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Measurement

Excursus: Measurement technology in the IoT world​

Measurement technology plays a crucial role in the IoT world, as it forms the basis for recording and interpreting physical processes. Here are some important aspects of measurement technology to consider when collecting data from IoT devices:

Sampling rates​

  • Sampling rate: The sampling rate refers to the frequency at which data points or measured values are recorded per unit of time. A higher sampling rate enables a more detailed recording of rapid changes in a physical process.

Aliasing​

  • Nyquist-Shannon sampling theorem: This theorem states that the sampling rate must be at least twice as high as the highest frequency component of the signal to be measured in order to avoid the alias problem.

  • Aliasing: Aliasing occurs when signal frequencies are not captured correctly because the sampling rate is too low. This leads to signals being misinterpreted or distorted. To prevent aliasing, the sampling rate must be selected according to the Nyquist frequency.

Data quality and accuracy​

  • Data quality: The quality of the recorded data is crucial for reliable analyses. Factors such as noise, calibration and interference can influence data quality and must be taken into account.

  • Accuracy: The accuracy of measuring devices and sensors is of great importance. Calibration and regular maintenance are necessary to ensure precise results.

Data preparation and pre-processing​

  • Signal pre-processing: Signals can be pre-processed before storage and analysis to reduce noise, compress data or remove unwanted artifacts.

  • Data filtering: Filtering techniques can be used to eliminate unwanted frequency components or to amplify specific frequencies.

Timestamp​

  • Timestamps: Timestamps are important for tracking data in chronological order and synchronizing events. Accurate timestamps enable the correlation of data points across different sources.

Measurement technology is a key aspect in the recording and evaluation of physical processes in the IoT environment. It makes it possible to gain valuable insights from the captured data and make informed decisions. Choosing the right sampling rates, taking aliasing into account and paying attention to data quality and accuracy are crucial to obtaining reliable IoT data.