Network Working Group                                         J. Jimenez
Internet-Draft                                                  Ericsson
Intended status: Informational                             H. Tschofenig
Expires: January 3, 2019                                        Arm Ltd.
                                                               D. Thaler
                                                           July 02, 2018

   Report from the Internet of Things (IoT) Semantic Interoperability
                         (IOTSI) Workshop 2016


   This document provides a summary of the 'Workshop on Internet of
   Things (IoT) Semantic Interoperability (IOTSI)', which took place in
   Santa Clara, California, on March 17-18, 2016.  The main goal of the
   workshop was to foster a discussion on the different approaches used
   by companies and Standards Developing Organizations (SDOs) to
   accomplish interoperability at the application layer.  This report
   summarizes the discussions, and lists recommendations to the
   standards community.  The views and positions in this report are
   those of the workshop participants and do not necessarily reflect
   those of the authors and the Internet Architecture Board (IAB), which
   organized the workshop.

Status of This Memo

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   This Internet-Draft will expire on January 3, 2019.

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Copyright Notice

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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  What Problems to Solve  . . . . . . . . . . . . . . . . . . .   4
   4.  Translation . . . . . . . . . . . . . . . . . . . . . . . . .   7
   5.  Dealing with change . . . . . . . . . . . . . . . . . . . . .   9
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   7.  Collaboration . . . . . . . . . . . . . . . . . . . . . . . .  11
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  11
   9.  Appendix A: Program Committee . . . . . . . . . . . . . . . .  11
   10. Appendix B: Accepted Position Papers  . . . . . . . . . . . .  11
   11. Appendix C: List of Participants  . . . . . . . . . . . . . .  14
   12. Informative References  . . . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  17

1.  Introduction

   The Internet Architecture Board (IAB) holds occasional workshops
   designed to consider long-term issues and strategies for the
   Internet, and to suggest future directions for the Internet
   architecture.  The investigated topics often require coordinated
   efforts of many organizations and industry bodies to improve an
   identified problem.  One of the targets of the workshops is to
   establish communication between relevant organizations, especially
   when the topics are out of the scope for the Internet Engineering
   Task Force (IETF).  This long-term planning function of the IAB is
   complementary to the ongoing engineering efforts performed by working
   groups of the IETF.

   With the expansion of the Internet of Things (IoT), interoperability
   becomes more and more important.  Standards Developing Organizations

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   (SDOs) have done a tremendous amount of work to standardize new
   protocols, and to profile existing protocols.

   At the application layer and at the level of solution frameworks,
   interoperability is not yet mature.  Particularly, the work on data
   formats (in the form of data models and information models) has not
   seen the same level of consistency throughout SDOs.

   One common problem is the lack of an encoding-independent
   standardization of the information, the so-called information model.
   Another problem is the strong relationship with the underlying
   communication architecture, such as a Remote Procedure Call (RPC)
   style design or a RESTful design.  Furthermore, groups develop
   solutions that are very similar on the surface but differ slightly in
   their standardized outcome, leading to interoperability problems.
   Finally, some groups favor different encodings for use with various
   application layer protocols.

   Thus, the IAB decided to organize a workshop to reach out to relevant
   stakeholders to explore the state-of-the-art and to identify
   commonality and gaps [IOTSIAG][IOTSIWS].  In particular, the IAB was
   interested to learn about the following aspects:

   o  What is the state of the art in data and information models?  What
      should an information model look like?

   o  What is the role of formal languages, such as schema languages, in
      describing information and data models?

   o  What is the role of metadata, which is attached to data to make it

   o  How can we achieve interoperability when different organizations,
      companies and individuals develop extensions?

   o  What is the experience with interworking various data models
      developed from different groups, or with data models that evolved
      over time?

   o  What functionality should online repositories for sharing schemas

   o  How can existing data models be mapped against each other to offer

   o  Is there room for harmonization, or are the use cases of different
      groups and organizations so unique that there is no possibility
      for cooperation?

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   o  How can organizations better work together to increase awareness
      and information sharing?

2.  Terminology

   The first roadblock to interoperability at the level of data models
   is the lack of a common vocabulary to start the discussion.
   [RFC3444] provides a starting point by separating conceptual models
   for designers, or "information models", from concrete detailed
   definitions for implementers, or "data models".  There are concepts
   that are undefined in that RFC and elsewhere, such as the interaction
   with the resources of an endpoint, or "interaction model".  Therefore
   the three "main" common models that were identified were:

   Information Model
      An information model defines an environment at the highest level
      of abstraction and expresses the desired functionality.
      Information models can be defined informally (e.g., in plain
      English) or more formally (e.g., UML, Entity-Relationship
      Diagrams, etc.).  Implementation details are hidden.

