Industry 4.0

Prerequisite for fast IoT

| Author / Editor: Martin Klapdor / Steffen Donath

Fast processing of huge amounts of data in the cloud? The latency of edge computing is much shorter.
Fast processing of huge amounts of data in the cloud? The latency of edge computing is much shorter. (Source: Markus Spiske/Unsplash)

The cloud is less suitable for processing large amounts of data in real time. Companies in industrial production should rely on edge computing, which reduces latency times.

According to McAfee’s third annual cloud report, 96 % of all German companies already use cloud services. According to the industry association Bitkom, they rely particularly on cloud-based office software (46 %), security solutions (44 %) and groupware such as e-mail and calendars (35 %). The cloud concept, however, is less suitable when large amounts of data have to be processed in real time. If, for example, production machines have to make decisions quickly and independently, their data should be processed decentrally and not first transferred to the cloud. In addition, decentralised data processing is a fundamental prerequisite, especially for autonomous and networked driving.

The analysis-relevant data can only be transmitted with a good connection and sufficient bandwidth. In addition, even with a good connection, the latency time for LTE would be significantly too long for use in road traffic. The computer-controlled vehicle must be able to react to an unexpected event with lightning speed – the time required to transfer data from the point of generation to the point of processing, i.e. to the cloud, and back again, is definitely not available. The process of data transmission can also be a gateway for cyber criminals.

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Edge computing can help in these cases. This is due to the huge amounts of data that are processed directly at the edge of the network. Meaning they are not first transferred to a data centre and then back again, but are analysed and utilised exactly where they are produced. The resulting shortened latency time is critical for the success of autonomous driving, all 5G scenarios and industrial production and thus the greatest advantage of edge computing.

But to take full advantage of Edge Computing, it is important that network connectivity is always available across the board and reliable. A complete transparency, i.e. an end-to-end view of all networked applications, is therefore required. However, if data is not continuously processed, standardised and correlated “on the edge”, it is difficult to gain real-time insight into application and service security.

Smart data are special data that are extracted from the huge amounts of IP data. These data contain only meaningful information that can be organised and analysed for further use. If companies use Smart Data to analyse their networks, they are able to analyse all data in real time. This enables them to gain valuable insight into how applications behave within their network.

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By accessing Smart Data, organisations can make informed decisions about how to optimise their networks and applications and where to allocate capacity to improve performance. In addition, Smart Data provides increased visibility across the network. This enables organisations to identify anomalies more quickly and respond to changes that indicate threats or performance issues. Because many organisations rely on their IoT applications on the network, smart data is critical to success.

The technology of Edge Computing is still in its infancy, but the advantages cannot be denied: Increased processing speed, reduced latency and data processing even at low bandwidths. By using a smart data solution, companies are able to fully monitor their ecosystem and identify potential sources of disruption.

This article was first published by ETMM.

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