How Are Edge Computing and IoT Related?

When IoT (internet of things) came to the scene, cloud computing was at its peak. Organizations used the cloud to power their IoT infrastructures. However, after some years, decentralized computing began to offer better performance and efficiency. At this juncture, the drawbacks of cloud-based systems were exposed, giving rise to the adoption of another technology that is increasingly becoming the de-facto standard for IoT systems: edge computing.

Edge computing is a paradigm of distributed computing in which significant data processing is performed at the data source, rather than relying on a cloud-based system. For example, traditionally, an IoT sensor was supposed to send the data to the cloud for performing data analytics. With edge computing, this data analytics part was performed locally with the edge equipment, paving the way for efficient and decentralized computing methodology.

Edge computing specially offers broader applications to the IIoT (Industrial Internet of Things), which typically consists of a complex network of several controllers, sensors, and servers. A large number of these IIoT infrastructures are installed in several, remote locations. Using a cloud for all of these locations does not offer optimal results for all types of operations. Here, a decentralized paradigm that allows for at-source processing of services can provide a more significant advantage. A Real-Life Example

To understand how edge advantage is advantageous for IoT, let’s go over a real-life example.

A remotely-located turbine requires continuous maintenance. Technicians can leverage edge computing, so they generate data visualizations for basic analytics of the field’s surroundings. If the management had used a cloud-based infrastructure here, then irregular cellular communications can hamper the transfer of crucial data. This data delivery is necessary for proper diagnosis of the turbine; therefore, edge computing stands out as a better alternative for its real-time computational advantage.

Therefore, processing capacity gets supplied to the edge of the data source with the standard IIoT gateway equipment. However, this does not mean that the cloud is entirely ineffective. The turbine management can use the cloud for those intensive analytics in which a more robust set of resources is essential, such as data warehousing and stored business logic. Deciding Between Cloud and the Edge

Edge computing not only provides better performance, but it is also a cost-effective strategy. It can save bandwidth and other computing resources, resulting in a significant amount of cost reductions. Edge, on the whole, emerges as a dominant paradigm for IIoT applications to maintain data accuracy, freshness, quality, and delivery speed.

However, it is vital to make sure that edge computing is placed appropriately in the IoT equation. It is not a complete replacement of cloud computing. Instead, it is an enabler that can replace the cloud for certain operations.

An ideal strategy is to use both the cloud and edge computing to complement each for developing productive and efficient IoT platforms. The key is to identify which of the computing style is beneficial for which of the processing tasks.

If you are in a similar spot of bother, contact Tantiv4, so that we can develop a strategy for the integration of edge and cloud computing in your IoT infrastructure.

Tantiv4’s ThingaOS™ can do the needful without disrupting their existing product assembly lines and portfolios.