   Data Model
      A data model defines concrete data representations at a lower
      level of abstraction, including implementation and protocol-
      specific details.  Some examples are: SNMP Management Information
      Base (MIB) modules, W3C Thing Description (TD) Things, YANG
      models, LWM2M Schemas, OCF Schemas, and so on.

   Interaction Model
      An interaction model defines how data is accessed and retrieved
      from the endpoints, being therefore tied to the specific
      communication pattern that the system has (e.g., REST methods,
      Publish/Subscribe operations, or RPC calls).

   Another identified terminology issue is the semantic meaning overload
   that some terms have.  The meaning can vary depending on the context
   in which the term is used.  Some examples of such terms are:
   semantics, models, encoding, serialization format, media types or
   encoding types.  Due to time constraints, no concrete terminology was
   agreed upon, but work will continue within each organization to
   create various terminology documents.  The participants agreed to set
   up a github repository [IOTSIGIT] for sharing information.

3.  What Problems to Solve

   The participants agreed that there is not simply a single problem to
   be solved, but rather a range.  During the workshop the following
   problems were discussed:

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   o  Formal Languages for Documentation Purposes

   To simplify review and publication, SDOs need formal descriptions of
   their data and interaction models.  Several of them use a tabular
   representation found in the specification itself, but use a formal
   language as an alternative way of describing objects and resources
   for formal purposes.  Some examples of formal language use are as

   The Open Mobile Alliance (OMA), now OMA SpecWorks, used an XML schema
   [LWM2M-Schema] to describe their object and resource definitions.
   The XML files of standardized objects are available for download at

   The Bluetooth SIG defined Generic Attributes (GATT) services and
   characteristics for use with Bluetooth Smart/Low Energy.  The
   services and characteristics are shown in a tabular form on the
   Bluetooth SIG website at [SIG], and are also defined as XML instance

   The Open Connectivity Foundation (OCF) uses JSON Schemas to formally
   define data models, and RAML to define interaction models.  The
   standard files are available online at

   The AllSeen Alliance uses AllJoyn Introspection XML to define data
   and interaction models in the same formal language, tailored for RPC-
   style interaction.  The standard files are available online on the
   AllSeen Alliance web site, but both standard and vendor-defined model
   files can be obtained by directly querying a device for them at

   The World-Wide Web Consortium (W3C) uses the Resource Description
   Framework (RDF) to define data and interaction models using a format
   tailored for the web.

   The Internet Engineering Task Force (IETF) uses YANG to define data
   and interaction models.  Other SDOs may use various other formats.

   o  Formal Languages for Code Generation

   Code generation tools that use formal data and information modelling
   languages are needed by developers.  For example, the AllSeen Visual
   Studio Plugin [AllSeen-Plugin] offers a wizard to generate code based
   on the formal description of the data model.  Another example of a
   data modelling language that can be used for code generation is YANG.
   A popular tool to help with code generation of YANG modules is pyang
   [PYANG].  An example of a tool that can do code generation for
   multiple ecosystems is OpenDOF [OpenDOF].  Use cases discussed for

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   code generation included easing development of server-side device
   functionality, clients, and compliance tests.

   o  Debugging Support

   Debugging tools are needed that implement generic object browsers,
   which use standard data models and/or retrieve formal language
   descriptions from the devices themselves.  As one example, the NRF
   Bluetooth Smart sniffer from Nordic Semiconductor [nRF-Sniffer] can
   be used to display services and characteristics defined by the
   Bluetooth SIG.  As another example, AllJoyn Explorer
   [AllJoynExplorer] can be used to browse and interact with any
   resource exposed by an AllJoyn device, including both standard and
   vendor-defined data models, by retrieving the formal descriptions
   from the device at runtime.

   o  Translation

   The working assumption is that devices need to have a common data
   model with a priori knowledge of data types and actions.  However
   that would imply that each consortium/organization will try to define
   their own, causing a major interoperability problem, if not a
   completely intractable one given the amount of variations,
   extensions, compositions or versioning changes that will happen on a
   per data model basis.

   Another potential approach is to have a minimal ammount of
   information on the device to allow for a runtime binding to a
   specific model, the objective being to require as little prior
   knowledge as possible.

