Lecture

Key Components in IoT and Benefits for Industry

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture explores the key components of the Internet of Things (IoT), including sensors, big data storage, computing platforms, analytics, and deep learning. It also delves into the potential long-term economic benefits of IoT, such as remote healthcare savings and energy efficiency, with examples of billions of connected devices and the revenue opportunities they present.

In MOOC

IoT Systems and Industrial Applications with Design Thinking

Internet of Things (IoT) and smart connected devices have radically changed the way our world works and how companies operate and create new businesses. Do you want to learn about IoT fundamentals? This is a course for you! In this course we provide an overview of the underlaying IoT technologies and its business applications. This course is an introduction to IoT and explains why it is a continuously evolving concept with many industrial applications. You will learn the fundamental building blocks of IoT

Official source

Related concepts (17)

Internet of things

The Internet of things (IoT) describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. The Internet of things encompasses electronics, communication and computer science engineering. Internet of things has been considered a misnomer because devices do not need to be connected to the public internet, they only need to be connected to a network, and be individually addressable.

Connected car

A connected car is a car that can communicate bidirectionally with other systems outside of the car (LAN). This allows the car to share internet access, and hence data, with other devices both inside and outside the vehicle. For safety-critical applications, it is anticipated that cars will also be connected using dedicated short-range communications (DSRC) or cellular radios, operating in the FCC-granted 5.9 GHz band with very low latency. Both the U.S. and EU scenarios concentrate on 5.

Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.

Smart device

A smart device is an electronic device, generally connected to other devices or networks via different wireless protocols (such as Bluetooth, Zigbee, near-field communication, Wi-Fi, LiFi, or 5G) that can operate to some extent interactively and autonomously. Several notable types of smart devices are smartphones, smart speakers, smart cars, smart thermostats, smart doorbells, smart locks, smart refrigerators, phablets and tablets, smartwatches, smart bands, smart keychains, smart glasses, and many others.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Related lectures (64)

Artificial Intelligence: The New Radical Addition to Work

Explores the convergence of IoT, Big Data, and Deep Learning in Artificial Intelligence applications across various industries.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Internet of Things: Evolved Workplace

Delves into the impact of IoT on the workplace, discussing its origin, system architectures, future applications, and economic benefits.

Self-supervised Learning for Autonomous Vehicles