Why IoT and edge computing need to work together
IoT creates and consumes large amounts of data that require processing and analysis. Edge computing moves computing services closer to data sources such as end users or IoT devices.
This allows IoT data to be collected and processed at the edge location where the device is located, rather than sending the data back to the data center or cloud to identify patterns that initiate actions such as anomaly detection for predictive maintenance faster.
The ability of IoT devices to harness computing power is becoming increasingly valuable as a means of rapidly analyzing data in real time.
What is IoT and Edge Computing?
The Internet of Things (IoT) is the process of connecting physical objects to the Internet. An IoT device is a physical IoT device or hardware that transmits and receives data over a network without manual intervention.
From common household appliances such as light bulbs to medical devices, wearables, smart devices, and even traffic lights in smart cities, these IoTs fall into this category.
A typical IoT system continuously transmits, receives, and analyzes data in a feedback loop, which can be manipulated by humans or analyzed through artificial intelligence and machine learning (AI/ML) in near real-time or over an extended period of time.
Edge computing reduces latency and saves bandwidth by performing computing at or near the physical location of a user or data source.
By placing computing services closer to these locations, users can enjoy a better user experience with faster and more reliable services. Businesses can better support latency-sensitive applications and use technologies such as AI/ML analytics to spot trends and improve products and services.
Edge computing enables businesses to use and deploy a common pool of resources across multiple locations to handle growing devices and data through centralized infrastructure scaling.
IoT gateways send data from the edge back to the cloud or centralized data center, or to edge systems for local processing.
How do IoT and edge computing relate?
IoT can take advantage of smooth computing power as it gets closer to the physical location of a physical device or data source. To rapidly analyze data generated by IoT sensors or devices to respond and mitigate issues more quickly, data analytics must occur at the edge, rather than at a central site that takes time to travel.
Edge computing serves as a local source of processing and storage for the data and computing power required by IoT devices, reducing communication latency between IoT devices and the central IT network to which they are connected.
Without edge computing, IoT would have to rely only on network connectivity and computing services in the cloud or datacenter. Sending and receiving data between IoT devices and the cloud can slow response times and reduce operational efficiency.
Edge computing also helps address other issues, such as the network bandwidth required to transfer large amounts of data over slow cellular or satellite connections, and allows systems to continue working offline when network connectivity is lost.
By implementing edge computing, you can leverage the massive amounts of data generated by connected IoT devices. Deploying analytic algorithms and machine learning models at the edge can process data locally to make faster decisions. Edge computing also benefits from being able to aggregate data before sending it to a central site for further processing or long-term storage.
Edge Computing and Cloud Computing
In the cloud computing model, computing resources and services are often centralized into large data centers. The cloud provides part of the network infrastructure needed to connect IoT devices to the Internet.
Edge devices require some kind of network connection to facilitate two-way communication between the device and a database in a centralized location, and the cloud typically provides this network connection.
Communication provided by the cloud can be in the form of sending data from an edge device to the data center via the cloud, or sending decision logs from the edge device back to the data center for data storage , data processing, and big data analysis.
What is the difference between IoT devices and edge devices?
Edge devices are physical hardware at a remote location at the edge of a network. It has enough memory, processing power, and compute resources to collect, process, and execute data in near real-time with limited help from other parts of the network.
An IoT device refers to a physical object that is connected to the Internet and is a data source, and an edge device refers to a location from which data is collected and processed.
Edge devices with sufficient storage and computing power to make decisions and process data with low latency (milliseconds) can be considered part of the IoT.
The terms IoT device and edge device are sometimes used interchangeably.
Examples of real IoT
The word 'smart' generally refers to IoT. Some examples of IoT include:
autonomous vehicle
smart thermostat
smart home
Virtual and Augmented Reality
smart city
Industrial IoT
smart watch
IoT and Edge Computing Use Cases
Industrial IoT, or IIoT , refers to IoT used in industrial environments, such as machines in factories. Take, for example, the lifecycle of heavy equipment used in a factory. Each user places a different burden on the equipment, and it may break down at any time during operation.
In these machines, IoT sensors can be added to easily failing or overused parts to collect data, which can be analyzed and utilized for predictive maintenance to reduce overall downtime.
Autonomous vehicles are a good example of why IoT and edge computing need to work together. Autonomous vehicles running on the road must collect and process real-time data such as traffic volume, pedestrians, road signs, traffic lights, and monitor vehicle systems.
When a vehicle stops quickly to avoid an accident but needs to quickly change direction, it takes too long for the vehicle and the cloud to send and receive data.
Edge computing brings cloud computing services to the vehicle, enabling the vehicle's IoT sensors to locally process data in real-time to avoid accidents.
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Why Choose Edge Computing from Red Hat?
Red Hat's edge computing solutions are focused on streamlining operations through automated provisioning, management, and orchestration. Red Hat can help you build a common infrastructure that spans your workload needs, including compute, storage, and network.
Red Hat® Enterprise Linux® is an operating system (OS) with sufficient consistency and flexibility to run enterprise workloads in the data center with modeling and analytics at the edge.
Anywhere in the world, we're struggling a lot in the process of deploying mini server rooms on lightweight hardware. Red Hat Enterprise Linux is built for workloads that require long-term reliability and security across a broad ecosystem of certified hardware, software, cloud, and service providers .
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