IoT Systems involve numerous interconnected things that sense or enact on the physical world to support customized software services for human beings. From a software and systems engineering point of view, such systems are essentially complex sociotechnical systems that lead to the development of dynamically adaptable, cyber-physical, systems. The adaptability with regards to the physical environment comes from a feedback (control) loop (e.g., MAPE-K loop) assimilating data from the sensors, building a model of the surrounding environment, planning or possibly predicting new scenarios, and soliciting the actuators accordingly, in the form of a sequence of actions. As with any sociotechnical systems, the planning process is usually semi-automatic, highly interacting with final users to provide the best experience. Various software services have been developed in the past decade, leveraging important frameworks developed by the IoT community (e.g., protocols and gateways), and leading to a wide range of smart systems in energy, production systems, robotics, transportation, healthcare, agriculture among others. The smartness of the system comes from the ability to bring intelligence into the feedback loop. This intelligence primarily leverages the assimilation and curation of the acquired data. However, as a sociotechnical system, it is of outermost importance of also considering broader physical, economic, social and environmental concerns in which the systems and final users involved. Since such information is difficult to get from sensors or to hard-code into the software itself, additional information must be combined with the available data to provide a holistic and systemic view of the system and its environment, support for making informed decisions. This need is currently supported by the concept of digital twins. When comes the time of designing such a feedback loop, modeling appears to be key. Modeling is key to capture any sort of knowledge in the form of descriptive models built from acquired observations or data, and modeling is also key to drive the development and evolution of complex systems in the form of prescriptive models reducing the accidental engineering complexity. The gap between the descriptive models and the prescriptive models can be made manually, or automatically through predictive models. In this talk, I review the various types of models required for intelligently designing software services on top of IoT systems, and I discuss the different roles such models are playing in the overall lifecycle. I present the opportunities for the modeling community, as well as the open challenges to be tackled to achieve such a vision. In particular, I explore the required common modeling foundations for seamlessly combining the different types of models, and the development of complex digital twins to support informed decision making in the feedback loop of smart sociotechnical IoT systems.

1. KEYNOTE @ MDE4IOT’19
BRINGING INTELLIGENCE TO
SOCIOTECHNICAL IOT SYSTEMS
Modeling Opportunities and Challenges
BENOIT COMBEMALE
PROFESSOR, UNIV. TOULOUSE, FRANCE
HTTP://COMBEMALE.FR
BENOIT.COMBEMALE@IRIT.FR
@BCOMBEMALE
BENOIT COMBEMALE
PROFESSOR, UNIV. TOULOUSE & INRIA, FRANCE
HTTP://COMBEMALE.FR
BENOIT.COMBEMALE@IRIT.FR
@BCOMBEMALE

2. Disclaimer
• No, this is not yet another cool talk about AI applications! J
• but rather a reflection on how to intelligently design software services on top
of IoT systems, and the various roles of the different types of models involved
• I’m an outsider, definitively not an IoT expert (protocols, gateways…)
• but a software engineer who give a modeling and language perspective on
developing, so-called smart, software services on top of IoT systems
• I present a vision, not a solution
• From an MDE point of view, where models are (subjectively)
cornerstone
2
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019

3. IoT Systems
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 3
• Interconnected digital things (dedicated
protocols, sensors/actuators)
• Middleware (gateways)
• Software services (aka. smart systems)

4. Modeling IoT Systems
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 4
▸ multi-engineering design models for
system development
▸ models @ runtime (i.e., included into the
control loop) for dynamic adaptations
… up to Digital Twins!
Engineers
System Models Cyber-Physical
System
sensors actuators
Physical
System
Software
<<controls>><<senses>>

5. Modeling IoT Systems
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 5
Engineers
System Models Cyber-Physical
System
sensors actuators
Physical
System
Software
<<controls>><<senses>>

7. Towards Smart Sociotechnical IoT Systems
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 7
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>
?

