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After being interviewed for my colleague Sarah’s latest post on why the term “Internet of Things” hasn’t taken off quite yet in popular culture, I started to think about ways that we here at Temboo could help educate the public about the concept.

According to Google Trends, among the top 10 related queries to searches for “Internet of Things” are:

  • what is internet of things
  • what is the internet of things
  • internet of things definition
  • definition of internet of things

So, yeah, it does seem as though people are still in need of a refresher on the key concepts and terms related to the Internet of Things.

That’s why I decided to create this IoT definition glossary to provide a brief overview on things like fog computing, AI, and preventive maintenance.

Below are some of the main terms that I think you should know if you are looking to learn more about IoT. If you have other terms to suggest adding to this list, shoot us a message at hey@temboo.com and I’ll look into updating this post.

The Complete IoT Glossary

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5G: The fifth generation of mobile technologies designed for a future where many physical elements of the world are connected to the internet. It will be much faster than its predecessor (4G), and is projected to spur growth in the IoT industry especially.

For further reading on 5G, check out our post “Open Opportunities to Shape the Future of 5G & IoT Right Now.

Image via projectmaterials.com

Actuators: Components that are responsible for moving or controlling a mechanism or a system. They require a source of energy which can be an electric voltage or signal triggered by an electrical system. There are many types of actuators that can do things like control pumps, lights, motors, valves, and many other things.

Additive manufacturing: Another term for 3D printing. The process builds successive layers of a material into a three-dimensional item guided by a CAD model.

Artificial Intelligence (AI): A genre of computer science in which non-human machines demonstrate some form of intelligence. You can think of this as computers being able to take actions to achieve a goal based on their perceived environment.

For further reading on AI, check out our post “We Analyzed IoT, AI, and VR to See Which is the Most Mainstream. Here’s What We Learned.

AIoT: A term used to describe AI in conjunction with the IoT. This is often used when thinking about IoT in an analytics and data collection construct.

Anomaly detection: The identification of outliers or unexpected items or events in data sets. For example, when your bank sees suspicious activity on your account they are detecting an anomaly. It is identifiable because it is different that your previous spending activity. Machine learning algorithms are often trained to do this.

APIs (Application Programming Interfaces): Tools for building applications that perform commonly desired actions by using methods that already exist. For example, the Google Maps API allows anyone to embed high quality, digital maps on a webpage. You can think about these as building blocks that allow you to accomplish tasks without needing to build complex services from the ground up.

Automation: Simply put, this is the process of performing a procedure or action without human assistance. It’s often referred to in an industrial context via control systems for machinery or processes in factories, but also covers applications like setting your household thermostat to turn on at a certain time or when the temperature breaches a set threshold.

Big data: Deals with data sets that are too large or complex to be used with traditional data processing applications software. This field tries to extract information, analyze, or find meaning from these large data sets.

Bluetooth: Wireless technology standard that exchanges data between devices over short distances via radio waves.

For further reading on Bluetooth, check out our post “Everything You Need to Know About Bluetooth Mesh for Industrial IoT“.

Bluetooth Low Energy (BLE): Bluetooth technology that consumes less power than traditional Bluetooth but within a similar communication range.

Cloud computing: Also referred to colloquially as “the cloud”, this term describes computer software, systems, and services that run on the internet without direct management by a user, rather than on a computer. Systems in the cloud appear to be running on your computer, phone, or any other computing device but are actually running over the internet on a “cloud” of computers that are sharing services with each other.

Condition monitoring (CM): An aspect of predictive maintenance, condition monitoring refers to the process of monitoring an aspect of a machine to collect data that can indicate any significant changes that could lead to a breakdown or fault. The types of conditions monitored can vary from temperature, to vibration, to many other parameters.

For further reading on condition monitoring, check out our post “7 Questions To Ask When Building an IoT System for Condition Monitoring“.

Connected products: Also referred to as “smart” products, this term simply refers to any consumer item that has the capability to be connected to the internet.

