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A glossary of the IoT terminology you must know

Tech professionals should learn this IoT terminology to start their journey and join emerging discussions with the same knowledge base.

IoT, like any specialized subject, has its fill of jargon and acronyms that everyone must understand before they can dig deeper into its nuances.

The easiest way to explain IoT starts with everyday examples, including smart thermostats or apps that allow you to turn off lights without the physical flip of a switch. These smart or connected devices represent the basic parts of IoT; technologists have so much more to learn if they want to implement IoT into their organizations. IoT deployments incorporate many other technologies, such as AI, cloud and edge computing, connectivity and security.

10 beginner terms to understand IoT in context

Start with the basic IoT terminology to discover important components of IoT and how IoT fits in with other technology trends.

Internet of things. IoT is a system of sensors or devices that collects and exchanges data through a network connection without any human involvement. IoT can monitor the state of a machine, the environment or even a person -- in other words, the "things" in the internet of things. IoT derives value from the analysis of the data it generates when admins apply the technology to increase efficiency or better understand processes.

IoT devices. Sometimes referred to as smart or connected devices, these include any device or sensor with computing capabilities that connects to the internet but is not traditionally internet enabled. For example, a smart thermostat could adjust the temperature of a meeting room based on the number of people in the room. Broadly, smart sensors take input from their surroundings and act with predefined functions when specific conditions are met before the data is sent from the device. Traditional computing devices, such as computers, smartphones or tablets, do not fall under the realm of IoT.

Industrial IoT. IIoT narrows down the focus of IoT to just the sensors and actuators used in a manufacturing or industrial process. IIoT often refers to using data from machines that were not previously connected for real-time analytics that promote more efficient business actions. Use cases of IIoT include predictive maintenance, supply chain traceability and asset tracking. The architectural components of IoT and IIoT often are similar, but organizations use each for different purposes. IoT covers connected device applications more broadly in many verticals, whereas IIoT connects devices in manufacturing and utilities that deal with more critical processes than general IoT.

Industrial IoT applications

Fourth industrial revolution. Technologists might hear other professionals refer to IoT as part of the fourth industrial revolution, or Industry 4.0. The idea behind it is that emerging technology trends -- including IoT, virtual reality and AI -- transform the way people live and work, similar to how previous industrial revolutions brought disruptive industrial, technological and digital advancements.

Digital transformation. Some organizations might talk about IoT as the catalyst for digital transformation. This evolution goes beyond just adopting IoT or another emerging technology and requires organizations to rethink and reinvent how they operate at fundamental levels. Organizations must examine and restructure their business strategy and culture to solve problems and optimize processes by turning manual tasks into digital ones.

Big data. IoT devices can generate massive amounts of data that are useful for machine learning, predictive modeling and analytics. From the data, organizations can identify patterns and trends to inform their business decisions. IoT data requires the infrastructure to better manage, store and analyze a large volume of structured and unstructured data in real time. Organizations use big data to give them a competitive advantage in marketing their products or services, improve their processes and increase customer satisfaction.

IoT analytics. IoT data is a subset of big data. IoT's value comes from what organizations do with the data devices generate. Organizations use analytics tools to distill actionable information from the plethora of IoT data. IoT analytics tools use sensor data from manufacturing equipment, smart meters or delivery vehicles for reporting and analysis. IoT analytics presents organizations with challenges that stem from the different types of data, the amount of data and the speed required for data processing.

IoT edge computing. Edge computing is the architecture IoT uses to process the data close to its generation point, particularly when data processing is time sensitive. Edge computing addresses the problem of sending vast amounts of data over the network, and processes data on the device itself or at a server close to it. Instead of processing all data for further use, edge computing means a smart security camera can process and only transmit data from the device when it detects movement, which lessens the load on the network. Any data that does not require real-time analysis can be analyzed in the cloud.

Edge computing applications

IoT cloud computing. Organizations can use cloud computing services to provide fully managed scalable compute resources for IoT deployments. These services come in the form of infrastructure, platforms or SaaS. Information storage in the cloud gives organizations a backup for disaster recovery and can save them the cost of building the necessary infrastructure in-house.

IoT security. The IoT industry has not always prioritized security measures during development, which has left connected devices with vulnerabilities to mitigate. Many methods that secure businesses also secure IoT devices, such as firewalls, but connecting devices to the internet presents some unique problems. IoT devices typically exist at the edge of an organization's network and greatly increase the potential attack surface. Manufacturers develop IoT devices to be small, but this limits the resources each device contains, including the space for greater security measures, such as advanced encryption. IoT organizations also lack agreement on industry-accepted standards, which means organizations have many frameworks to choose from without clear guidelines. Organizations may end up with devices that use different protocols, which makes it difficult to secure systems and ensure interoperability. Groups, such as the National Institute of Standards, are working to develop industry standards, but these efforts are still in progress.

