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Why IoT and edge computing need to work together

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IoT produces a large amount of data that needs to be processed and analyzed so it can be used. Edge computing moves computing services closer to the end user or the source of the data, such as an IoT device. 

This enables the IoT data to be gathered and processed at the edge where the device is located, rather than sending the data back to a datacenter or cloud to help identify patterns that initiate actions faster like anomaly detection for predictive maintenance. 

The ability of IoT devices to utilize compute power is becoming increasingly valuable as a means to rapidly analyze data in real-time. 

The Internet of Things (IoT) refers to the process of connecting physical objects to the internet. IoT refers to any system of physical IoT devices or hardware that receive and transfer data over networks without any human intervention. 

An IoT device can be anything from common household objects like lightbulbs; to healthcare assets like medical devices; to wearables, smart devices, and even traffic signals in smart cities.

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. 

Edge computing is computing that takes place at or near the physical location of either the user or the source of the data, which results in lower latency and saves bandwidth.

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 and using technologies like AI/ML analysis to 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.

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.

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 sensors and devices to be analyzed quickly so it can be used 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, which reduces the latency of communication between IoT devices and the central IT networks those devices are connected to.

Without edge computing, IoT would rely on network connectivity and compute services from a cloud or datacenter. Sending data back and forth between an IoT device and the cloud can result in slower response times and less operational efficiency.

Edge computing also helps to address other issues such as the network bandwidth required to send massive amounts of data over slow cellular or satellite connections, and the ability for systems to continue to work offline when a network connection is lost.

Edge computing allows you to benefit from the large amount of data created by connected IoT devices. Deploying analytics algorithms and machine learning models to the edge enables data processing to happen locally and be used for rapid decision making. Edge computing also allows for data to be aggregated before being sent to a centralized site for further processing or long term storage.

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 some kind of network connectivity to facilitate back-and-forth communication between the device and a database at a centralized location. That network connection is usually provided by clouds.

The communication provided by a cloud 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 processing, or big data analysis.

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.

If something is referred to as “smart”, that generally implies IoT. These are a few examples of IoT: 

  • Self-driving cars
  • Smart thermostat
  • Smart homes 
  • Virtual and augmented reality 
  • Smart cities
  • Industrial IoT
  • Smartwatches

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 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. 

Red Hat’s edge computing solutions focus on making operations simpler through automated provisioning, management, and orchestration. We want to help you establish a common infrastructure that stretches across your workload needs (compute, storage, and network).

Red Hat® Enterprise Linux® is an operating system (OS) that’s consistent and flexible enough to run enterprise workloads in your datacenter to modeling and analytics at the edge.

There are a lot of challenges that come with deploying mini server rooms on lightweight hardware all over the world. Red Hat Enterprise Linux is built for workloads requiring long-term stability and security across a broad ecosystem of certified hardware, software, cloud, and service providers. 

For building, deploying, and managing container-based applications across any infrastructure or cloud—including private and public datacenters, or edge locations—choose Red Hat OpenShift. It’s a container-centric, high-performance, enterprise-grade Kubernetes environment.

The tools to support an edge computing solution

Enterprise software optimized for lightweight deployment on all kinds of hardware, letting you put the right amount of computing power in the right place.

Red Hat Enterprise Linux

The Linux foundation for scaling existing apps and rolling out emerging technologies across edge, bare-metal, virtual, container, and all types of cloud environments.

Red Hat OpenShift

For building, deploying, and managing container-based applications across any infrastructure or cloud—including private and public datacenters, or edge locations.

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