Edge Computing and IIOT: a new revolution

What is Edge Computing?

During the last few years, Cloud Computing has now become broadly adopted, but the number of companies facing the limitations of this centralized approach is continually increasing.

New technologies require a time of response super fast (probably real-time in most of the cases), IoT devices are spreading around, and the expected final experience from clients and users has changed. All these three factors are leading the computation towards a distributed and new approach: edge computing.

Edge computing moves the power of processing closer to the source or next to the last-mile network connection to the data source. Data no longer needs to travel to a centralized computational node to be processed.

Edge computing offers, in consequence, a new and robust solution for all these applications requiring response times of less than a second. By having the computational power close to the edge, we reduce latency drastically and accelerate the processing.

Edge computing and IIOT

Edge Computing is managing to solve some of the toughest challenges of the industrial internet. With this new approach, the computation power is distributed horizontally among all the IIOT subsystems. This new distributed model allows a wide variety of interactions and communication paradigms including peer-to-peer networking, edge-device collaboration, distributed queries across data stored in devices, and distributed data management.
These are some of the main benefits of Edge Computing in IIOT:

Latency reduction

Edge computing reduces significantly the latency times associated with centralized cloud systems. Nowadays, every second matter and reducing time means cutting costs. For example, any autonomous utility assets in dangerous critical areas only can be managed via a real-time interface, and that requires very low latency.

Security from the source

Edge Computing can be effectively managed from all the different layers of data. The entire data lifecycle will be covered, from the hardware to the communication. This then allows to improve the compliance of relevant security regulations and to better admin certifications and security patches in each distributed node.

Quality of service

Having each distributed device and source of data clearly identified and remotely managed reduces the risk of an overall system failure significantly and allows to timely detect an issue in a particular node (source).

Quality of Analytics

Having data collected from the source allows analyzing data faster, as it comes to the analytical platform previously processed at the edge. The interoperability between distributed IIOT devices is easier to manage to have edge computing in between.

Cost

The operational costs are reduced because the data that is finally sent from the device to storage has been previously processed, which means that it will require less network wideband and less storage space.

Increased operational efficiency

Effective and faster access to data enables more impactful analysis that enhances better operational decision-making.

Driving automation

Achieving true automation within industrial shop floors can be possible through the complete integration of edge computing in the equipment, devices, and processes that drive operations. In situations where IIoT devices produce large data sets, sending captured data to centralized systems for analysis before sending actionable results back to the device slows down automation.
Edge computing can be integrated to eliminate communication and processing time lags to drive real-time automation. This means a lights-out factory in the real sense can be achieved where human contact and labor are reduced.