Edge Computing: Bringing Instancy, Flexibility and Security

Technology has brought many advantages to our daily lives. Whilst those advantages have been revolutionary technology needs to evolve further and there are still many areas that have to be covered. Especially, when it comes to data – the new gold – there are several technologies and frameworks that have to be developed and tested, in order to make data acquisition and processing more scalable and reliable. An emerging approach, called edge computing, addresses the problem of (high frequency) data processing and transmission in complex technological environments and makes these processes more secure and efficient.

Not only various industries, but also some of our daily life activities are enhanced by edge computing devices. Among these are for example healthcare, infrastructure, manufacturing, security, finance and the energy sector.

In our previous blog post, we have mentioned edge computing as one of the technologies that made Internet of Things (IoT) and especially Industrial Internet of Things (IIoT) possible today. Which shows a great perspective of technological advancements. The technology arises from specific needs and these needs help other technologies to develop and advance as well.

What is edge computing?

It allows to do certain data transformations and calculations directly at the “edge” – meaning, directly where the data is produced. This is opposed to having centralized server / cloud systems, where the data is gathered and all the transformations are made. Hence, edge computing can imply dozens or even hundreds of small or microscopic data-servers (edge-devices), connected through an IoT network, which execute part or even of the data-transformations on the collected data before sending the data to a central repository.

What are edge analytics?

Edge analytics are powered by edge computing technology. Essentially, this means analysing data, which is coming from IoT devices and subsequently transfer the data to a centralised location. In addition, it promises a huge potential to increase the quality of real-time data. When combined with the Cloud, edge analytics offer better management of IoT data.

Benefits of edge computing

• Provides faster connection/services

• Reliable due to the implementation of network topology

• Distribution of data to various locations/devices through a common database

• Flexibility of the data storage by hybrid cloud computing

• Security of the data by its infrastructure

• Offers low-cost solutions

The main goal of the concept is to increase efficiency on operations.

Why is edge computing needed?

Computers, operating systems, applications, user interfaces and IoT devices need stronger computing power to operate efficiently. The amount of processed data is multiplying such as the deployment of 5G which offers faster data transfer on mobile and IoT devices. Before the processing of data multiplied immensely, artificial intelligence (AI) and cloud technology were expected to automatize and increase the innovation rate through the data. To meet these requirements and provide better networking and infrastructure opportunities, edge computing is much needed.

What challenges does edge computing face?

Even though it brings a lot of advantages, it is not easy to commission and maintain the quality of the operation of edge computers. Businesses need to address the possible pain-points that might occur in the future in order not to avoid significant loss.

• For better troubleshooting during the incidents, site management operations have to be reliable to simplify the operations for edge computing

• Edge computing locations mainly have narrow or no technical expertise located on site

• Providing and optimising the capacity for the edge servers to multiple small-scale locations are harder than providing and optimising for a single location

• Edge site operations should be ongoing even when the network is down

What are the types of edge computing?

Sensor edge: Sensors collect, translate and transfer the data to the data platforms or datacentres. Time sensitive and large amount of data benefit from edge computing to perform these operations in a faster and more reliable way.

Cloud edge: The first generation of edge computing is Cloud. The prime characteristics of Cloud are being accessible through a network connection, being centralised and commonly consisting large datacentres.

Datacentre edge: Datacentre edge serves as a crucial point as many businesses transfer their physical datacentres to the Cloud. They can be commissioned for a high severity incident management or for special needs to bring flexibility.

Device edge: Each device has its specific purpose. The collected and analysed data from devices offer uninterrupted operations and advance prediction for maintenance.

Compute edge: They can also be considered as small datacentres. They are usually commissioned next to IoT devices. The purpose is decreasing the latency, having a larger bandwidth and higher efficiency.

The edge computing architecture

The way the architecture of a technology is built, defines the way it works. Architectures with advanced functionalities are more complex. However, the architecture can be explained in a simplified way.

The starting point is the edge devices. Generated data is transferred to edge servers via a secure network connection that has the minimum needed bandwidth. After stored in the edge servers, the data is sent to edge cloud where worldwide access with an active network connection is provided. Since it is mainly used by businesses, most of them prefer to have a hybrid cloud that offers more flexibility.

Edge computing VS. cloud computing:

When comparing edge computing and cloud computing, there are some similarities that can cause confusion and misunderstandings. It is important to understand the differences in edge computing bridges the gaps of cloud computing.

Cloud computing represents the usage of different services such as software, development platforms, storages, servers and other applications with an active network connection. Through the timeline, cloud technology, has invested a lot in centralised services with major datacentres.

Edge computing bridges the gap where networking requirements or other issues cause a problem. It offers instant computing power and data analysis in contrary to centralised datacentres with cloud computing . Besides the advantages of cloud computing, it can cause unnoticeable lag times. For businesses or operations every moment matters, this is why edge computing is bridging this gap ever so smoothly.

Edge computing and IoT

Edge computing and IoT technology benefit from each other and increase their efficiency. The foundations and development of edge computing reflects the need of instant processing of high-volume data generated by IoT devices. It increases the connectivity level and eliminates the latency issues. That way, edge computing multiplies the effectiveness and trustworthiness of the data generated by IoT devices. With more increased density, demand and amount of IoT devices , the need for edge computing will become more essential.

ANNEA and edge computing

Edge computing covers a huge portion on how the expertise of ANNEA is applied and delivered. Depending on the sensor type that is either already implemented or is planned to be implemented, different techniques are available.

For a better outcome, we recommend the ANNEA sensors to be implemented on the wind turbines in order to get higher resolutions of data. This enables to perform certain calculations automatically and results in increased efficiency. We analyse different types of high frequency data, which are extremely difficult to transfer. Therefore, we do the data pre-processing directly in the ANNEA sensor boxes, before it is transferred to the cloud or to our ANNEA platform.

After transferring these high volume data, it is possible to visualise and analyse them on the ANNEA Platform. Even combining them with existing data and applying different models is possible. The platform offers full control and transparency on the current and future status of your machines. The ANNEA Platform has the purpose of providing a seamless process, while combining cutting-edge technology with complex and huge data sets, ensuring that all the data security measurements are in place.

Contact the ANNEA Expert Team to learn more about our tailor-made approach!

What is next?

As we move towards our process of how ANNEA delivers its unique solutions, we hope you get more insights the more you read. Our next topic will be our famous Automated Predictive Engine.

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