The world’s first IoT device was born in the early 1980s, when students at Carnegie Mellon University in Pennsylvania found a way for a Coca-Cola vending machine to communicate its stock through the campus computer network thus, avoiding unnecesary trips if the machine was running out of stock.
Today, more than half of the electronic devices manufactured in the world are IoT devices, i.e. are capable of communicating data over computer networks; and this will increase exponentially by 2025. The total installed base of Internet of things connected devices worldwide is projected to amount to 30.9 billion units by 2025 according to Statista.
It is fair to say that virtually any data needed for a company to strengthen its decision making process, or optimise its operational processes, is available on its computer networks at one level or another.
In 2011, the industry coined the term «data-lake» to define those company databases that centralise data from a wide range of connected devices, without very rigid structures so that they can easily evolve towards any use of the data.
In keeping with this analogy, some analysts have transformed the term into «data-tusnami», referring to the inability of many companies to take advantage of the huge volumes of data. The most important battle today is not how much data you can get, but how to acquire and process it in an optimal way in order to get the most value out of it, in an efficient manner.
To navigate this «data-tsunami» that come from thousands of IoT devices, Edge Computing technology has emerged as a solution that proposes that data does not have to be centralised entirety, instead, part of it can be processed on distributed computers – called – Edge Nodes – in the same place where the data is generated. Only the result or aggregation of such computation will be then centralised, thus avoiding overloading the infrastructure, eliminating unnecessary latencies, 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 almost real time, depending on the production and consumption lever of users. 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 real time and only communicate with the centralised infrastructure the relevant deviations that will cause a significant impact on the network.
Types of Edge Computing
Edge computing is witnessing a significant interest with new use cases, especially after the introduction of 5G. The 2021 State of the Edge report by the Linux Foundation predicts that the global market capitalization of edge computing infrastructure would be worth more than $800 billion by 2028.
In recent years there has been a great deal of work by large corporations defining and explaining what edge computing is and its different case studies that ends with a number of definitions and classifications. All of them, group the different types of edge depending on the location in which the data processing is carried out.
When data processing is conducted at the closest point to the network and furthest from the devices, we speak of «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 avoiding unnecessary latency when the communicating devices are on the same network.
If data processing is performed on network equipment or data aggregators located in the local network itself, it is named by the «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 act as data concentrators, IoT gateways, or even intelligent industrial automation equipment.
Finally, when the processing is embedded in the IoT equipment itself, we talk 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.
The Challenges of IoT Edge Computing
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 challenges that any enterprise must consider in its design and implementation phase. The most important challenges we have identified are:
- Cybersecurity: being a network of distributed resources, in many cases unattended, connected in many cases 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.
- Scalability: especially in «Far Edge» or «Micro-Edge» computing environments, the number of devices deployed can be very large (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 way.
- Integration: the typology of connected devices, especially in the industrial world, is highly fragmented. There are no fully pervasive communications 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 effective integration and evolution of different parts with the deployed infrastructure. Monolithic, closed solutions with high integration costs should be avoided.
Industries that are benefiting the most from IoT Edge Computing
Across all sectors, industrial companies are undergoing digital transformation processes. Being able to connect devices, as well as collect and exploit this data, is becoming crucial to be competitive. The industries where IoT Edge can have the most impact are those that work with a high volume of connected devices. The impact is also exponentially greater where these devices are in distributed geographies and generate data at high frequencies.
In this sense, they are clearly positioned:
- Utilities: Business continuity is key for the critical electricity, gas or water services 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 allows real-time analysis, with processing much closer to the asset, which means much less dependence on connectivity and better response times.
- Renewable energy: Edge computing can have a big impact on the sustainable management of limited renewable energy resources, such as solar and wind power. Again, in a remote and highly distributed environment, it can avoid high dependency on connectivity and provide and robustness and securit 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 Grid: 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.
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- 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 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 so well in cloud environments.
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