ETSI TS 103 779 V1.1.1 (2022-05)

SmartM2M; Requirements and Guidelines for cross-domain data usability of IoT devices

DTS/SmartM2M-103779

General Information

• Status: Not Published
• Technical Committee: SmartM2M - Smart M2M
• Current Stage: 12 - Completion
• Due Date: 01-Jun-2022
• Completion Date: 19-May-2022

Standards Content (Sample)

ETSI TS 103 779 V1.1.1 (2022-05)






TECHNICAL SPECIFICATION
SmartM2M;
Requirements and Guidelines for cross-domain
data usability of IoT devices

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2 ETSI TS 103 779 V1.1.1 (2022-05)

Reference
DTS/SmartM2M-103779
Keywords
artificial intelligence, data usability, IoT, oneM2M,
use case

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All rights reserved.

ETSI

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Contents
Intellectual Property Rights . 4
Foreword . 4
Modal verbs terminology . 4
Introduction . 4
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 8
3.1 Terms . 8
3.2 Symbols . 8
3.3 Abbreviations . 8
4 Recommendations for data usability . 9
5 Requirements and guidelines for preserving data usability . 11
5.1 General considerations . 11
5.2 Service requirements . 12
5.2.1 Requirements to be fulfilled by sensor/data sources . 12
5.2.2 Requirements to be fulfilled by IoT platform . 12
5.2.3 Requirements to be fulfilled by AI/ML or monitoring function . 13
5.2.4 Requirements to be fulfilled by operator of system . 13
5.2.5 Requirements to be fulfilled by data users . 13
5.3 Operational requirements . 14
5.3.1 Requirements to be fulfilled by sensor/data sources . 14
5.3.2 Requirements to be fulfilled by IoT platform . 14
5.3.3 Requirements to be fulfilled by AI/ML or monitoring function . 14
5.3.4 Requirements to be fulfilled by operator of system . 15
5.3.5 Requirements to be fulfilled by user of data . 15
6 Conclusion . 15
Annex A (informative): Challenges in adopting the guidelines and about the integration of
such guidelines within automatic validation systems. 17
A.0 Introduction . 17
A.1 Interoperability . 17
A.2 Collecting data from sensors . 18
A.3 Granularity . 20
A.4 Traceability . 21
A.4.1 Logging . 21
A.4.2 File-Based Traceability Recommendation . 21
A.4.3 Distributed Ledger Recommendation . 22
A.4.4 Streaming-Data Packages Recommendation . 22
History . 23


ETSI

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4 ETSI TS 103 779 V1.1.1 (2022-05)
Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI Web server (https://ipr.etsi.org/).
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
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not constitute an endorsement by ETSI of products, services or organizations associated with those trademarks.
DECT™, PLUGTESTS™, UMTS™ and the ETSI logo are trademarks of ETSI registered for the benefit of its

