The Internet of Things
What is the Internet of Things (IoT)?
In simple terms, the Internet of Things (IoT) refers to an ongoing trend of connecting all kinds of physical objects to the internet, especially ones that you might not expect. This can mean everything from common household objects like refrigerators and lightbulbs, to business assets like shipping labels and medical devices, to unprecedented wearables, smart devices, and even smart cities that only exist because of IoT.
More specifically, IoT refers to any system of physical devices that receive and transfer data over wireless networks without human intervention. This is made possible by integrating simple computing devices with sensors in all kinds of objects. For instance, a "smart thermostat" ("smart" usually means "IoT") can receive location data from your smart car while you are commuting, which it uses to adjust your home’s temperature before you arrive. This is achieved without your intervention, and produces a more desirable result than if you manually adjusted the thermostat before leaving for the day or after you returned.
A typical IoT system, like the smart home described above, works by continuously sending, receiving, and analyzing data in a feedback loop. Depending on the kind of IoT system, analysis can be conducted either by humans or artificial intelligence and machine learning (AI/ML), in near real-time or over a longer period. Think of the smart home example again. In order to predict the optimal time to control the thermostat before you arrive home, your IoT system might connect to the Google Maps API for data about real-time traffic patterns in your area, as well as utilize long-term data your car collects about your commuting habits. Beyond that, IoT data collected from every smart thermostat customer can be analyzed by utilities companies in larger-scale optimization efforts.
As a technological breakthrough, IoT often gets attention from a consumer point-of-view, where exciting new experiences with technologies like wearable smartwatches are described in relation to their inherent privacy and security concerns. This consumer perspective is probably important to understand if you’re thinking about adopting an enterprise-level IoT project, especially if the end user is the general public, but you’ll also want to read about IoT from the perspective of business use cases.
From the perspective of enterprise IT, IoT solutions allow companies to improve their existing systems, and also build entirely new connection points to customers and partners. It also brings with it new IT challenges. The volume of data that is producible by a system of smart devices can be impressive, hence the name "big data," but integrating big data into existing systems and setting up analytics to act on it can get complicated. Additionally, IoT security can be a major consideration when deciding how open to build an IoT platform. Still, for many companies, IoT has proven to be worth the effort, and successful enterprise IoT use cases can be found in nearly every industry.
Examples of enterprise IoT
Industrial IoT (IIoT): Imagine the lifecycle of heavy machinery used on a construction site. Different human operators may put different levels of stress on the equipment over time, and break-downs for any number of reasons are an expected part of operations. Now consider the implementation of specialized sensors to those parts of the machinery that are most prone to breakage and overuse. Not only are sensors like this used for predictive maintenance and to improve human proficiency (an example of real-time data collection and analysis), but also to feed data back to the factory where engineers can improve new model designs (an example of longer-term data analysis).
Farming IoT: IoT has revolutionized farming in a number of ways, including through the use of moisture sensors. By installing an array of moisture sensors across their fields, farmers are now able to receive more accurate data to predict when to irrigate their crops. IoT can also be taken a step further in this use case, where moisture sensors are connected to IoT applications controlling the irrigation machinery itself, automatically triggering irrigation based on sensor data, all without the need for human intervention.
Logistics and transportation IoT: One of the first implementations of IoT in the logistics and transportation industry involved labelling shipping containers with radio-frequency identification (RFID) devices. These simple labels store digital data that can be captured by a reader through radio waves as long as the RFID is within a certain distance of a reader. At first, this allowed logistics companies to track when containers arrived at certain checkpoints where RFID readers were installed, like a warehouse or shipping yard. Advancements in IoT have now led to battery-powered smart tracking devices to replace RFID, though. These devices can transmit data continuously to IoT applications without the need for on-site readers, meaning companies can analyze real-time data for a shipment across every stretch of the supply chain.
IoT and edge computing
What makes a smartphone "smart?" The obvious answer is that it includes a computer processor and associated hardware that allows the phone to display a graphical interface, run an operating system, connect to the internet, run apps, and so on. The answer is similar for the thermostat in the smart home example above—the thermostat is "smart" because it includes a computer system that can receive and transfer data without human intervention.
In the realm of IoT, the ability of devices to utilize compute power is becoming increasingly valuable as a means to rapidly analyze data in real-time, and for good reason. Simply sending or receiving data can be an important step in an IoT solution, but sending, receiving, and analyzing data together with IoT applications opens up many more possibilities.
Consider the RFID example in the logistics and transportation industry. This early IoT device stores digital data that it sends to a reader device through the use of radio waves. That reader device can receive the radio waves and then make the information available for analysis, but communication between the RFID and reader is always one way. The RFID device cannot itself receive updates from the reader, just as the reader cannot transfer data or instructions back to the RFID. This limits the container tracking to check-ins at certain locations, rather than continuous monitoring. But what if the IoT device tracking the containers could coordinate with IoT sensors installed in a driverless vehicle transporting them, all connected to a data analytics system managed by the logistics company?
To achieve this IoT scenario, the logistics company would need a lot of compute power available in the physical IoT devices, especially the driverless car. Rather than being simple sending and receiving devices—always waiting for instructions from a centralized data center over Wi-Fi—the IoT devices would themselves need to process data and make decisions. This implementation of compute power closer to the outer edges of a network, rather than at a centralized data center, is known as edge computing.
In a cloud computing model, compute resources and services are often centralized at large datacenters, which are accessed by end users at the "edge" of a network. This model has proven cost advantages and more efficient resource sharing capabilities. However, new forms of end-user experiences like IoT need compute power closer to where a physical device or data source actually exists, i.e. at the network’s "edge."
In response to this, edge computing refers to a model that distributes compute resources out to the "edge" of a network when necessary, while continuing to centralize resources in a cloud model when possible. It is a solution to the problem of needing to quickly provide actionable insights based on time-sensitive data. Coordinating a fleet of driverless vehicles transporting containers with smart tracking devices is a flashy example, but there are many smaller, more practical implementations as well.
Consider the construction site again. Perhaps this company has also developed a bluetooth enabled tool at a job site sending data through worker’s smartphones, which helps the company track it for loss prevention. But now imagine 10 employees working around that device all day, so that their smartphones are constantly pinging the server to indicate where the tool is. Obviously, this redundant server activity can overload the company’s system. By developing IoT applications that can be run on the workers’ smartphones, though, they can essentially push the intelligence to the smartphones—to the network’s "edge"—to analyze and reduce unnecessary server pings.
Red Hat® AMQ—based on open source communities like Apache ActiveMQ and Apache Kafka—is a flexible messaging platform that delivers information reliably, enabling real-time integration. It provides remote service interfaces for connecting large numbers of IoT devices to a messaging back end.
Red Hat® Fuse is a distributed, cloud-native integration platform. Its distributed approach allows teams to deploy integrated services where required. The API-centric, container-based architecture decouples services so they can be created, extended, and deployed independently.