For further reading on connected products check out our post “How to Build and Design Smart, Connected Products (the Right Way)“.

High speed Internet connection

Connectivity or Internet Connectivity: This refers to the means of connecting something to the internet. There are various methods including WiFi, Ethernet, etc. Choosing your method of connectivity is an important factor when setting up your IoT system.

Cyber-physical systems (CPS): Similar to IoT, these types of systems take objectives that are defined in the digital world to the physical world. They consist of 6 primary characteristics including processing, physical action, energy requirements, sensing, collaboration, and coordination.

Cybersecurity: Simply put, this term refers to any method used to maintain a safe online presence. It includes passwords, two-factor authentication, user permissions, etc.

For further reading on cybersecurity, check out our post “IoT Security 101: The 5 Essential Focus Areas for Building a Safe System“.

Example of a Kosmos dashboard

Dashboard: In a software context, this term refers to any tool that can show a data set in a way that is simplified and easily interpreted by the user. Typically it shows charts, graphs, controls, and other data visualizations.

Data-driven decision management (DDDM): The process of collecting and analyzing relevant information to make business choices based on the insights gathered. This practice is used in almost every industry from manufacturing to government to retail.

Dead zones: Areas with little to no cellular or internet connectivity. For example, deep underground mining areas that have limited connectivity can be considered “dead zones”.

For further reading on how to deal with dead zones in an IoT context, check out our post “How to Achieve Internet Connectivity in Old Buildings & Other Dead Zones“.

Deep learning: Deep learning uses learning algorithms called neural networks to process information. This enables computers to identify patterns in data and define relationships between complex systems of inputs and outputs, among other tasks. Can be supervised, semi-supervised, or unsupervised.

Digital transformation: The process of using technology to change or create new methods of doing tasks. No longer only a cultural shift, digital transformation is now mainly used in a business context to describe the ongoing change in the nature of work and running organizations.

For further reading on digital transformation, check out our post “The Keys to Digital Transformation Without Destruction“.

Digital twin: A digital twin is a virtual model of physical assets, processes, systems, or devices that shows both the elements and the dynamics of how these things work. Digital twins can allow for planning for the future, data analysis, system monitoring and more.

Downtime: The length of time when a machine is out of commission or not available to be used. In the auto industry, downtime costs manufacturers, on average $22,000 a minute.

For further reading on preventing downtime, check out our post “The 8-Step Formula to Calculate the Costs of Unplanned Downtime“.

Edge computing: Edge computing consists of computing that occurs outside of the cloud in applications where real-time data is being monitored or collected.

Edge device: In an IoT context, this refers to the hardware that is controlling the flow of data between networks. For example, in the above architecture diagram, the edge device is a sensor which sends readings to a gateway device where it is analyzed and processed before it is sent to the cloud.

Embedded system: A controller that has its own dedicated function within a larger system that is entrenched inside a complete device. These systems are often based on microcontrollers.

Environmental monitoring: The process of collecting and analyzing data on the environment at both large and small scales. In an IoT context, this often involves placing sensors in particular areas to measure things like soil moisture or water quality.

For further reading on environmental monitoring, check out our post “Why Environmental Monitoring with IoT Could Solve the Climate Crisis“.

Equipment monitoring: The process of collecting and analyzing data on a part of a machine or a piece of equipment. Often used for predictive maintenance and preventing downtime.

Fleet management: the care, control, operation, and handling of a group of commercial transportation vehicles such as trucks, planes, ships, and rail cars. In an IoT context, this often involves vehicle telematics for tracking and diagnostics. The overall purpose is to minimize risks, improve efficiency, and reduce costs.

Fog computing: Similar to edge computing, fog computing is concerned with leveraging the computing capabilities within a local nework to carry out computation tasks that would ordinarily have been carried out in the cloud.

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Gateway device: a piece of hardware that acts as an access point between two networks. In an IoT context, this refers to the hardware device that connects edge devices such as sensors to the cloud.

