Consumer Internet of Things (IoT) devices like smart speakers and connected thermostats, as well as Industrial Internet of Things (IIoT) equipment, which can be used to streamline manufacturing processes, are all powered by underlying IoT technology. Yet these IoT technologies are less understood than the examples of connected products and processes that are delivered to end users.
IoT technology comprises the array of hardware, software, and protocols that constitute the Internet of Things ecosystem. This includes sensors, actuators, communication protocols, as well as data storage, processing, analysis, and security technologies.
Dgtl Infra provides an in-depth overview of the various technology components that underpin Internet of Things (IoT) systems. By the end of this article, you will have insights into the most important technologies powering the Internet of Things (IoT) today, as well as the trends that will shape the future of IoT, such as edge computing, 5G, machine learning (ML), artificial intelligence (AI), and quantum computing, to name a few.
What is IoT Technology?
The term IoT technology is sometimes confusingly used as a synonym for IoT device or even just the Internet of Things (IoT) – but these terms are not the same.
When it comes to the Internet of Things (IoT), many different types of technology are combined to enable IoT devices to function. Furthermore, IoT devices are often interconnected to dozens or even thousands of other devices. In other words:
IoT technologies are the array of tools that underpin and power Internet of Things devices and networks, such as sensors, actuators, communication protocols, as well as data storage, processing, analysis, and security technologies.
Examples of important IoT technologies currently in use are:
- Sensors to measure and monitor a variety of factors like temperature, pressure, humidity, acceleration, gyroscopic forces, chemical substances, smoke, and infrared light
- Actuators and controllers, such as hydraulic / pneumatic actuators, solenoids, electric motors, microcontrollers, embedded systems – which enable IoT devices to take physical actions
- Communication technologies such as LTE, 5G, Zigbee, Z-Wave, LoRa (long range), NB-IoT, and Wi-Fi – which enable devices to receive and transmit data. Machine-to-machine (M2M) communication protocols are especially relevant in the context of IoT
- Gateway technologies allow inter-device and device-to-cloud communication, as well as data preprocessing and device management, in some cases
- Software and cloud technologies for the storage and analysis of data generated by IoT devices. Examples of IoT databases include InfluxDB, MongoDB, and SQLite for data storage, as well as an array of signal processing, machine learning, and artificial intelligence tools for data analysis and business intelligence
- Security technologies, such as endpoint encryption, context-aware authentication, and network segmentation, to protect IoT devices as well as the data they transmit and receive
Below, we examine the different types of IoT technologies and their role in present-day and future IoT applications.
IoT Sensor Technology
IoT sensors come in all shapes and sizes, and can be classified based on the following attributes, among others:
- Property to be Measured (also called Measurand): for example, mechanical, magnetic, thermal, or electrical sensors
- Energy Consumption: active and passive IoT sensors
- Output Signal: analog and digital IoT sensors
The most popular types of IoT sensors are motion sensors, temperature sensors, pressure sensors, proximity sensors, image sensors, and sound sensors. Each type of sensor has its own unique capabilities that make it valuable for different applications.
Examples of IoT Sensor Technology
Below are common examples of how IoT sensor technology is used in common applications:
- Motion Sensors: used for home security systems, automated doorbells, pet-related IoT devices, and Industrial Internet of Things (IIoT) applications – such as in manufacturing assembly lines
- Temperature Sensors: used in climate monitoring and process-control IoT devices
- Image Sensors: used for everything from drone guidance, to traffic monitoring, and automated quality inspections
General Motors – Sensors Example
General Motors (GM), which designs, builds, and sells cars and trucks worldwide, is delivering new experiences using IoT sensor technology in its modern fleet of vehicles. For example, GM’s Cadillac LYRIQ SUV has up to 15 cameras and more than 100 sensors on board. Recently, Scott Miller, a Vice President at General Motors, detailed how the company’s end-to-end software platform integrates with a vehicle’s on board sensors:
“Vehicle sensors, signals, and actuators can be accessed by application program interfaces, APIs, from the software platform and used to develop new features or to improve existing ones. For example, rain sensors and the windshield wipers can be accessed by API to activate traction control. Even better, exterior vehicle cameras can be used to provide real-time dash cam capabilities with content automatically uploaded to the cloud.”