Dive deeper with intermediate IoT terminology

With a basic understanding of the IoT industry and how IoT fits into organizational strategies, IT administrators can now move on to divisions within IoT technology and terms about how IoT works.

Machine-to-machine. M2M classifies any technology, such as AI, that connected devices use to transfer information or act without human interaction. Even though IoT is built off M2M capabilities, they are not the same. M2M, as the name implies, encompasses machines communicating with machines in a closed hardware system. IoT sensors connect devices to a larger network of machines that communicate with other machines and humans, and humans that communicate with machines. M2M technology is core to IoT for use in real time, remote monitoring of patients or equipment and improving process efficiency. The technology has low power consumption, the ability to continually or selectively send and receive data and location-specific triggers for alert devices.

M2M vs. IoT

Internet of medical things. IoMT, also known as healthcare IoT, covers medical devices and applications that connect to healthcare networks to analyze data and improve patient care. The most prevalent uses of IoT in healthcare facilities include remote patient monitoring and asset tracking sensors. IoMT comes with increased security risks to patient safety. As more devices are introduced to networks, the larger the attack surface becomes, and hackers have more opportunities to access patient data or devices.

Artificial intelligence of things. AIoT refers to uses where AI combines with IoT to increase operational efficiency and improve data processing for better informed decisions. With AI, IoT devices can make decisions without the need for human intervention. If an IoT device is programmed to monitor the temperature of a machine, AIoT software can register when the temperature goes above or below a desired range and make any adjustments.

IT/OT convergence. The nature of IoT technology requires IT and operations technology teams to work together on software, hardware, control systems and networks to succeed. The two teams' convergence can cause some conflict in organizations because of different priorities and perspectives. IT teams focus on the data created by IoT sensors and security. Operational technology (OT) teams must incorporate the new technology from a practical standpoint and ensure production goes smoothly. Organizations must define the IT and OT roles and responsibilities, outline where IT and OT overlap and provide the teams with the right tools to support the convergence.

Facilitate IT/OT convergence

IoT attack surface. IT administrators face a challenge when it comes to IoT security because the spread of devices in remote locations expands the attack surface, the total potential vulnerabilities in IoT devices, software and networks. Every IoT device connects to a network and has its own IP address, which gives hackers many opportunities to gain access.

IoT platforms. Organizations may use many different types of IoT platforms -- hardware or software that host an application or service -- to manage any aspect of IoT, including devices, data, analytics and security. Platforms simplify the challenges of IoT deployment management and scalability. Engineers can design their own IoT platforms to manage devices, but many vendors offer IoT platforms as a service, including AWS, Google and Microsoft.

Fog computing. Some IT professionals use the terms fog and edge computing interchangeably, but there are differences between the concepts. Fog computing is the data, compute, storage and applications present between the cloud and the devices that create data. Some organizations use edge computing to strictly mean the compute that happens within network endpoints, absent of any network connections. The data that exists outside of an edge device, but not yet in the cloud is fog. Fog computing reduces the bandwidth, latency and amount of data sent to the cloud.

Fog computing

IoT gateway. An IoT gateway device or software program preprocesses all data between IoT devices and the cloud. The gateway aggregates and analyzes the edge data to reduce the volume transferred through the network, which reduces the latency and bandwidth. Without this device, organizations would spend much more on data costs in the cloud. Gateways also increase security for the data moving to and from the cloud through encryption and tamper detection.

IoT gateway

IoT OS. IoT devices require specific technology to transfer data over the internet because of their small size and frequent use in remote locations. IoT developers must design IoT OSes specifically to fit the constraints on size, power and processing capacity. IoT OSes include Ubuntu Core, RIOT, Zephyr, TinyOS and Windows for IoT.

IoT network protocols. Protocols are the rules applied to data format, transmission and receipt for devices to communicate between servers, routers and endpoints, even if the IoT devices use different standards or designs. Examples of IoT protocols include Message Queuing Telemetry Transport (MQTT), Wi-Fi, Bluetooth, Zigbee, LoRaWAN and Z-Wave.

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IoT devices integrated with mobile applications will help in the process of tracking material flow, out of stock or overstock scenarios in real-time and improve inventory management says, 

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