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23/30484362 DC BS ISO/IEC 30178 Internet of Things (IoT). Data format, value and coding, 2023
- JTC1-SC41_392e_CD.pdf [Go to Page]
- FOREWORD
- Introduction
- 1 Scope
- 2 Normative references
- 3 Terms and definitions
- 4 Abbreviated terms
- 5 Background [Go to Page]
- 5.1 Overview [Go to Page]
- 5.1.1 General
- 5.1.2 Sensor-related data
- 5.1.3 Source data collection
- 5.1.4 Time-resolution and sample rate
- 5.1.5 Signal-processing implications
- 5.1.6 Low-power sensors
- 5.1.7 Measurement data
- 5.1.8 Numerical component
- 5.1.9 Semantic component
- 5.1.10 Unit
- 5.1.11 Prefix
- 5.2 Sensor value metadata [Go to Page]
- 5.2.1 General
- 5.2.2 Resolution
- 5.2.3 Precision (measurement)
- 5.2.4 Sampling time (time resolution)
- 5.2.5 Sample rate
- 5.2.6 Range
- 5.2.7 Accuracy
- 5.2.8 Physical quantity
- 5.2.9 Unit
- 5.2.10 Data type
- 5.2.11 Physical quantity
- 5.3 Physical quantities [Go to Page]
- 5.3.1 Overview
- 5.3.2 Occurrences [Go to Page]
- 5.3.2.1 Overview
- 5.3.2.2 Sensory data
- 5.3.2.3 Documentation
- 5.3.2.4 Geographic information system (GIS) data
- 5.3.2.5 Building information models (BIM)
- 5.3.2.6 Machine learning & predictive maintenance
- 5.4 Examples of data models [Go to Page]
- 5.4.1 Overview
- 5.4.2 Exchange formats
- 5.4.3 File formats
- 5.4.4 Information modelling formats [Go to Page]
- Information Modelling is more about creating efficient ways of representing complex data in a consistent way. It is not just about encoding the structure of geometric data, but also its logical organization.
- In applications where complex information needs to inform how data should be interpreted, information modelling standards (such as OPC-UA) constitute the semantic and numeric building blocks for a vast number of applications.
- The purpose is to allow for encoding meaning within data, so that every system that implements the same information model can handle data with a higher semantic weight (which typically requires a more complex interpretation).
- 5.5 Interoperability challenges [Go to Page]
- 5.5.1 System integration
- 5.5.2 Maximizing the economic value of data through reusability
- 5.5.3 Sensor lifecycle cost
- 5.5.4 Sensor data capital value
- 5.5.5 Scalability
- 5.5.6 Semantics
- 5.5.7 Symbol mapping
- 5.5.8 Sensor Semantics [Go to Page]
- In most data models that involve sensors, some data points are always present – both for the data, and the metadata.
- Two examples that have already been mentioned as such are the unit and prefix of the measurement sample. There are an endless number of variations of how to represent unit and prefix alone.
- Consider the following (plausible) variations of how the identical length 100 micrometer can be represented when serialized:
- Just by varying how to structure and represent the essential data of a numeric value with a prefix and a unit, a massive variation of representation can emerge.
- Excluding the remaining 17 prefixes and their equivalent base-10 values, possible variation in numerical precision and range – it is impossible to resolve any randomly selected variation into a data type that can be consistently compared (correctly) without a significant number of assumptions about the format.
- Computers can apply mathematics on numerical values to compare them, but they can’t apply mathematics to text. This means that for a computer to be able to mathematically compare different representations, it cannot do so on pure cleartext.
- The cleartext “number” therefore has to be resolved to a numerical variable first.
- It isn’t enough to convert just the first numerical component, though. The prefix is a symbol for another numerical value, which must therefore also be merged with the main numerical value in order to be resolved.
- Adding a prefix is useful for human readability, but only adds a semantic degree of freedom to a computer. When considering all the different variations on spelling for the prefix, it adds another degree of freedom that any integration needs to handle between different representations. In terms of resolving a representation to magnitude and unit, the prefix ultimately only corresponds to a base-10 exponent that is implicitly multiplied with the numerical value.
- The insight that can be gained from this example is that, from an interoperability perspective, supplementing numerical values with prefixes give rise to very large range of variation that, for any random integration, adds to the required workload for resolving a value and its unit.