Hardware agnostic: In an IoT context, this refers to software systems that do not require specific proprietary devices in order to be deployed. Many IoT software platforms are only compatible with hardware that they make specifically for their own systems. Platforms like Temboo’s Kosmos IoT system do not require users to use special hardware and work with many different types of devices without suffering compatibility issues.

IIoT (Industrial Internet of Things): Refers to the use of the Internet of Things in an industrial context. This includes IoT applications in manufacturing, energy management, supply chains, and more.

Timeline of Industrial Revolutions up to Industry 4.0

Industry 4.0 or Industrie 4.0: Industry 4.0 refers to the promise of connecting the digital and the physical worlds through smart factories. Thanks to the emergence of cyber-physical systems, the internet of things, and cloud computing, some experts believe we are on the brink of a fourth industrial revolution. All of these technologies fall under the umbrella of Industry 4.0, which refers to the recent trend towards automation and data exchange in manufacturing.

For further reading on Industry 4.0, check out our post “The Industry 4.0 Mega Guide: 6 Commonly Asked Questions, Answered“.

Interoperability: The ability of different systems and devices to seamlessly work together in a coordinated manner. This is a big challenge in IoT deployments but can be solved through hardware agnostic platforms like Temboo’s Kosmos System.

IoMT (Internet of Medical Things): Internet of Things applications used in medical treatment, devices, facilities, and more. For example, wearable medical devices that can track and monitor patient health are being adopted around the world and can be considered one of the more mainstream applications of IoMT.

For further reading on IoMT, check out our post “A Fantastic Voyage Into the Internet of Medical Things (IoMT)“.

IoT (Internet of Things): Any system with the ability to transfer data over a network without requiring human interaction. For example, industrial sensors that collect data on the machine vibration and send that data to the cloud are a part of the Internet of Things, as are smart watches that can monitor your heart rate during exercise and send that information to an app on your phone.

IoT platform: Multi-layer technology that enables the deployment, management, and automation of connected devices in an IoT system.

Learn more about Temboo’s IoT platform, Kosmos, here.

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M2M (machine-to-machine communication): Direct communications between machines using any channel. Can include industrial instrumentation, signaling, telemetry, and more.

MAC address: Short for “media access control address”, this term refers to a unique identifier assigned to a device for communications within a network segment such as Ethernet, Wi-Fi, and Bluetooth.

Machine learning: Machine learning is a subset of AI in which machines can be trained to take data and ‘learn’ things about it for themselves rather than coding the machine to do a task. It’s about pattern recognition – machine learning technology can allow a system to make predictions based on the patterns and data it receives.

Mesh Networking: With mesh networking, all of the devices in the network can communicate with each other, rather than having to connect with one central hub. This makes the size and area of the network virtually unlimited, which is why it is so useful for industrial IoT operations like large connected sensor networks.

Microcontrollers for IoT

Microcontroller: Microcontrollers can be thought of as tiny computers that can be added to any physical object or space to give it a ‘brain’. They contain one or more computer processors, along with memory and programmable input/output peripherals – all in a single integrated circuit.

For further reading on microcontrollers, check out our post, “How to Choose a Microcontroller for IoT“.

Modbus: Originally developed by Modicon in 1979, Modbus is an open, non-proprietary communications protocol that enables machines to communicate and coordinate with each other by passing information in a standardized way.

Read more about Modbus here!

Neural network: A learning algorithm used to model complex relationships between inputs and outputs, to find patterns in data, or to capture the statistical structure in an unknown probability distribution between observed variables.

OTA Updates Using Temboo

OTA (over the air) updates: OTA updates are a mechanism for remotely updating internet-connected hardware with new settings, software, and/or firmware. At this point in the evolution of IoT, it is well established that a robust OTA update mechanism is an essential component for any successful IoT system design.

For further reading on OTA updates, check out our post, “How to Approach OTA Updates for IoT“.

