Introduction

We have prepared some FITA Demos that can be easily deployed in your Kubernetes cluster while providing interactive examples of FITA in action. Therefore, you can use these demos to experiment the main FITA features.

git clone https://github.com/fraunhoferportugal/fita.git

Before diving into the example demos, you should make sure that you have all the required frameworks and tools, which are listed in the requirements section.

Requirements

The following list present the main requirements for trying any Demo:

• Kubernetes Distribution
  • We developed and tested FITA demos using MicroK8s.
  • K3s, kind, and k0s are possible alternatives.
• FITA
• IoTNetEmu
• Docker Engine

Demos

After installing and preparing all the requirements, you are finally able to easily deploy and experiment any FITA Demo.

Here you can find a simple description for each available demo:

• Temperature Monitoring Demo
• Intelligent Traffic Control Demo

Temperature Monitoring Demo

This demonstrator showcases FITA by presenting a temperature monitoring setup using two (emulated) Far-Edge nodes built on top of a Kubernetes cluster. A custom component is deployed across the cluster to collect temperature data from the Far-Edge nodes. FITA is the core component of this demonstrator as it is responsible of enabling the connectivity to the Far-Edge devices and allowing the data collection. The goal of this demo is the simulation of a real-world monitoring scenario that collects environment metrics.

Try Demo 🚀

Intelligent Traffic Control Demo

This demonstrator showcases a traffic monitoring system on a Kubernetes cluster equipped with a device featuring a noise sensor and a camera. The captured video is transmitted to a web-based streaming application, making it easily accessible for real-time monitoring. Similarly to the Temperature Monitoring Demo, FITA enables a custom component to collect noise data and visualize it in Grafana dashboards. To enable traffic image processing, when the noise surpasses a predefined threshold, the system triggers an AI model that processes the collected images for object detection. In a real-world scenario, this approach increases the energy efficiency by limiting image processing to noisy situations while enabling real-time traffic monitoring.

Try Demo 🚀