8. Towards Smart Sociotechnical IoT Systems
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 8
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>
?
economic
social
environmental
individual
technological

9. What about Scientific Modeling?
▸Models (computational and data-intensive sciences) for
analyzing and understanding physical phenomena
Context
Heuristics-Laws
Scientists
Physical Laws
(economic, environmental, social)
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 9

10. Simulator
Context
Simulation
Processes
Heuristics-Laws
Scientists
Physical Laws
(economic, environmental, social)
<<represents>>
What about Scientific Modeling?
▸ Simulators for tradeoff analysis,
what-if scenarios, analysis of
alternatives and adaptations to
environmental changes, etc.
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 10

11. How to bring intelligence?
• Prescriptive models are key to drive any engineering processes
• Descriptive models are key to capture (reason about) any knowledge
• built from observations
• built from data (input/output data, measured data, external data)
• Moving from descriptive to prescriptive
• manually
• through automated transformations
• through predictive models
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 11

12. How to bring intelligence?
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 12
▸ descriptive models
▸ analysis models (incl. large-scale simulation, constraint
solver) of the surrounding context related to global
phenomena (e.g. physical, economical, and social laws)
▸ user models (incl., general public/community
preferences) and regulations (political laws)
▸ predictive models (predictive techniques from AI, machine
learning, SBSE, fuzzy logic)
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>
How to bring intelligence in the feedback (control) loop of sociotechnical IoT systems?

13. Sustainability Systems
▸ Sustainability systems are smart-CPS managing resource production,
transport and consumption for the sake of sustainability
▸ Ex: smart grids, smart city/home/farming, etc.
▸ Sustainability systems
▸ must balance trade-offs between the social, technological,
economic, and environmental pillars of sustainability
▸ involve complex decision-making with heterogeneous analysis
models, and large volumes of disparate data varying in
temporal scale and modality
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 13

14. MDE for Sustainability Systems
▸ Scientific models are used to understand sustainability concerns and
evaluate alternatives (what-if/for scenarios)
▸ Engineering models are used to support the development and runtime
adaptation of sustainability systems.
How to integrate engineering and scientific models in a synergistic fashion to
support informed decisions, broader engagement, and dynamic adaptation in
sustainability systems?
Modeling for Sustainability
B. Combemale, B. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In MiSE @ ICSE , 2016
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 14
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

15. The Sustainability Evaluation ExperienceR (SEER)
▸Smart Cyber-Physical Systems
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 15
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

16. The Sustainability Evaluation ExperienceR (SEER)
▸ Based on informed decisions
▸ with environmental, social and economic laws
▸ with open data
Heuristics-Laws
Scientists
Open Data
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>
<<supplement
field data>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 16
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

17. The Sustainability Evaluation ExperienceR (SEER)
▸ Providing a broader engagement
▸ with "what-if" scenarios for general public and policy makers
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 17
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

18. The Sustainability Evaluation ExperienceR (SEER)
▸ Supporting automatic adaptation
▸ for dynamically adaptable systems
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 18
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

19. The Sustainability Evaluation ExperienceR (SEER)
▸ Application to health, farming system, energy…
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 19
Towards Model-Driven Sustainability Evaluation
Kienzle et al., To appear in Communications of the ACM, 2019
Preprint available at https://hal.inria.fr/hal-02146543v2

20. The Sustainability Evaluation ExperienceR (SEER)
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Farmers
Agronomist
Irrigation
System
in collaboration with
MDE in Practice for Computational Science
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal
In International Conference on Computational Science (ICCS), 2015
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 20

21. FARMING SYSTEM MODELING
- 21
https://github.com/gemoc/farmingmodeling
in collaboration with

22. - 22
WATER FLOOD PREDICTION - WIP
Virtual lab for scientific computing
Approach: web-based and scalable deployment of modeling environment
Challenge: process elicitation & structuration, model composition/integration,
continuous integration/deployment, calibration & sensibility analysis
Exploration for decision making and education
Approach: approximate computing techniques
Challenge: error estimate, uncertainty management, etc.
Domain-specific indicators for impact and tradeoff analysis
Approach: domain-specific languages and active mapping
Challenge: advanced debugging, live modeling (i.e., immediate
feedback and direct manipulation) for what-if/how-to scenarios
in collaboration with

23. - 23
AIRCRAFT ENGINE DEVELOPMENT- WIP
- 23
Systems Engineering
Approach: separation of concerns and views consistency and synchronization
Challenge: definition of coordination patterns and generative approaches
in collaboration with
Design Space Exploration
Approach: continuous / discrete event integration
Challenge: unified interfaces, co-simulation

24. - 24
HPC FOR NUMERICAL ANALYSIS- WIP
in collaboration with
Fostering metamodels and grammars within a dedicated environment for HPC: the NabLab environment
Benoît Lelandais, Marie-Pierre Oudot, Benoît Combemale
In International Conference on Software Language Engineering (SLE), 2018

25. - 25
HPC FOR NUMERICAL ANALYSIS- WIP
in collaboration with
in collaboration with
• Advanced debugging / monitoring
• Collaborative editing
• Modular and distributed language services
• Dynamically adaptable language workbench

