La evaluación de la asignatura se lleva a cabo por medio de un proyecto. Los proyectos tienen que tener un carácter eminentemente práctico. A continuación podeis encontrar un listado de posibles proyectos. Podéis tambien hacer vosotros una propuesta de proyecto; de todas formas antes de comenzar cualquier actividad tenéis que contactar con un profesor de la asignatura. En el caso de seleccionar proyectos de la lista, enviar correo con tres propuestas ordenadas por prioridad. Teneis que seleccionar el proyecto a realizar antes del 5 de Abril de 2022. Entrega De cada proyecto hay que entregar, utilizando la Tarea Poliformat "Proyecto Seminario IoT", y antes del 6 de mayo de 2022, lo siguiente: código fuente desarrollado.

:::info The code of this section is here  ::: Edge impulse Edge Impulse is the leading development platform for machine learning on edge devices. It is for developers but also used by enterprises. It allows to cover all the phases in a ML base IoT project, from training to deployment: Currently it can handle the following hardware:

Based on: https://github.com/tinyMLx/courseware https://blog.tensorflow.org/2019/11/how-to-get-started-with-machine.html :::info The code of this section is here  ::: Introduction

Based on: https://docs.docker.com/network/ https://training.play-with-docker.com/docker-networking-hol/ :::warning Create your own Docker ID at https://hub.docker.com/signup and… Option 1: You can download and install Docker on multiple platforms. Refer to the following link: https://docs.docker.com/get-docker/ and choose the best installation path for you.

based on https://training.play-with-docker.com/ops-s1-images/ :::info The code of this section is here  If you are running Docker online: https://labs.play-with-docker.com/ you can upload files in the session terminal by dragging over it. ::: Basic steps

Based on: https://github.com/docker/labs/blob/master/swarm-mode/beginner-tutorial/README.md https://docs.docker.com/engine/swarm/ Check out the documentation on Docker Swarm Mode for more information. :::warning Create your own Docker ID at https://hub.docker.com/signup and… Since this example would require various machine it can be done online using: https://labs.play-with-docker.com/

:::warning Create your own Docker ID at https://hub.docker.com/signup and… Option 1: You can download and install Docker on multiple platforms. Refer to the following link: https://docs.docker.com/get-docker/ and choose the best installation path for you. Option 2: You can use a virtual machine that you can download here You have to use VirtualBox with the option: "Files / Import virtualized service". The VM has user "docker" with password "docker"

:::info You can execute the code either in your computer or online: online. Create an account in https://repl.it or in your computer. You must have python3 installed and the paho-mqtt library: $ sudo pip3 install paho-mqtt

based on https://training.play-with-docker.com/dev-stage1/ https://training.play-with-docker.com/ops-s1-hello/ :::warning Create your own Docker ID at https://hub.docker.com/signup and… Option 1: You can download and install Docker on multiple platforms. Refer to the following link: https://docs.docker.com/get-docker/ and choose the best installation path for you.

:copyright: https://codelabs.developers.google.com/codelabs/openthread-simulation/#0 Introduction OpenThread released by Google is an open-source implementation of the Thread networking protocol. Google Nest has released OpenThread to make the technology used in Nest products broadly available to developers to accelerate the development of products for the connected home. The Thread specification defines an IPv6-based reliable, secure and low-power wireless device-to-device communication protocol for home applications. OpenThread implements all Thread networking layers including IPv6, 6LoWPAN, IEEE 802.15.4 with MAC security, Mesh Link Establishment, and Mesh Routing. To understand the terms: mesh, Thread, and OpenThread you have to first watch these videos: in this first video (duration 5:14) Mesh Networking is explained

Based on: https://github.com/docker/labs/blob/master/swarm-mode/beginner-tutorial/README.md https://docs.docker.com/engine/swarm/ Check out the documentation on Docker Swarm Mode for more information. What is a Docker Swarm? Docker swarm is a container orchestration tool, meaning that it allows the user to manage multiple containers deployed across multiple host machines. A swarm consists of multiple Docker hosts which run in swarm mode and act as managers (to manage membership and delegation) and workers (which run swarm services). A given Docker host can be a manager, a worker, or perform both roles. When you create a service, you define its optimal state (number of replicas, network and storage resources available to it, ports the service exposes to the outside world, and more). Docker works to maintain that desired state. For instance, if a worker node becomes unavailable, Docker schedules that node’s tasks on other nodes. A task is a running container which is part of a swarm service and managed by a swarm manager, as opposed to a standalone container.

Based on: https://docs.docker.com/network/ https://training.play-with-docker.com/docker-networking-hol/ Networking overview One of the reasons Docker containers and services are so powerful is that you can connect them together, or connect them to non-Docker workloads. Docker containers and services do not even need to be aware that they are deployed on Docker, or whether their peers are also Docker workloads or not. Whether your Docker hosts run Linux, Windows, or a mix of the two, you can use Docker to manage them in a platform-agnostic way. This section introduces some basic Docker networking concepts and prepares you to design and deploy your applications to take full advantage of these capabilities.

Introduction to the TIG Stack The TIG Stack is an acronym for a platform of open source tools built to make collection, storage, graphing, and alerting on time series data easy. A time series is simply any set of values with a timestamp where time is a meaningful component of the data. The classic real world example of a time series is stock currency exchange price data.  Some widely used tools are: Telegraf is a metrics collection agent. Use it to collect and send metrics to InfluxDB. Telegraf’s plugin architecture supports collection of metrics from 100+ popular services right out of the box.

Based on https://github.com/tinyMLx/courseware Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("Public License")(C) and https://towardsdatascience.com/exploring-ia-at-the-edge-b30a550456db Baisc reference books: The reference book on TinyML. , It has good introductions about ML and TF. The TinyML book is clearly written for people who already understand TF well enough.

:::info You can execute the code either in your computer or online: online. Create an account in https://repl.it in your computer. You must have python3 installed and the paho-mqtt library: $ sudo pip3 install paho-mqtt

based on https://training.play-with-docker.com/dev-stage1/ https://training.play-with-docker.com/ops-s1-hello/ :::danger Option 1: You can download and install Docker on multiple platforms. Refer to the following link: https://docs.docker.com/get-docker/ and choose the best installation path for you. Option 2: