of these algorithms that fit for constrained IoT systems such as microcontrollers are appearing. Using Machine Learning « at-the-edge » has indeed several advantages such as the reduction of network latency, it provides better privacy, and are working offline. In this presentation, we will demonstrate how to deploy Deep Learning algorithms on IoT devices thanks to TensorFlow Lite. We will see how to use it to design a smart vertical farming system able to predict and optimize the plant growth, at home or in developing countries where a reliable Internet connection still is missing.

In this talk I will show how trending technologies like IoT, Machine Learning and Tensorflow can make the world better :) I will discuss how we can use Tensorflow Lite on IoT and evaluate its performances and limits. I will explain our use case in vertical farming, show code snippets and make some short demo.

Summary:

• Vertical farming use case, how to use technologies to try stopping hunger in poor countries
• Our IoT system for data collection: data collection, electronics and embedded system
• Tensorflow Lite
• How we use Tensorflow Lite on RPI
• Short Demo
• Performance evaluation, limits, further work