Members. 3GPP™ and LTE™ are trademarks of ETSI registered for the benefit of its Members and of the 3GPP
Organizational Partners. oneM2M™ logo is a trademark of ETSI registered for the benefit of its Members and of the
®
oneM2M Partners. GSM and the GSM logo are trademarks registered and owned by the GSM Association.
Foreword
This Technical Specification (TS) has been produced by ETSI Technical Committee Smart Machine-to-Machine
communications (SmartM2M).
Modal verbs terminology
In the present document "shall", "shall not", "should", "should not", "may", "need not", "will", "will not", "can" and
"cannot" are to be interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of
provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Introduction
The growth of the number of IoT devices and the data generated by those devices has led to the need to process a large
amount of data. Processing such large amounts of data has become more challenging and has led to increased use of
automated processing such as machine learning. Effective use of this data for decisions has been seen to depend on the
quality of information used for modelling and how the systems work and interact together. The use of machine learning
to process data has led to a debate on data gathering, data ownership, data transparency, and data bias that is going well
beyond technical matters (privacy, regulation, remuneration schemes). The (negative) impact of poor-quality training
data is obvious, especially in health applications, road travel, etc.
IoT devices and platforms also provide data that are used directly by human and very often non-technical users. This is
the case for example for medical teams and their patients in the medical sector, mechanics in the automotive sector or
first responders in the emergency sector. Trust in the IoT system can be ensured only if these data bring in a real
added-value and are delivered in a non-ambiguous manner to these users.
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5 ETSI TS 103 779 V1.1.1 (2022-05)
In AI, in many cases, the source of poor or incorrect results is because of machine learning models that have biased
outputs which can be traced back to lack of sufficient or poorly classified training data. Developing trained models is
time and compute intensive and poor data used in training can results in the need to retrain which can take time (and
therefore money). Models based on poor data can have unintended consequences from incorrectly classifying new data
that can lead to expensive failures or negative social outcomes. As they become used in more critical use cases the
results can be catastrophic, such as could be the case of failure in an autonomous vehicle. Similar impact would arise
from poor data when IoT devices provide information to non-technical persons or to monitoring algorithms.
Creating more accurate machine learning models can be greatly enhanced by improving the quality and quantity of
classified training sets. To emphasize the point, it is not a lack of data but the lack of classified data that impacts the
machine learning algorithms. Recommendations in the present document include clearly describing the generated data
at all stages of a machine learning pipeline, including:
• a description of the data from an IoT sensor with a common ontology
• a description of the environment the sensor data was collected with a common ontology
• storage in manner that makes the collected data shareable and discoverable
• classification of the data (either manually or by machine learning algorithms) with a common ontology
• traceability of all the sources of classification
The recommendations captured in the present document address the full machine learning pipeline. For maximum
benefit the entire system should apply these recommendations, but each individual component or actor in the system
can implement the relevant guidelines to provide a better outcome for the usability of the data generated from sensors
and machine learning based solutions.
The intended audience of the present document are IoT sensor module developers, IoT platform and service providers,
machine learning model developers, application developers and IoT consumers.
IoT sensor module developers are at the start of the pipeline and improvements in the characterization of data generated
by the sensor can have a significant impact on its use throughout the pipeline. The data generated should be described
with an appropriate common ontology that will make discovery and use of the data easier.
IoT platform and service providers can make data easily available and easy to annotate with the information needed for
proper utilization through the lifecycle of the data generation, classification, and consumption.
Machine Learning algorithm developers will be able to find good data easier and subsequently they will generate better
models. Additionally, machine learning models should generate labels with an appropriate common ontology that will
make discovery and use of the data easier.
Application developers will be able to find and use machine learning models that are relevant to their application use
case and know exactly what data is suitable for a discovered model and which models are suitable for their data sources.
IoT consumers in this context are those intending to make use of a solution that includes an IoT device or system. These
recommendations can be used as a checklist for any solution considered for deployment.