26. Towards Unifying Modeling Foundations
▸ Convergence of engineering and scientific models
▸ Prescriptive requires descriptive models
▸ Descriptive requires prescriptive models
▸ Predictive models can bridge the gap from descriptive to prescriptive
▸ Grand Challenge: a modeling framework to support the integration of data from
sensors, open data, laws, regulations, scientific models (computational and data-
intensive sciences), predictive models, engineering models, and preferences.
▸ Domain-specific languages (DSLs) for socio-technical coordination
▸ to engage engineers, scientists, decision makers, communities and the general public
▸ to integrate analysis/predictive/user models into the control loop of smart CPS
▸ Compositional modeling framework are key!
▸ dedicated languages or composition operators to help integrating heterogeneous models
▸ automated or interactive flow across the various models
▸ support for transparency and explanation
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 26

27. Towards a Compositional Modeling Framework
• Prolific research on model composition
• cf. CMA workshops, seminars and resulting papers
• differing in formality, level of detail, chosen paradigm, and styles
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 27
[SoSyM, 2019], Preprint at https://hal.inria.fr/hal-01949050
• But yet restricted in scope!
• mostly investigated for prescriptive models to
automate the development
• All sharing limited common foundations
• structural composition = structural merging
• behavioral composition = event scheduling
• What about multi-scale operators (in
space and time), uncertainty
management, etc.?

28. The MODA Framework
A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems
• Different types of models
• Engineering Model
• Scientific Model
• Machine Learning Model
• Playing different roles
• descriptive
• prescriptive
• Predictive
• A model built for a given purpose,
can plays one or more roles with
respect to that purpose.
• Challenges
• Observation and Measurement
• Interdisciplinary and Integration
• Decision Support
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 28
A Hitchhiker’s Guide to Model-Driven Engineering for Data-Centric Systems
Combemale et al., Work in progress, Bellairs Workshop on Data and Models, 2019

29. Take Away Messages
▸ Smartness comes from human beings
▸ Modeling Opportunities: models are key to capture
knowledge from observations and available data
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 29
Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<controls>><<senses>>Communities
(e.g., farmers)
<<adapt>>
<<supplement
field data>>
<<provide configuration,
preferences, questions>>
<<present possible future
and variable indicators>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
Software Services built on IoT Systems are inherently
Sociotechnical (cyber-physical) systems
▸ Seamless integration of descriptive, predictive and
prescriptive models (SE, DevOps, Evolution, DAS)
▸ Modeling Challenges:
▸ unifying foundations supporting the integration
of various types and roles of models
▸ Live and collaborative (meta)modeling
▸ Uncertainty management

30. Conclusion
▸ Integration of descriptive and predictive models in the
feedback (control) loop of smart sociotechnical IoT systems is
key to provide
▸ more informed decisions,
▸ a broader engagement,
▸ and eventually relevant runtime reconfigurations
▸ SEER is a particular instantiation of such a vision for
sustainability systems
▸ Other SE opportunities:
▸ Value-driven software engineering
▸ Ethics in SE
▸ ICT for Sustainability
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019 30

