The Internet of Things (IoT) is made up of smart devices connected to a network—sending and receiving large amounts of data to and from other devices—which produces a large amount of data to be processed and analyzed.
Edge computing, a strategy for computing on location where data is collected or used, allows IoT data to be gathered and processed at the edge, rather than sending the data back to a datacenter or cloud.
Together, IoT and edge computing are a powerful way to rapidly analyze data in real-time.
What are IoT and edge computing?
The Internet of Things (IoT) refers to the process of connecting physical objects to the internet. IoT refers to any system of physical devices or hardware that receive and transfer data over networks without any human intervention. A typical IoT system works by continuously sending, receiving, and analyzing data in a feedback loop. Analysis can be conducted either by humans or artificial intelligence and machine learning (AI/ML), in near real-time or over a longer period.
If something is referred to as smart, that generally implies IoT. Think of self-driving cars, smart homes, smartwatches, virtual and augmented reality, and industrial IoT, for example.
Edge computing takes place at or near the physical location of either the user or the source of the data. By placing computing services closer to these locations, users benefit from faster, more reliable services with better user experiences, while companies benefit by being better able to support latency-sensitive applications, identify trends, and offer better products and services.
Edge computing is one way that a company can use and distribute a common pool of resources across a large number of locations to help scale centralized infrastructure to meet the needs of increasing numbers of devices and data.
What’s the difference between an IoT device and an edge device?
Edge devices are physical hardware located in remote locations at the edge of the network with enough memory, processing power, and computing resources to collect data, process that data, and execute upon it in almost real-time with limited help from other parts of the network.
An IoT device is a physical object that has been connected to the internet and is the source of the data. An edge device is where the data is collected and processed.
Edge devices can be considered part of the IoT when the object has enough storage and compute to make low latency decisions and process data in milliseconds.
The terms IoT device and edge device are sometimes used interchangeably.
How are IoT and edge related?
IoT benefits from having compute power closer to where a physical device or data source actually exists. In order for the data produced by IoT devices to react faster or mitigate issues, it needs to be analyzed at the edge, rather than traveling back to a central site before that analysis can take place.
Edge computing is a local source of processing and storage for the data and computing needs of IoT devices. Here are some of the benefits of using IoT and edge together:
Reduced latency of communication between IoT devices and the central IT networks.
Faster response times and increased operational efficiency.
Improved network bandwidth.
Continued systems operation offline when a network connection is lost.
Local data processing, aggregation, and rapid decision making via analytics algorithms and machine learning.
An IoT gateway can send data from the edge back to the cloud or centralized datacenter, or to the edge systems to be processed locally.
Edge computing and cloud computing
In a cloud computing model, compute resources and services are often centralized at large datacenters. Clouds often provide a portion of the network infrastructure required to connect IoT devices to the internet.
Edge devices require network connectivity to central locations for different purposes: To allow remote management, to receive automation instructions, to forward network telemetry traffic needed for analytics, and to send data information which will be lately stored in databases, and analyzed to accomplish business objectives.
The communication provided by a cloud service may just be the transfer of data from an edge device, across a cloud, and into a datacenter—or it could be the edge device sending a log of the decisions it made back to the datacenter for data storage, data management, data processing, or big data analysis.
IoT and edge computing use cases
Industrial IoT, or IIoT, refers to the use of IoT in an industrial context, such as the machines in a factory. Think of the lifecycle of heavy machinery used in a factory. Different people may stress equipment differently over time, and breakdowns are an expected part of operations.
IoT sensors can be added to parts of the machinery that are most prone to breaking or overuse. The data from these sensors can be analyzed and used for predictive maintenance, reducing overall downtime.
Autonomous vehicles are an example of why IoT solutions and edge computing need to work together. An autonomous vehicle driving down the road needs to collect and process real-time data about traffic, pedestrians, street signs and stop lights, as well as monitor the vehicle’s systems.
If the vehicle needed to stop or turn quickly to avoid an accident, sending data back and forth from the vehicle to the cloud to be processed would take too long.
Edge computing brings cloud computing services to the vehicle, allowing the IoT sensors in the vehicle to process the data locally in real-time to avoid an accident.
Why choose Red Hat for edge?
Red Hat’s open source edge computing solutions focus on streamlining operations through automated provisioning, management, pre-defined configurations, and orchestration. We want to help you establish and optimize a common infrastructure that stretches across your compute, storage, and network needs.
Red Hat® Enterprise Linux® is an operating system (OS) that’s consistent and flexible enough to run enterprise workloads in your datacenter or modeling and analytics at the edge. It helps you deploy mini server rooms on lightweight hardware all over the world and is built for workloads requiring long-term stability and security services on hundreds of certified hardware, software, cloud, and service providers.
Red Hat® Ansible® Automation Platform's self-contained automation capabilities can be deployed across hybrid clouds and edge environments. Its automation mesh component provides a framework to scale automation from single sites to the edge. It's even available on leading public cloud providers' infrastructure, like Red Hat® Ansible® Automation Platform on Microsoft Azure.
Finally, Red Hat® OpenShift® is a Kubernetes platform to build, deploy, and manage container-based applications across any infrastructure or cloud—including private and public datacenters, or edge locations.