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BSI 23/30484362 DC 2023

BSI 23/30484362 DC 2023

$13.70

BS ISO/IEC 30178 Internet of Things (IoT). Data format, value and coding

Published By Publication Date Number of Pages
BSI 2023 59
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PDF Catalog

PDF Pages PDF Title
3 HORIZONTAL_STD
FUNCTION_EMC
FUNCTION_ENV
FUNCTION_QUA
FUNCTION_SAFETY
7 FOREWORD
9 Introduction
10 1 Scope
2 Normative references
3 Terms and definitions
12 4 Abbreviated terms
14 5 Background
5.1 Overview
5.1.1 General
15 5.1.2 Sensor-related data
16 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
17 5.1.7 Measurement data
5.1.8 Numerical component
5.1.9 Semantic component
5.1.10 Unit
18 5.1.11 Prefix
19 5.2 Sensor value metadata
5.2.1 General
20 5.2.2 Resolution
5.2.3 Precision (measurement)
5.2.4 Sampling time (time resolution)
5.2.5 Sample rate
21 5.2.6 Range
5.2.7 Accuracy
5.2.8 Physical quantity
5.2.9 Unit
5.2.10 Data type
22 5.2.11 Physical quantity
5.3 Physical quantities
5.3.1 Overview
5.3.2 Occurrences
5.3.2.1 Overview
23 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
24 5.4 Examples of data models
5.4.1 Overview
5.4.2 Exchange formats
5.4.3 File formats
5.4.4 Information modelling formats
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).
25 5.5 Interoperability challenges
5.5.1 System integration
26 5.5.2 Maximizing the economic value of data through reusability
5.5.3 Sensor lifecycle cost
27 5.5.4 Sensor data capital value
28 5.5.5 Scalability
30 5.5.6 Semantics
31 5.5.7 Symbol mapping
5.5.8 Sensor Semantics
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:
32 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.
33 5.5.9 Representation vs. presentation
5.5.10 Human Readability
34 5.5.11 Resolving data points
{“bn”:”urn:dev:ow:10e2073a01080063″,”u”:”Cel”,”t”:1.276020076e+09, “v”:23.5}
{“u”:”Cel”,”t”:1.276020091e+09, “v”:23.6}
36 5.5.12 Type Inference & Casting
5.5.13 Hard Coupling
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
37 5.5.16 Presentation
5.5.17 Data translation
5.5.17.1 Mapping of Formats
5.5.17.2 Mapping of Data Points
5.5.17.3 Resolving Data Points
38 5.5.17.4 Identifying Overlap
5.5.17.5 Assessing Completeness
5.5.17.6 Separation of Dependencies
39 5.5.18 Errors and quality
40 6 Core profile
6.1 Overview
41 6.2 Sensor
6.2.1 Unit
6.2.2 The “unit-word”
42 6.2.3 Dimensionless Units
 Decibel
 Drag Coefficient
 Gain
 pH
 Radian (angle)
 Refractive Index
 Strain
 Mass Fraction
 Molar Fraction
6.2.4 Range
43 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
6.3.1 Overview
6.3.2 Operations
44 6.3.3 Significant Comparison
45 7 High-level system design
7.1 Overview
46 7.2 Type safety
47 7.3 Conversion mechanics
7.3.1 Overview
7.3.2 Single-step conversion
7.3.3 Unit resolver
48 7.3.4 Magnitude resolver
7.3.5 Precision estimation
7.3.6 Time-resolution and oversampling
49 7.4 Sanity-check mechanisms
7.5 Component manufactory
7.6 Digitized specification
7.6.1 Measurement range
7.6.2 Accuracy
7.6.3 Precision
50 Annex A : Single-step Conversion
A.1 Overview
A.2 Example 1: Forward temperature conversion
51 A.3 Example 2: Forward energy conversion
53 A.4 List of unit-word value examples
55 A.5 Floating-point format
56 ANNEX B Unifying Sensor Data and Mapping with Standards
59 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).

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BSI 23/30484362 DC 2023
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