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IoT, like any specialized subject, comes with its fill of jargon and acronyms that everyone must understand before they can dig deeper into its nuances.
The easiest way to explain IoT usually starts with everyday examples, including smart thermostats or apps that allow you to turn off lights without flipping a physical switch. These smart or connected devices represent only the basic parts of IoT; technologists have so much more to learn if they want to implement IoT into their organizations. IoT deployments embody many other technologies, such as AI, cloud and edge computing, connectivity and security.
Start with the basic IoT terminology to discover important components of IoT and how IoT fits in with other technology trends.
10 beginner terms to understand IoT
Internet of things. IoT is a system of sensors or devices used to collect and exchange data through a network connection without any human involvement. IoT can be used to 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 applied to increase efficiency or better understand processes.
IoT devices. Also sometimes referred to as smart or connected devices, these include any device or sensor with computing capabilities that connects to the internet but would not necessarily be internet-enabled traditionally. 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 environment 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 connected to the internet previously for real-time analytics that promote more efficient business actions. Applications of IIoT include predictive maintenance, supply chain traceability and asset tracking. The architectural components of IoT and IIoT often are the same, 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.
Fourth industrial revolution. Technologists might hear other professionals refer to IoT as part of the fourth industrial revolution. The idea behind it is that emerging technology trends -- including IoT, virtual reality and AI -- are transforming 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 digitally transforming their company using IoT as the catalyst for change. 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 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 can be used in 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. The value of IoT comes from what organizations do with the data devices generate. They use analytics tools to distill actionable information from the plethora of IoT data. IoT analytics tools take data from sensors on manufacturing equipment, smart meters or delivery vehicles for reporting and analysis. IoT analytics presents organizations with challenges stemming from the different types of data, the amount of data and the speed needed for data processing.
IoT edge computing. Edge computing is the architecture IoT needs to process the data close to where it is generated, particularly when data processing is time sensitive. Edge computing addresses the problem of sending vast amounts of data over the network by processing 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 need to be analyzed in real time can be analyzed in the cloud.
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. Storing information 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 building in security measures during development, which has left connected devices with vulnerabilities to mitigate. Many methods used to secure businesses will 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, meaning organizations have many frameworks to choose from without clear guidelines. They may end up with devices that use different protocols, which makes it difficult to secure systems and ensure interoperability. There are groups working to develop industry standards, such as the National Institute of Standards, but they are still in progress.