Which challenges in the laboratory are solved with data standards?
Posted: Sat Jan 25, 2025 4:08 am
To compensate for the lack of authentication with MQTT-SN (or CoAP), DTLS-PSK can also be used here. To establish the secure connection, correct credentials known to the server must be transmitted in the form of a client identifier and a pre-shared key. Incoming MQTT-SN messages can then be assigned to the respective device on the cloud side based on the client identifier used.
The number of DTLS implementations currently available is manageable, especially on the server side: the existing ones are at best in an experimental stage or are no longer being actively developed. However, the Eclipse project Scandium, developed in the Java programming language, is a positive example of a DTLS implementation that supports a large number of cipher suites and is being actively developed. This implementation forms the basis for the CoAP implementation Californium and is therefore used on a larger scale.
Especially when battery-operated devices are used, the use of encryption algorithms implemented in hardware is essential not only because of the higher processing speed, but above all because of the cyprus consumer email list lower energy consumption.
The era of digitalization means that the amount of data available is increasing rapidly. To avoid problems, good communication between the entities is the key to managing data. Data standards play a major role here, as they are the solution to several data problems in the laboratory. However, the lack of data standards can cause problems in the future, as in today's complex laboratory landscape, data exchange must become more efficient and faster so that laboratories can secure their existence in the market.
Data standards can help solve many of the problems that laboratories face due to the tremendous growth of knowledge and data. Data standards also help to share information more easily, efficiently and transparently.
Why are data standards necessary?
There are several reasons why data standards are needed today. Some of the most important are¹:
There is an enormous growth of knowledge and data
There is a desire for information to be shared openly and transparently
Digitalisation plays an important role here as it permeates all areas of business, science and society and is one of the main drivers of innovation and international cooperation
Aside from the need for efficient data exchange and global collaboration, the complexity of laboratories is constantly increasing. Therefore, designing efficient data exchange is not only desired, but often necessary. Some of the challenges that laboratories have to contend with today are:
Incompatibility: Within a laboratory, these problems can arise due to the large number of different devices and software that are used on a daily basis. In addition, communication between laboratories or systems is important, so incompatibility makes the exchange and merging of data difficult.
Data loss: If data cannot be interpreted correctly because it is in different formats or because information is misinterpreted during transmission, data is lost. This affects the quality and reliability of the results.
Difficulties in data analysis: A large number of different formats and structures make the analysis and evaluation of data difficult.
Lack of reproducibility: One possible reason for the non-reproducibility of experiments is that uniform documentation was not possible.
The number of DTLS implementations currently available is manageable, especially on the server side: the existing ones are at best in an experimental stage or are no longer being actively developed. However, the Eclipse project Scandium, developed in the Java programming language, is a positive example of a DTLS implementation that supports a large number of cipher suites and is being actively developed. This implementation forms the basis for the CoAP implementation Californium and is therefore used on a larger scale.
Especially when battery-operated devices are used, the use of encryption algorithms implemented in hardware is essential not only because of the higher processing speed, but above all because of the cyprus consumer email list lower energy consumption.
The era of digitalization means that the amount of data available is increasing rapidly. To avoid problems, good communication between the entities is the key to managing data. Data standards play a major role here, as they are the solution to several data problems in the laboratory. However, the lack of data standards can cause problems in the future, as in today's complex laboratory landscape, data exchange must become more efficient and faster so that laboratories can secure their existence in the market.
Data standards can help solve many of the problems that laboratories face due to the tremendous growth of knowledge and data. Data standards also help to share information more easily, efficiently and transparently.
Why are data standards necessary?
There are several reasons why data standards are needed today. Some of the most important are¹:
There is an enormous growth of knowledge and data
There is a desire for information to be shared openly and transparently
Digitalisation plays an important role here as it permeates all areas of business, science and society and is one of the main drivers of innovation and international cooperation
Aside from the need for efficient data exchange and global collaboration, the complexity of laboratories is constantly increasing. Therefore, designing efficient data exchange is not only desired, but often necessary. Some of the challenges that laboratories have to contend with today are:
Incompatibility: Within a laboratory, these problems can arise due to the large number of different devices and software that are used on a daily basis. In addition, communication between laboratories or systems is important, so incompatibility makes the exchange and merging of data difficult.
Data loss: If data cannot be interpreted correctly because it is in different formats or because information is misinterpreted during transmission, data is lost. This affects the quality and reliability of the results.
Difficulties in data analysis: A large number of different formats and structures make the analysis and evaluation of data difficult.
Lack of reproducibility: One possible reason for the non-reproducibility of experiments is that uniform documentation was not possible.