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6 ETSI TS 103 779 V1.1.1 (2022-05)
1 Scope
The present document has the objective:
• to define minimum requirements for data and services usability on professional and general public IoT devices
and platforms, whether they are critical or not;
• to develop a horizontal cross-domain specification encompassing these requirements.
2 References
2.1 Normative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
Referenced documents which are not found to be publicly available in the expected location might be found at
https://docbox.etsi.org/Reference.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are necessary for the application of the present document.
[1] ETSI TS 103 264: "SmartM2M; Smart Applications; Reference Ontology and oneM2M
Mapping".
[2] ETSI EN 303 645: "CYBER; Cyber Security for Consumer Internet of Things: Baseline
Requirements".
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI TR 103 778:"SmartM2M; Use cases for cross-domain data usability of IoT devices".
[i.2] E Goldstein, U Gasser, and B Budish: "Data Commons Version 1.0: A Framework to Build
Toward AI for Good", 2018.
NOTE: Available at https://medium.com/berkman-klein-center/data-commons-version-1-0-a-framework-to-build-
toward-ai-for-good-73414d7e72be (Accessed 15 November 2021).
[i.3] 3GPP TS 22.891 (V14.2.0): "Technical Specification Group Services and System Aspects;
Feasibility Study on New Services and Markets Technology Enablers", September 2016.
[i.4] 3GPP R1-162204: "Numerology requirements", April 2016.
[i.5] M Chen, Y Miao, Y Hao, and K Hwang: "Narrow band internet of things", IEEE Access, vol. 5,
pp. 20557-20577, 2017.
[i.6] Z He: "Automatic cooking system", US Patent App. 16/155,895, February 2019.
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[i.7] F Adelantado, X Vilajosana, P Tuset-Peiro, B Martinez, J Melia-Segui, and T Watteyne:
"Understanding the limits of lorawan", IEEE Communications Magazine, vol. 55, pp. 34-40,
September 2017.
[i.8] C Yi, J Cai, and Z Su: "A multi-user mobile computation offloading and transmission scheduling
mechanism for delay-sensitive applications", IEEE Transactions on Mobile Computing, 2019.
[i.9] A Pal and K Kant: "Nfmi: Connectivity for short-range iot applications", Computer, vol. 52,
pp. 63-67, February 2019.
[i.10] M Merry: "Environmental problems that batteries cause", Sciencing, March 2019.
[i.11] A Froytlog, T Foss, O Bakker, G Jevne, M A Haglund, F Y Li, J Oller, and G Y Li: "Ultra-low
power wake-up radio for 5g iot", IEEE Communications Magazine, vol. 57, no. 3, pp. 111-117,
2019.
[i.12] Z Qin, F Y Li, G Y Li, J A McCann, and Q Ni: "Low-power wide-area networks for sustainable
iot", IEEE Wireless Communications, 2019.
[i.13] B Safaei, A M H Monazzah, M B Bafroei, and A Ejlali: "Reliability side-effects in internet of
nd
things application layer protocols", in 2 International Conference on System Reliability and
Safety (ICSRS), pp. 207-212, IEEE, 2017.
[i.14] N A Mohammed, A M Mansoor, and R B Ahmad: "Mission-critical machine-type
communications: An overview and perspectives towards 5G", IEEE Access, 2019.
[i.15] M B Mollah, S Zeadally, and M A K Azad: 'Emerging wireless technologies for internet of things
applications: Opportunities and challenges', 2019.
[i.16] J Wu and P Fan: "A survey on high mobility wireless communications: Challenges, opportunities
and solutions", IEEE Access, vol. 4, pp. 450-476, 2016.
[i.17] M Ryu, J Yun, T Miao, I-Y Ahn, S-C Choi, and J Kim: "Design and implementation of a
connected farm for smart farming system', in IEEE SENSORS, pp. 1-4, IEEE, 2015.
[i.18] L F Ochoa, G P Harrison: "Minimizing energy losses: optimal accommodation and smart
operation of renewable distributed generation", IEEE Trans Power Syst, 26 (1), pp. 198-205, 2011.
[i.19] T Hedberg Jr, S Krima, J A Camelio: "Embedding X.509 digital certificates in three-dimensional
models for authentication, authorization, and traceability of product data", Journal of Computing
and Information Science in Engineering 17(1):11008-11011, 2016.
NOTE: Available at https://doi.org/10.1115/1.4034131.
[i.20] T Hedberg Jr, S Krima, J A Camelio: "Method for enabling a root of trust in support of product
data certification and traceability", Journal of Computing and Information Science in Engineering
19(4):041003, 2019.
NOTE: Available at https://doi.org/10.1115/1.4042839.
[i.21] D Yaga, P Mell, N Roby, K Scarfone: "Blockchain technology overview", National Institute of
Standards and Technology, Gaithersburg, MD, 2018.
NOTE: Available at https://doi.org/10.6028/NIST.IR.8202.
[i.22] S Krima, T Hedberg Jr, A Barnard Feeney: "Securing the digital threat for smart manufacturing",
National Institute of Standards and Technology, Gaithersburg, MD, AMS 300-6, 2019.
NOTE: Available at https://doi.org/10.6028/NIST.AMS.300-6.
[i.23] D Wu, M J Greer, D W Rosen, D Schaefer: "Cloud manufacturing: Strategic vision and state-of-
the-art", Journal of Manufacturing Systems 32(4):564-579, 2013.
NOTE: Available at https://doi.org/10.1016/j.jmsy.2013.04.008.
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[i.24] X Vincent Wang, X W Xu: "An interoperable solution for cloud manufacturing", Robotics and
Computer-Integrated Manufacturing 29(4):232-247, 2013.
NOTE: Available at https://doi.org/10.1016/j.rcim.2013.01.005.
[i.25] L Zhang, Y Luo, F Tao, B H Li, L Ren, X Zhang, H Guo, Y Cheng, A Hu, Y Liu: "Cloud
manufacturing: a new manufacturing paradigm", Enterprise Information Systems 8(2):167-187,
2014.
NOTE: Available at https://doi.org/10.1080/17517575.2012.683812.
[i.26] L Ren, L Zhang, L Wang, F Tao, X Chai: "Cloud manufacturing: key characteristics and
applications", International Journal of Computer Integrated Manufacturing 30(6):501-515, 2017.
NOTE: Available at https://doi.org/10.1080/0951192X.2014.902105.
[i.27] High Priority IoT Standardisation Gaps and Relevant SDOs, Release 2.0, Alliance for Internet of
Things Innovation (AIOTI), January 2020.
NOTE: Available at https://aioti.eu/wp-content/uploads/2020/01/AIOTI-WG3-High-Priority-Gaps-v2.0-200128-
Final.pdf.
[i.28] ETSI TR 103 582: "EMTEL; Study of use cases and communications involving IoT devices in
provision of emergency situations".
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in ETSI TR 103 778 [i.1] and the following apply:
data consumer: AI, monitoring algorithm or human that uses the data provided by an IoT platform or device
NOTE: After the data consumer has used the data, they remain available for further usage.
ML algorithms: specific algorithms used to analyse data as well as any pre-processing or post-processing performed
on the data before use in the ML algorithm
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
th th
4G/5G 4 /5 Generation (mobile networks)
AI Artificial Intelligence
AI/ML Artificial Intelligence/Machine Learning
AIOTI Alliance for Internet of Things Innovation
API Application Programming Interface
CPU Central Processing Unit
CSV Comma Separated Value
DCAT Data CATalogue vocabulary
HTTP Hyper Text Transfer Protocol
IoT Internet of Things
IP Intellectual Property
JSONLD JavaScript Object Notation for Linked Data
MIMO Multiple-Input and Multiple-Output
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ML Machine Learning
mMIMO massive MIMO
NOMA Non-Orthogonal Multiple Access
RDF Resource Description Framework
ROI Return Of Investment
SAREF Smart Applications REFerence ontology
SDMX Statistical Data and Metadata eXchange
SKOS Simple Knowledge Organization System
UAV Unmanned Aerial Vehicle
URI Universal Resource Identifier
4 Recommendations for data usability
ETSI TR 103 778 [i.1] identifies and describes use cases where the IoT data and services require data usability for
humans and for machines consuming data for AI (for example machine learning). The data that IoT devices and
platforms provide should be easily accessed to all authorized users, understood and acted upon by a large non-technical
public in the case of humans (e.g. medical teams and their patients in the medical sector, mechanics in the automotive
sector, first responders in the emergency sector, etc.) and by machines and processes when the data are fed to the AI
components of a system (e.g. machine learning). Its main objective is to analyse these use cases to derive requirements
and guidelines towards a horizontal cross-domain standard, with the specification of minimum requirements for data
usability of professional and general public IoT services, whether they are critical or not. In that aim, ETSI
TR 103 778 [i.1] analyses the impact of these use cases from the data usability point of view for both machines
(algorithms and AI/ML) and humans. The present document fulfils one of the standardization gaps identified in the
AIOTI report published in 2020 [i.27]. It also includes part of the recommendations that were produced in the use case
analysis of ETSI TR 103 582 [i.28].
Potential solutions build up a list of what can mitigate the identified issues with the intent of decreasing the likelihood
of these issues. Each use case has been analysed again to determine which potential solutions could be applied and then
identify the residual impact assessment, with a goal to have the minimal residual impacts for each use case.