Siemens S7-1200 PLC

PLC (Programmable Logic Controller): The ruggedized computers that have been at the heart of industrial automation applications since the 1970s. They’re used to automate industrial control systems that have strict requirements around real-time controls and fault tolerance and are a critical part of many types of automated physical systems.

To learn more about PLC programming in the Age of IoT, check out this article from Temboo’s Head of Product, Cormac Driver.

Predictive maintenance (PdM): Utilized in the industrial world since the 1990s, this type of maintenance involves monitoring the performance of machines during normal operation to reduce the likelihood of failures. The data collected will help determine when a machine is operating outside of normal behavior, thus allowing maintenance to happen before it breaks down. This is one use case for machine learning in IoT.

Preventive Maintenance: Similar to predictive maintenance, the goal here is to reduce equipment failures. However, with preventive maintenance, maintenance is performed regularly on the machine to reduce the likelihood of failure rather than doing maintenance when the machine begins to exhibit abnormal behavior (see predictive maintenance above).

Redundancy: Adding backups to a system as a failsafe with the intention of increasing the reliability of it.

Learn more about system redundancy here.

Remote monitoring and control (M&C): Systems designed to supervise and govern objects or facilities over a large distance with some degree of automation. For example, monitoring power grids over a wide area from a point outside of that area.

ROI (return on investment): The formula to calculate the amount of money saved or earned based on the cost of initial investment. This is a measure of performance to evaluate the efficiency of an investment.

Role-based access control (RBAC): The process of restricting system access to authorized users for security purposes. This also includes applications that allow users to have certain permissions relative to their roles as well as restrictions.

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SCADA (Supervisory Control and Data Acquisition): A hardware and software system for monitoring and controlling a factory or industrial equipment.

SDGs (Sustainable Development Goals): In 2015, the United Nations established the 17 Sustainable Development Goals which make up a systematic global framework to end poverty and protect our planet by 2030. According to a recent study, 84% of IoT deployments are addressing the SDGs in some way. 70% of these deployments were driven by the private sector.

Learn more about how IoT can help address the SDGs here.

SDK (Software Development Kit): a collection of libraries, tools, documentation, processes, code samples, and/or more that allow users to create software applications on a given platform.

Sensors: Electronic components whose purpose is to detect events or changes in their physical environments and send that information via electrical signal to other electronics, usually a computer processor. There are countless types of sensors that can measure all sorts of things. For instance, sensors can convert light, motion, heat, moisture, or pressure into an actionable representation (e.g. a numerical value).

Read more about sensors here.

Skills gap: The difference between the skills that employers want or need and the skills their workforce offers. This is a big issue in the manufacturing industry in particular right now.

Smart buildings: The use of automation to optimize all or some of the processes that occur inside a building: heating and cooling, security, lighting, ventilation, water usage, and more.

Here’s the ultimate guide to everything you’ve ever wanted to know about smart buildings.

Smart cities: Cities that leverage digital technology and connectivity to improve quality of life, efficiency of operations and services, and economic prosperity for citizens while ensuring long-term economic, social, and environmental sustainability.

Smart home: Also referred to as home automation or domotics, this is a similar concept to smart buildings but applied to individual homes. It can reference HVAC systems, security, doorbells, entertainment systems and more.

Sub-GHz: a type of wireless technology that allows data transmission using a frequency band under 1 GHz to distant hubs without using a lot of power. A great option for many IoT applications.

Read more about Sub-GHz here.

Upskilling: The process of teaching employees new skills to fill vacancies from the organization’s current workforce. It is especially relevant in the context of emerging technologies.

Learn more about upskilling here.

Wearables: Electronic devices that are designed to be worn by people. Can refer to fitness trackers, pacemakers, smart glasses, and more.

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Posted by:Jessica Califano, Product Outreach

Jessica is on the Product Outreach team at Temboo. When she's not creating content about the latest developments in IoT, she enjoys traveling, photography, and design. She's also passionate about gender equality, advocating for women in STEM fields, and environmentalism.