IoT Glossary

The Internet of Things is already ubiquitous. Everyone speaks of the fact that things are connected with each other via sensors and that data is exchanged and shared. This can result in new revenue streams and business models. Basically, the data is recorded at various data points and merged via a cloud platform. On the way there, the large amounts of data can be exported via protocols or other interfaces.

In the following, we explain some terms and applications from the world of IoT that are used in our solutions:

IoT | IIoT

The Internet of Things (IoT), describes a network of “intelligent” objects that communicate with each other and to the outside world and are able to carry out processes and tasks automatically. Examples of such objects from everyday life are smartwatches, assistance systems in cars and smart home devices.


Anomaly Detection

Anomaly detection is a method of security in the context of IT and OT. In contrast to common security solutions, the anomaly detection is not limited to the detection of known threats. The aim of the method is to detect any anomaly in a network.

An anomaly describes any change in the permitted and known standard communication in networks. An anomaly can therefore include malware or cyberattacks as well as faulty data packets and changes in communication caused by network problems, capacity bottlenecks or system errors. This enables a holistic fault defense and guarantees complete digital transparency.


Condition Monitoring

Condition monitoring is about continuously monitoring the technical condition of a machine. Data is continuously acquired, collected, transmitted, evaluated and compared. For this purpose, physical quantities, for example temperatures, speeds, pressure, fill levels or vibrations, are measured and the values ​​determined are analyzed. Condition monitoring helps to better understand the machines, to quickly notice changes, such as progressive signs of wear and tear on individual components, and to be able to better coordinate machine maintenance. The method is intended to increase both machine efficiency and safety.


Digital Twin

A digital twin is a virtual model of a process, a product or a service that connects the real and virtual world. Digital twins use real data from installed sensors, which represent the working conditions or the position of machines. This combination of the virtual and real worlds enables the analysis of data and the monitoring of systems, such as understanding and processing problems before they even occur, avoiding downtimes, developing new opportunities and using computer simulations (simulation) to plan the future.



The word AI is an abbreviation and stands for artificial intelligence. Artificial intelligence describes technologies that imitate cognitive skills that only humans were previously capable of. This includes, for example, strategic thinking or language skills. In this way, employees can often be supported in repetitive and time-consuming activities that previously required a lot of attention. The potential of IoT solutions can only be fully explored with artificial intelligence.


Machine Learning

Machine learning represents a sub-area of ​​artificial intelligence. With the help of machine learning, IT systems are enabled to recognize patterns and laws on the basis of existing databases and algorithms to develop solutions. Artificial knowledge is generated from experience. The knowledge gained from the data can be generalized and used for new problem solutions or for the analysis of previously unknown data.


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