Figure 1: Link between the use cases and the specifications
This clause contains a summary describing the major points of attention to consider when an AI system is deployed. It
provides a table describing a list of recommendations grouped by type and, for each of them, the recommendation that
may be addressed to handle some of the impact to issues raised under the use cases that have been described in ETSI
TR 103 778 [i.1]. The aim of this clause is to connect the outcomes of the work performed in ETSI TR 103 778 [i.1]
with the set of requirements provided in clause 5.
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10 ETSI TS 103 779 V1.1.1 (2022-05)
Table 1: Summary of recommendations in ETSI TR 103 778 [i.1]
Category Recommendation Description
Setup IoT infrastructure/devices Easy way for sensor data to be directed to a data consumer
bootstrap. (human or ML algorithm).
Each deployed IoT infrastructure/device has to be properly
setup in order to grant an efficient and effective flow of involved
data. During the bootstrap operation it is necessary to check if
all data gathered by sensors are easily provided to the target
data consumers. Target data consumers may be either humans
or ML algorithms.
Data format description and Data formats used within a deployed IoT infrastructure/device
intelligibility. have to be properly described in order to avoid ambiguity for the
target data consumers using such data. Target data consumers
may be either humans or ML algorithms.
Configuration Mitigation of data heterogeneity. A complex IoT infrastructure/device may include data produced
by means of different data formats (e.g. different sensor
manufacturers, external API services). It may be necessary to
foresee operations to mitigate the data heterogeneity. Such an
operation is necessary to standardize the input data format
exploited by ML algorithms and/or humans. Use of ontologies
thought for specific domains (e.g. SAREF [1]) can be foreseen.
Data quality. Each IoT infrastructure/device has to be accompanied with
appropriate metadata (e.g. accuracy) for each data source
...