IoT Actuator Technology
Actuators enable IoT devices to take physical action based on the signals or data that they measure or receive. Common types of actuators used in IoT devices include:
- Solenoid: these are electromechanical devices that use a magnetic field to create mechanical force. Solenoids are often used to control valves and switches
- Motor: commonly used in IoT devices to drive wheels, gears, or mechanical arms
- Piezoelectric Actuator: convert mechanical stress to an electrical output (or vice versa) and are used as upstream controllers for valves and pumps
- Pneumatic and Hydraulic Actuator: these devices make use of pressurized air or hydraulic fluids, and are used in IoT devices to power robotic arms or legs
Microcontroller and Embedded System Technology
Microcontrollers are circuits placed on single microchips to control connected devices and systems. Embedded systems also play an integral role in IoT, but these perform specific tasks within larger systems. Both microcontrollers and embedded systems are widely used in IoT applications owing to their small size, low cost, low power consumption, high reliability, and highly customizable properties.
For example, a microcontroller might be used to control a smart IoT thermostat, collecting temperature data from a sensor and adjusting the heating or cooling system accordingly. While an embedded system may be utilized to monitor the operation of a factory machine, collecting data on its performance and sending alerts if it detects any problems.
Industrial Internet of Things (IIoT) applications often have specific requirements for microcontrollers, such as high reliability, extended temperature range, and robust communication capabilities. Examples of the most popular microcontrollers for IIoT applications include:
- Atmel SAM3X: a 32-bit ARM Cortex-M3 microcontroller used in IIoT applications due to its high performance and low power consumption
- Freescale Kinetis: family of microcontrollers used in IIoT applications due to their robust communication capabilities and extended temperature range
- Intel Quark: family of microcontrollers designed for use in low-power, low-cost IoT applications. They are used in IIoT applications due to their low power consumption and small size
- Microchip PIC32: family of microcontrollers used in IIoT applications due to their low power consumption and wide range of features
- NXP LPC: family of microcontrollers used in IIoT applications due to their high performance, low power consumption, and extended temperature range
Signal and Data Processing in IoT
Signal and data processing is critical in IoT technology, as it allows IoT systems to interpret data from sensors and other sources. Specifically, signal processing algorithms use mathematical models to analyze incoming sensor information, enabling IoT systems to perform more efficiently. IoT signal processing technologies include digital signal processors (DSPs), field programmable gate arrays (FPGAs), always-on embedded processors, and neural networks.
Signal and data processing enables IoT systems to identify anomalies or outliers so that they can be addressed quicker, as compared to manual review processes. By combining analytics with machine learning algorithms, IoT systems can detect patterns from sensor readings which would otherwise go unnoticed by human operators. This allows IoT systems to process real-time input from various devices across their networks, enabling benefits that are not otherwise possible.
Machine-to-Machine (M2M) Communication Technology
Machine-to-machine (M2M) protocols enable communication between devices and systems across the different layers of an IoT network. M2M protocols facilitate communication by using specialized algorithms which ensure reliable performance in challenging environments, and even when multiple IoT devices are trying to communicate at the same time.
Notably, M2M technologies can be used to reduce power consumption when transmitting data wirelessly over IoT networks, making them more energy-efficient than traditional protocols.
