What Is Edge Computing? The Revolutionary Data Processing Technique

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 to cover. This is especially true 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.

Edge computing enhances not only various industries, but also some of our daily activities. Among these are healthcare, infrastructure, manufacturing, security, finance and the energy sector.

In unserem letzten Blogbeitrag, we mentioned edge computing as one of the technologies that made Internet of Things (IoT) and especially Industrial Internet of Things (IIoT) possible today. This 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 centralised server/cloud systems, where the data is gathered and all the transformations are made. Hence, edge computing can employ 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 comes from IoT devices, and subsequently transferring 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.

THE BENEFITS

• It provides faster connection/services.
• It is reliable due to the implementation of network topology.
• Distribution of data to various locations/devices through a common database is possible.
• There is flexibility in the data storage via hybrid cloud computing.
• Security of the data by its infrastructure is possible.
• It offers low-cost solutions.

The main goal of edge computing is to increase efficiency of operations.

WHY IS IT 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 automate 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 IT FACE?

Even though it brings a lot of advantages, edge computing is not easy to commission and it is difficult to 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: Sensoren sammeln, übersetzen und übertragen die Daten an die Datenplattformen oder Rechenzentren. Zeitsensible und große Datenmengen profitieren vom Edge Computing, um diese Vorgänge schneller und zuverlässiger durchzuführen.

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.

Data centre edge: Data centre edge serves as a crucial point as many businesses transfer their physical data centres 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 small data centres. They are usually commissioned next to IoT devices. Their purpose is to decrease the latency, and have 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 an edge servers, the data is sent to the 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 that edge computing supports cloud computing, but they are not the same thing.

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 data centres.

Edge computing supports where networking requirements or other issues cause a problem. It offers instant computing power and data analysis in contrary to centralised data centres with cloud computing . Besides the advantages of cloud computing, its lag times are practically. For businesses or operations, where every moment matters, edge computing is smoothing out these issues.

EDGE COMPUTING AND IOT

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

HOW ANNEA FITS IN

Edge computing is a huge part of how the expertise of ANNEA angewendet und bereitgestellt wird. Je nach Sensortyp, der entweder bereits implementiert ist oder noch implementiert werden soll, stehen verschiedene Techniken zur Verfügung.

Um ein besseres Ergebnis zu erzielen, empfehlen wir, die ANNEA-Sensoren auf den Windkraftanlagen zu installieren, um eine höhere Auflösung der Daten zu erhalten. Dadurch können bestimmte Berechnungen automatisch durchgeführt werden, was zu einer höheren Effizienz führt. Wir analysieren verschiedene Arten von Hochfrequenzdaten, die extrem schwierig zu übertragen sind. Daher führen wir die Vorverarbeitung der Daten direkt in den ANNEA-Sensorboxen durch, bevor sie in die Cloud oder auf unsere ANNEA-Plattform übertragen werden.

After transferring this high volume data, it is possible to visualise and analyse it 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.

Kontaktieren Sie das ANNEA-Expertenteam , um mehr über unseren maßgeschneiderten Ansatz zu erfahren!

WHAT’S NEXT?

Wir hoffen, dass Sie je mehr Sie lesen, desto mehr Einblicke erhalten, wie ANNEA seine einzigartigen Lösungen bereitstellt. Unser nächstes Thema wird unsere berühmte Automated Predictive Engine sein.