- Furthermore, trying to predict (at design-time) which particular variation that one will need to translate to and from becomes unrealistic.
- It can also be concluded that semantic degrees of freedom diminish interoperability.
- 5.5.9 Representation vs. presentation
- 5.5.10 Human Readability
- 5.5.11 Resolving data points [Go to Page]
- {"bn":"urn:dev:ow:10e2073a01080063","u":"Cel","t":1.276020076e+09, "v":23.5}
- {"u":"Cel","t":1.276020091e+09, "v":23.6}
- 5.5.12 Type Inference & Casting
- 5.5.13 Hard Coupling [Go to Page]
- If it doesn’t, and quality guarantees have to be made, the necessary review of the data source’s characteristics create a hard coupling that negates the benefits of exchange formats that apply representation abstractions, such as text-based data.
- 5.5.14 Machine-to-machine Exchange
- 5.5.15 Representation
- 5.5.16 Presentation
- 5.5.17 Data translation [Go to Page]
- 5.5.17.1 Mapping of Formats
- 5.5.17.2 Mapping of Data Points
- 5.5.17.3 Resolving Data Points
- 5.5.17.4 Identifying Overlap
- 5.5.17.5 Assessing Completeness
- 5.5.17.6 Separation of Dependencies
- 5.5.18 Errors and quality
- 6 Core profile [Go to Page]
- 6.1 Overview
- 6.2 Sensor [Go to Page]
- 6.2.1 Unit
- 6.2.2 The “unit-word”
- 6.2.3 Dimensionless Units [Go to Page]
- Decibel
- Drag Coefficient
- Gain
- pH
- Radian (angle)
- Refractive Index
- Strain
- Mass Fraction
- Molar Fraction
- 6.2.4 Range [Go to Page]
- Some sensors may use sub-components that are also sensors. In this case, measurement values that are passed without transformation of any kind from a child component to a parent component will also need to export their range intact.
- Ranges may also be associated with some physical criteria that always holds true for some set of physical conditions, such as the boiling point of water at room temperature and standard atmospheric pressure.
- For instance, if a sensor measures a water temperature outside the range 0-100 degrees Celsius (i.e. 273.15-373.15 kelvin), the water can no longer be assumed to be in its liquid state.
- In the case of more domain-specific ranges, the receiver of measurement data may not be in possession of the contextual facts of the sensor in question. It may therefore be necessary for the implementer to share the bounds of this range.
- This will enable exceptional states to be well-defined in terms of an already known variable.
- 6.3 Physical quantity [Go to Page]
- 6.3.1 Overview
- 6.3.2 Operations
- 6.3.3 Significant Comparison
- 7 High-level system design [Go to Page]
- 7.1 Overview
- 7.2 Type safety
- 7.3 Conversion mechanics [Go to Page]
- 7.3.1 Overview
- 7.3.2 Single-step conversion
- 7.3.3 Unit resolver
- 7.3.4 Magnitude resolver
- 7.3.5 Precision estimation
- 7.3.6 Time-resolution and oversampling
- 7.4 Sanity-check mechanisms
- 7.5 Component manufactory
- 7.6 Digitized specification [Go to Page]
- 7.6.1 Measurement range
- 7.6.2 Accuracy
- 7.6.3 Precision
- Annex A : Single-step Conversion [Go to Page]
- A.1 Overview
- A.2 Example 1: Forward temperature conversion
- A.3 Example 2: Forward energy conversion
- A.4 List of unit-word value examples
- A.5 Floating-point format
- ANNEX B Unifying Sensor Data and Mapping with Standards
- RFC 8428: Sensor Measurement Lists (SenML), https://datatracker.ietf.org/doc/rfc8428/
- ISO/IEC 1539-1:202n Information technology — Programming languages — Fortran — Part 1: Base language
- ISO/IEC 60559 Floating-point arithmetic
- ISO/IEC CD 11404 - Information technology — General-Purpose Datatypes (GPD)
- SAREF-Compliant Knowledge Discovery for Semantic Energy and Grid Interoperability. Amelie Gyrard, Antonio Kung, Olivier Genest, Alain Moreau. IEEE World Forum on Internet of Things (WF-IoT 2021).
- Word Bookmarks [Go to Page]
- HORIZONTAL_STD
- FUNCTION_EMC
- FUNCTION_ENV
- FUNCTION_QUA
- FUNCTION_SAFETY [Go to Page]
