IoT Edge Computing, Edge Nodes and use cases in the Industrial Sector

November 17, 2021, by David Purón

In recent years, Edge Computing technology has appeared, which proposes that data does not have to be centralized in its entirety, but that part of it can be processed in distributed computers called - Edge Nodes - in the same place where the data is generated.

Before the term IoT Edge came into use, in the early 1980s, students at Carnegie Mellon University of Pennsylvania found a way for the Coca-Cola vending machine to communicate its stock through the campus computer network. This would avoid a trip if the machine was out of stock, and the world's first IoT device was born.

Today, more than half of the electronic devices manufactured in the world are already IoT, i.e. capable of communicating data over computer networks. This will increase exponentially until by 2025 only one in four devices will not be able to communicate with the outside world.

Edge nodes

In 2011 the industry coined the term "data-lake" to define those enterprise databases, which centralise data from a wide range of connected devices, without very rigid structures so that they are easily evolvable to any use of the data. 

In keeping with this analogy, some analysts have humorously transformed the term into "data-tusnami", referring to the inability of many companies to take advantage of these huge volumes of data. The most important battle today is not how much data I can get, but how to acquire it and process it in an optimal way in order to get the most value out of it efficiently.

To navigate this "data-tsunami" coming from thousands of IoT devices, Edge Computing technology has emerged in recent years, which proposes that data does not have to be centralised in its entirety, but that part of it can be processed in distributed computers - calledEdge Nodes - in the same place where the data is generated. 

In this case, only the result or aggregate of such computation can be centralised, thus avoiding overloading the infrastructure, eliminating unnecessary latency, and mitigating the security and data sovereignty risks that matter so much to businesses and citizens today.

Imagine, for example, an energy distribution company that wants to balance its production, in near real time, according to what all its users produce and consume. The infrastructure to communicate, centralise and store all this data from thousands of sensors is so complicated that the return on investment may not be viable.

However, through edge computing, each transformation centre can analyse the information in realtime and only communicate with the centralised infrastructure those relevant deviations that can have a significant impact on the network.

Related reading:
Advantages of Edge Computing in Industrial Environments

IoT Edge: thick edge, thin edge, micro edge

In recent years there has been a great deal of work by large corporations defining and explaining what edge computing is and its different casuistry, arriving at a multitude of definitions and classifications. All of them group the different types of edge computing according to the location in which the data processing is carried out.

  • When data processing is carried out at the point closest to the network and furthest away from the devices, this is referred to as "Fog-Computing" (a term coined by Cisco) or "Thick-Edge". This occurs at distances of 100m to 40km from the devices, and is carried out by very powerful edge nodes, or in some cases even embedded in the network core equipment itself. This is the case for example with some 5G communications towers, which can perform data storage and processing while avoiding unnecessary latency when communicating devices are on the same network. 
  • If the data processing is carried out on network equipment or data aggregators located in the local network itself, it is particularised by the terms "Far-Edge" or "Thin-Edge". The physical distances in these cases can range from 1m to 100m, and is characterised by being carried out by medium-power Edge Nodes, 1GHz and no more than 8GB of RAM, which in many cases also act as data concentrators, IoT gateways, or even intelligent industrial automation equipment.
  • Finally, when the processing is embedded in the IoT equipment itself, we are talking about the so-called Micro-Edge, which in many cases has a very limited functionality as the devices themselves usually have a very limited computing capacity to avoid price increases or battery consumption. 
IoT Edge example

The challenges of the IoT Edge

There is no doubt that IoT at the Edge is one of the new enablers that will accelerate the digital transformation of enterprises. However, its deployment is not without its challenges that any enterprise must consider in its design and implementation phase.

1. Cybersecurity: As a network of distributed, in many cases unattended resources, often connected to critical elements, its security design, protection and monitoring requires special attention. This is of much greater relevance when Edge Nodes can operate the connected equipment.

2. Scalability: especially in "Far Edge" or "Micro-Edge" computing environments, the number of devices deployed can be very high (thousands to tens of thousands). Therefore the installation, provisioning and maintenance of Edge Nodes can raise the hidden costs of deployment to the point of being uneconomical. Especially in the case of industrial installations, which have extremely long lifetimes, it is essential to have tools that facilitate this lifecycle management of Edge Nodes in a remote, centralised and scalable manner.

3. Integration: The typology of connected devices, especially in the industrial world, is highly fragmented. There are no fully dominant communication protocols or common data structures. It is therefore important that an edge computing deployment is based on open technologies, ideally standard or widely used by industry, that allow the effective integration and evolution of different parts with the deployed infrastructure. Monolithic, closed solutions with high integration costs should be avoided.

IoT Edge applied to industries with distributed devices

Across all sectors, industrial companies are undergoing digital transformation processes. Being able to connect devices, as well as collect and exploit this data in real time, is becoming crucial to driving their businesses forward.

The industries where the IoT Edge can have the greatest impact are those that deal with a high volume of connected devices. But the impact is exponentially greater where these devices are in geographically distributed situations and generate data at high frequencies.

In this respect, they take a clear position:

  • UtilitiesBusiness continuity is key for the critical utilities sector. Monitoring their assets to detect failures, or even prevent them, is a basic functionality. However, their assets are often located in remote locations. Edge computing in this case enables real-time analysis, with processing much closer to the asset, meaning much less reliance on connectivity and better response times.

  • Renewable energy: Through Edge Computing a high impact can be achieved in the sustainable management of limited renewable energy resources, such as solar and wind energy. Again, in a remote and highly distributed environment, it can avoid a high dependency on connectivity and provide the high robustness and security needed for such a critical service. Edge computing algorithms can assess in real time, and even predict, the supply and demand of energy resources, leading to substantial improvements in the energy balance. Companies seeking to reduce carbon emissions are increasingly looking positively at the use of Edge Computing combined with the Cloud in this regard. 

  • Smart Grids : With the emergence of distributed energy resources, such as electric cars, chargers, batteries, self-consumption solar panels, and other elements, local decision making can lead to very high energy efficiency improvements for companies or large communities. Given that this management is complicated and has many variables, it is not expected to be left to the users, but must have a high degree of automation. And given that data privacy may imply restrictions on its use, the heterogeneity of devices may complicate its integration in cloud platforms, and latency or errors may have implications for the business case, Edge Computing is positioned as an architecture with very high potential. 

Recommended Reading: Four Edge Computing applications in the power sector

  • Logistics and Mobility: In this case, where assets are not only distributed and multiple, but mobile, the use of Far-Edge with in-vehicle computing nodes is a growing trend. From the most basic cases of sensorisation for securing loads or inventorying critical elements, to the most advanced ones for route optimisation or even semi-automation of driving, low latency response and overall system reliability are very relevant aspects when placing the computing at the closest point to the assets.

  • Distributed manufacturing: Distributed, or decentralised, manufacturing is understood as manufacturing whose product is produced in a network of several smaller, geographically dispersed facilities coordinated through computer networks. The cost of installing and maintaining these networks has traditionally been very high due to the need to transport a lot of data very frequently between multiple sites. While cloud computing has been a significant improvement in these environments, the combination with algorithms at the edge can optimise investment, improve data security, and facilitate compliance with industry regulations that do not fit as well in cloud environments.

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