Common Machine-to-Machine (M2M) protocols include:
- Modbus: widely-used protocol for communication between devices in industrial automation systems. Modbus is used to transmit data over serial communications or Ethernet networks
- OPC UA (Open Platform Communications Unified Architecture): a standardized M2M communication protocol that allows devices and systems from different vendors to communicate with each other. OPC UA is commonly used in IIoT applications to enable interoperability between different devices and systems
- Profinet: an Ethernet-based protocol that is commonly used in industrial automation systems. Profinet is designed to support real-time communication between devices and allows for the transmission of large amounts of data
- EtherCAT: an Ethernet-based protocol that is used in IIoT applications to enable real-time communication between devices. EtherCAT is designed to support high-speed communication and can handle large amounts of data
Machine Learning (ML) Technology in IoT
Machine learning (ML) enables algorithms to recognize patterns from large amounts of data without having to be explicitly programmed. IoT technologies leverage machine learning capabilities to obtain insights from the vast amounts of data generated by IoT devices and sensor networks, allowing for more efficient operations.
For instance, predictive maintenance uses IoT machine learning algorithms to analyze data obtained from industrial equipment, such as factory machines, enabling the detection of patterns that could indicate an impending failure. By proactively scheduling maintenance tasks, IoT can reduce downtime and improve system efficiency.
Energy management is another area where IoT machine learning technology can be used to great effect. IoT systems with machine learning capabilities allow homes to become smarter – appliances and thermostats can be hooked up to IoT networks, providing data which can then be analyzed by machine learning algorithms to optimize energy consumption and lower electricity bills.
In addition, IoT machine learning technology can help businesses make smarter decisions by providing real-time insights based on the data generated by their connected devices. For example, a retailer might use IoT sensors in their stores combined with machine learning algorithms and technology to analyze customer behavior to create better promotional campaigns or even predict what products customers will buy next.
Artificial Intelligence (AI) Technology in IoT
Artificial intelligence (AI) is a widely utilized technology in Internet of Things (IoT) devices that allows them to operate independently of human interaction. Instead of relying on individuals to input data or control IoT devices, artificial intelligence can be used to automate processes. Examples of artificial intelligence in IoT technology include:
IoT sensors and cameras can be used to gather data on traffic patterns, like vehicle speed and the number of vehicles on the road. This data is able to then be analyzed using artificial intelligence algorithms to identify areas of congestion and develop strategies for reducing them. Additionally, artificial intelligence algorithms can be used for more efficient routing of vehicles based on current traffic conditions.
IoT-enabled sensors and drones can collect a wealth of data on crop health and soil conditions, such as moisture levels, nutrient levels, pH (acidity) levels, and temperature readings – all of which can then be analyzed by artificial intelligence algorithms to optimize irrigation methods and fertilization practices. These practices help farmers increase yields while reducing costs associated with water usage and fertilizer application.
IoT technologies such as wearable sensors or smart pills are becoming increasingly popular in healthcare applications. By monitoring vital health metrics, such as heart rate or blood sugar level over time, these devices are capable of detecting anomalies in a patient’s health that may otherwise go unnoticed until it is too late. Through artificial intelligence algorithms trained on this data, doctors can anticipate potential health issues before they become more severe and take preventative measures to ensure optimal health outcomes for their patients.
Technology Shaping the Future of IoT
Aside from edge computing and artificial intelligence – which are experiencing rapid adoption and already shaping the IoT landscape today – a host of other emerging technologies promise to transform IoT ecosystems of the future. These IoT technologies include:
- 5G: widespread adoption of the next generation of wireless telecommunications standards, especially in IIoT contexts, promises to revolutionize manufacturing and smart cities. Specifically, 5G will result in significantly reduced latency, increased speed, the ability to connect to more devices simultaneously, added capacity (network throughput), and improvements in signaling efficiency
- Blockchain: decentralized, distributed ledgers can combine with IoT to maintain a record of all transactions or agreements that occur on a network. For example, blockchain-powered IoT systems is a new technology that can be used to verify and track products across a supply chain, and even automate certain processes through the use of smart contracts – software that digitally facilitates or enforces a rules-based agreement or terms between transacting parties
- Quantum Computing: expected to be exponentially more powerful than a classical computer. Quantum computing promises to reinvent real-time analytics, security, and machine learning – dramatically increasing the potential benefits of IoT