Internet of Things flat iconic illustration
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As more tech enterprises look for easier ways to optimize their solutions and reduce running costs, there is a growing demand for edge computing. Computing on the edge help to facilitate the processing of data without depending solely on centralized data centers, and IoT devices seem to be at the forefront in the application of this computing paradigm.

SEE: Don’t curb your enthusiasm: Trends and challenges in edge computing (TechRepublic)

Recent stats from the Global Edge Computing Market Report 2022 have predicted a huge growth in the edge computing market with its size expected to hit $155.90 billion by 2030, demonstrating a compound annual growth rate of about 38.9% during the forecast period.

There is also a corresponding increase in the adoption of IoT devices across the globe, which according to Statista is estimated to hit around 29 billion by 2030.

With such positive developments happening, there is a need to explore how IoT and edge computing can work together.

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Edge computing and IoT

Edge computing is a computing paradigm that supports the computation of data resources at the edge of the network or close to the devices where data is generated. It’s a computing model where data does not need to be transported to faraway data centers to be processed and analyzed.

On the other hand, IoT is a collection of interconnected smart devices that use sensors, communication hardware and embedded systems to collect, transfer and process data — either in cloud-hosted data storage centers or within the edge of the network. IoT is the term for the technology that brings internet connectivity, smartness, lightweight AI and analytics to physical objects.

There is a growing use case for IoT across several industries as internet-enabled sensors and analytic capabilities now power more devices to transform how machines work. With this increase in use cases comes a higher volume of data processed by IoT devices. However, more data to process sometimes leads to lag, privacy issues and slow processing time on data needed to solve urgent problems.

There is a need to improve IoT devices and reduce these bottlenecks largely caused by moving huge chunks of data from the IoT devices to data centers for processing and analysis. This is why IoT data needs to be gathered and processed on edge to facilitate real-time computing.

How IoT and edge computing can work together

The IoT industry and edge computing can work together for enhanced performance in several ways. Today, there is a growing deployment of edge computing on IoT devices. Below are some notable use cases where edge computing can power IoT both now and in the future.

Condition-based monitoring in IoT devices

One of the ways edge computing can power IoT is the case of condition-based monitoring. IoT condition monitoring is an important factor in business monitoring and maintenance strategy. Condition-based monitoring is a term that describes the monitoring of conditions on devices to point out changes and how the changes could lead to faults.

Condition-based monitoring on IoT focuses on data input and output to check for changes and possible lines of action to forestall failure or downtime in the device. For this to work, data needs to move over sensors, networks and connected IoT devices, where it’s analyzed and interpreted for quality predictive maintenance reporting in real-time. Due to the high volume of data processed, IoT condition-based monitoring devices need edge computing for better performance.

With edge computing, condition-based monitoring IoT devices can process data faster, do away with latency and provide information that will help engineers make better maintenance decisions. The integration of edge computing in this instance will help organizations to be more proactive in ensuring efficiency in their system and drastically reduce the cost of maintenance.

Better application of AI in IoT

With the proliferation of smart IoT devices across the globe, one can’t write off the place of artificial intelligence in IoT. Today, there are self-driving cars, robots that help with several production processes in the manufacturing industry, computer vision and other use cases of AI in IoT. All these technological advancements are made possible by the application of AI to IoT.

However, the surge in the use of AI in IoT devices can be made more efficient by the power of edge computing. There is no doubt about the huge amount of data needed for AI to make accurate predictions in IoT devices. Edge computing can be used to trim down the time it takes for data to be processed by keeping data computation within the edge.

Industrial IoT and Industry 4.0

Industrial IoT and Industry 4.0 are two terms that have gained much traction in recent years. The idea behind the two concepts is about using the internet and huge data to power and manage industrial smart machines.

Before now, industrialization was driven primarily by humankind and mechanical equipment, also known as dumb tools. However, given the penetration of technology in all spheres of modern business life, huge data and real-time analytics are being applied to industrial machines to enhance efficiency and improve production output.

IIoT also relies on data capturing and analytics of voluminous data and the internet to interact with other devices in real-time. As a result of the level of data computation required to drive IIoT and Industry 4.0, edge computing becomes vital in facilitating computing in IIoT.

Why IoT and edge computing will continue to work together

Current trends show that the world is still far from seeing the end of the convergence between edge computing and IoT. Nevertheless, there are many reasons why IoT and edge computing will continue to work together.

IoT needs stable connectivity to be super efficient, and edge computing guarantees that. IoT does not need to be in perpetual touch with the data hosted in the central cloud when edge computing easily provides an enabling ground for data computation on the edge. Application of IoT in businesses that offer financial services, health services and autonomous cars can not cope with latency if they must satisfy their customers. Therefore, IoT in these areas may continue to rely on edge computing to meet business goals and user satisfaction.

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