31. ICT4S 2020 “Systemic Sustainability”
June 21-27, 2020 | Bristol, UK
Call for Contributions
The ICT4S conferences bring together leading researchers and
decision-makers in government and industry around the application of
Information and Communications Technology (ICT) for Sustainability,
ICT effects on sustainability and the development of sustainable ICT
systems. ICT4S is a place for researchers from a variety of fields,
including Computer Science, Engineering, Environmental Studies,
Economics and Social Sciences.
Topics of Interest
Submissions to one of the proposed tracks on diverse topics related to
ICT for sustainability, including, but not limited to:
Sustainable community building via ICT • Grassroots movements
facilitated by ICT • Sustainability-driven resilience by and of ICT • Social
sustainability implications, contributions and limitations of ICT •
Enabling and systemic effects of ICT on society and/or the environment
• Smart cities, homes and offices • Intelligent energy management in
buildings • Smart cyber-physical systems (smart grids, smart city, smart
farming, etc.) • Sustainability in data centers and high-performance
computing • Intelligent transportation and logistics • Green networking,
monitoring and adaptation of software-intensive systems and services •
ICT-induced behavioral and societal change • Design principles for
sustainable ICT • Energy-efficient and energy-aware software
engineering • Sustainability of technical infrastructures • Software for
environmental sustainable ICT • Software for sustainable business
governance • Reduced hardware obsolescence • E-waste and closed
material cycles • Incentives and nudges for more sustainable ICT • Tools
supporting green decision making and development • Challenges for an
environmentally sustainable ICT industry • Art and education in ICT for
sustainability (incl. serious game) • Systematic interdisciplinary efforts
in ICT for sustainability • Computational sustainability • Climate
informatics • Languages, simulation tools and methods, and cloud
computing for environmental sciences • Modeling tools and methods for
sustainability-aware (cyber-physical) system development • Elicitation,
specification, and validation of sustainability requirements.
Organization
General Co-chairs: Ruzanna Chitchyan • Daniel Schien
PC Chairs: Ana Moreira • Benoit Combemale
Workshop Co-chairs: Oliver Bates
Doctoral Symposium: Birgit Penzenstadler
Community Engagement Co-chairs: Caroline Bird • Johanna Pohl
Demonstrations Co-chair: Thais Batista
Proceedings Chairs: Leticia Duboc • Monica Pinto
Art Exhibition: Samuel Mann • Melissa Mean • Simon Lock
Sponsorship Chair: Ian Brooks
web: 2020.ict4s.org • facebook: /ict4s • twitter: @ict4s
Arts Exhibition
We are looking for creative works of any format that challenge the
status quo, that provide new ways of looking at information, that
disrupt and prompt us to question, or perhaps provide seeds for
action. Visualisations are welcome, as well as demos and
installations of interfaces or works that bear a physical or pictorial
form.
Important Dates
29 January 2020 Abstract submission for research paper track
5 February 2020 Paper and poster submission
13 March 2020 Author notification
Submission
We welcome original papers (technical, exploratory, application or
empirical evaluation) reporting on research, development, case
studies, and experience and empirical reports in the field of ICT4S.
Submitted papers must be written in English.
• Inter-disciplinary conference (CS, design, SHS)
• Software engineering largely covered.
Modeling community particularly welcome!
• Research papers, journal first (to be proposed
by the authors) and posters
• Have a look to the Arts Exhibition!
• Deadline: Jan 29th (abstract), Feb 5th (paper)
• Format: ACM Format (double column), 10p.
• Submission: Easychair
• Feel free to contact me for any questions!

32. Bringing Intelligence to Sociotechnical IoT Systems
Modeling Opportunities and Challenges
Abstract.
IoT Systems involve numerous interconnected things that sense or enact on the physical world to support customized
software services for human beings. From a software and systems engineering point of view, such systems are essentially
complex sociotechnical systems that lead to the development of dynamically adaptable, cyber-physical, systems. The
adaptability with regards to the physical environment comes from a feedback (control) loop (e.g., MAPE-K loop) assimilating
data from the sensors, building a model of the surrounding environment, planning or possibly predicting new scenarios, and
soliciting the actuators accordingly, in the form of a sequence of actions.
As any sociotechnical systems, the planning process is usually semi-automatic, highly interacting with final users to provide
the best experience. Various software services have been developed in the past decade, leveraging important frameworks
developed by the IoT community (e.g., protocols and gateways), and leading to a wide range of smart systems in energy,
production systems, robotics, transportation, healthcare, agriculture among others. The smartness of the system comes from
the ability to bring intelligence into the feedback loop. This intelligence primarily leverages the assimilation and curation of
the acquired data. However, as a sociotechnical system, it is of outermost importance of also considering broader physical,
economic, social and environmental concerns in which the systems and final users involved. Since such information is difficult
to get from sensors, or to hard-code into the software itself, additional information must be combined with the available data
to provide an holistic and systemic view of the system and its environment, support for taking informed decisions. This need
is currently supported by the concept of digital twins.
When comes the time of designing such a feedback loop, modeling appears to be key. Modeling is key to capture any sort of
knowledge in the form of descriptive models built from acquired observations or data, and modeling is also key to drive the
development and evolution of complex systems in the form of prescriptive models reducing the accidental engineering
complexity. The gap between the descriptive models and the prescriptive models can be made manually, or automatically
through predictive models.
In this talk, I review the various types of models required for intelligently designing software services on top of IoT systems,
and I discuss the different roles such models are playing in the overall lifecycle. I present the opportunities for the modeling
community, as well as the open challenges to be tackled to achieve such a vision. In particular, I explore the required
common modeling foundations for seamlessly combining the different types of models, and the development of complex
digital twins to support informed decision making in the feedback loop of smart sociotechnical IoT systems.
Bringing Intelligence to Sociotechnical IoT Systems
Benoit Combemale @ MDE4IoT Workshop, Sep., 2019