What Is the Internet of Things (IoT)?

What Is the Internet of Things (IoT)?

The Internet of Things, or IoT, is a network of physical objects that contain sensors, software and other features allowing hardware to connect to the internet and exchange data with online systems and other connected devices.

The objects connected to the IoT can range from simple household appliances to extremely complex industrial machinery. There are roughly 18 billion devices connected to the IoT worldwide, according to Ericsson. Many of these devices allow users to access remote functionality in real time. Connected devices have been around for decades, but 5G wireless networks and advances in semiconductor chips have opened the door for rapid expansion of the IoT in the coming years.

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The IoT operates on three layers.

The physical layer of the IoT is the hardware used to connect a device to the internet, such as sensors. The physical layer is also called the perception layer because it is the layer that gathers information from the physical world or identifies other connected devices in the environment.

The next layer of the IoT is the network layer, which is responsible for connecting a device to other smart objects, servers or network devices and transmitting the data collected.

Finally, the IoT application layer provides services to users. This layer includes protocols and interfaces connected devices use to communicate with each other and their users.

IoT users also utilize remotes, devices such as smartphones, tablets, PCs and smartwatches, used to control IoT devices via a dashboard or other display.

Data collected from each device in an IoT network can be aggregated to help improve the performance of all the devices on the network. Insights gained from advanced IoT analytics can help make processes more efficient or even entirely automated. IoT-enabled automation is particularly useful when the automated tasks are repetitive, time-consuming or dangerous.

There are several different types of companies that profit from the IoT, including companies that make the chips and sensors inside connected devices, companies that produce the devices themselves and companies that create the software that makes the IoT functional.

  • Amazon.com Inc.’s (ticker: AMZN) Alexa virtual assistant technology and Echo smart speakers allow users to connect and interact with smart home devices easily via voice prompts. 

  • Best Buy Co. Inc. (BBY) is the market share leader in smart home appliance sales, including smart speakers, security cameras and thermostats.

  • Intel Corp. (INTC) designs and sells semiconductor chips used to power the cloud data centers and networks that link IoT devices.

  • Cisco Systems Inc. (CSCO) produces networking hardware that serves as the backbone of the IoT.

  • The Microsoft Corp. (MSFT) Azure IoT is a collection of Microsoft-managed cloud services that connect, monitor and control billions of IoT devices.

The IoT has a large and rapidly growing list of applications:

  • Virtual glasses, fitness bands, smartwatches and other wearable devices collect and process data such as a user’s heart rate, distance traveled and calories burned.

  • Connected wearables and sensors can help medical professionals monitor patients and even treat them remotely. 

  • Connected home appliances, such as light switches, security alarms and thermostats, allow smart homeowners to access, control and monitor their homes from their smartphones.

  • Autonomous vehicles produced by Alphabet Inc. (GOOG, GOOGL) and General Motors Co. (GM) carry passengers and cargo on public roads, navigating based on data collected from sensors installed throughout the car.

  • Utility companies utilize smart grid technology to improve energy efficiency by monitoring energy usage, managing energy shortages and gathering data on how individuals and companies use energy.

  • The industrial sector of the economy has been particularly proactive in building connected factories and plants, technology collectively known as the Industrial Internet of Things, or IIoT. Companies gather data on industrial processes from connected machinery in smart factories and use that data to make their production safer, more efficient and more automated.

The IoT is constantly generating massive quantities of data, so a growing number of companies are leveraging artificial intelligence to process that data and gain insights about IoT processes.

Machine learning is an application of AI technology that allows a connected device to automatically identify patterns or anomalies in data and use what it learns to improve on its own without human intervention. Machine learning technology can significantly improve equipment maintenance, help predict process outcomes and automatically adjust procedures based on information such as temperature, vibration or pressure. Machine learning can also help companies avoid unplanned shutdowns, increase their outputs and improve risk management.

Whether consumers like it or not, the future of appliances, vehicles, homes and even entire cities appears to be centered on the IoT. Connected devices can allow humans to make better decisions, giving them insights based on large quantities of data.

The IoT also allows people and businesses to track and monitor devices and processes at an unprecedented level, whether it be watching a home security camera on your smartphone or automatically checking the quality of the goods you are producing. IoT automation frees up precious time for users and employees, allowing them to devote more time and energy to tasks that involve higher-level thinking and problem-solving.

  • Efficiency. IoT automation can make businesses more efficient, helping monitor machinery parts, production quality or even environmental conditions. The IIoT can also automate processes that might otherwise be dangerous for human workers to perform. On an individual level, connected devices can help people save time and effort at home and throughout the day, automatically performing menial tasks such as locking doors and turning off lights.
  • Environmental friendliness. The IoT can also help with conservation efforts, optimizing electricity, water and other resource usage. 
  • Knowledge creation. The IoT collects large quantities of data that can help engineers, architects and scientists understand the world better. 

  • Security concerns. The IoT has raised concerns among skeptics about the risks of large-scale data collection and potential issues with tracking, privacy or security. Connected devices are exposed to cyberattacks in ways that non-connected devices are not. 
  • Complexity. Connected devices require an increasing amount of bandwidth, which can potentially slow down the performance of weaker networks. In addition, connecting simple appliances such as light fixtures, ovens and washing machines to the IoT can sometimes complicate an otherwise relatively simple task if the software or hardware isn’t functioning properly or loses its connection to the internet.

Before the 21st century, computer chips were too large and the internet was too limited to make connected devices worthwhile in most instances. However, in the early 1980s, graduate students in Carnegie Mellon University’s computer science department added light sensors to a campus vending machine to remotely track the soda levels in the machine.

Consumer sensor expert Kevin Ashton reportedly coined the term “Internet of Things” in 1999 to describe objects with networked sensors that could sense things for themselves.

LG unveiled the world’s first “digital, Web-enabled” smart refrigerator in 2000. In addition, the launch of the first Apple Inc. (AAPL) iPhone in 2007 and the subsequent rise of smartphones was a major watershed moment for the IoT as well.


A smart city is a city built to incorporate IoT devices and sensors into its infrastructure, such as its lights, meters and power grid.

Edge computing is the process of collecting and processing data at or near where it is collected rather than sending it to a data center or cloud application.

Artificial intelligence is the ability of a computer system to mimic human cognition, whereas machine learning is an application of AI that allows a computer to learn without direct instruction from a human.