   Moreover, gateways, bridges and other similar devices need to
   dynamically translate (or map) one data model to another one.
   Complexity will increase as there are also multiple protocols and
   schemas that make interoperability harder to achieve.

   o  Runtime Discovery

   Runtime discovery allows IoT devices to exchange metadata about the
   data, potentially along with the data exchanged itself.  In some
   cases the metadata not only describes data but also the interaction
   model as well.  An example of such an approach has been shown with
   HATEOAS [HATEOAS].  Another example is that all AllJoyn devices
   support such runtime discovery using a protocol mechanism called
   "introspection", where the metadata is queried from the device itself

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   There are various models, whether deployed or possible, for such
   discovery.  The metadata might be extracted from a specification, or
   looked up on a cloud repository (e.g., OneIoTa for OCF models), or
   looked up via a vendor's site, or obtained from the device itself
   (such as in the AllJoyn case).  The relevant metadata might be
   obtained from the same place, or different pieces might be obtained
   from different places, such as separately obtaining information such
   as (a) syntax information, (b) end-user descriptions in a desired
   language, and (c) developer-specific comments for implementers.

4.  Translation

   In an ideal world where organizations and companies cooperate and
   agree on a single data model standard, there is no need for gateways
   that translate from one data model to the other one.  However, this
   is far from reality today, and there are many proprietary data models
   in addition to the already standardized ones.  As a consequence,
   gateways are needed to translate between data models.  This leads to
   (n^2)-n combinations, in the worst case.

   There are analogies with gateways back in the 1980s that were used to
   translate between network layer protocols.  Eventually IP took over,
   providing the necessary end-to-end interoperability at the network
   layer.  Unfortunately, the introduction of gateways leads to the loss
   of expressiveness due to the translation between data models.  The
   functionality of IP was so valuable in the market that advanced
   features of other networking protocols became less attractive and
   were not used anymore.

   Participants discussed an alternative which they called a 'red star',
   shown in Figure 1, where data models are translated to a common data
   model shown in the middle.  This reduces the number of translations
   that are needed down to 2n (in the best case).  The problem, of
   course, is that everyone wants their own data model to be the red
   star in the center.

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      +-----+                                        +-----+
      |     |                                        |     |
      |     |  --                                 -- |     |
      |     |    --                             --   |     |
      +-----+      --                         --     +-----+
                     --                    ---
                       --                --
                         --            --
                           --        --
        ---                  -- A  --                  ---
       /   \                ___/ \___                 /   \
      |     | ---------------',   .'---------------  |     |
       \   /                 /. ^ .\                  \   /
        ---                 /'     '\                  ---
                           --        --
                         --            --
                       --                --
                     --                    --
                   --                        --
          /\     --                            --     /\
         /  \  --                                --  /  \
        /    \                                      /    \
       /      \                                    /      \
      /--------\                                  /--------\

           Figure 1: The 'Red Star' in Data/Information Models.

   While the workshop itself was not a suitable forum to discuss the
   design of such translation in detail, several questions were raised:

   o  Do we need a "red star" that does everything or could we design
      something that offers a more restricted functionality?

   o  How do we handle loss of data and loss of functionality?

   o  Should data be translated between data models or data models be

   o  How can interaction models be translated?  They need to be dealt
      with in addition to the data models.

   o  Many (if not all) data and interaction models have some bizarre
      functionality that cannot be translated easily.  How can those be

   o  What limitations are we going to accept in these translations?

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   The participants also addressed the question of when translation
   should be done.  Two use cases were discussed:

   a) Design time: a translation between data model descriptions, such
   as translating a YANG model to a RAML/JSON model, can be performed
   once, during design time.  A single information model might be mapped
   to a number of different data models.

   b) Run time: Runtime translation of values in two standard data
   models can only be algorithmically done when the data model on one
   side is algorithmically derived from the data model on the other
   side.  This was called a "derived model".  It was discussed that the
   availability of runtime discovery can aid in semantic translation,
   such as when a vendor-specific data model on one side of a protocol
   bridge is resolved and the translator can algorithmically derive the
   semantically-equivalent vendor-specific data model on the other side
   of a protocol bridge, as discussed in [BridgeTaxonomy].

   The participants agreed that algorithm translation will generally
   require custom code, whenever one is translating to anything other
   than a derived model.

   Participants concluded that it is typically easier to translate data
   between systems that follow the same communication architecture.

5.  Dealing with change

   A large part of the workshop was dedicated to the evolution of
   devices and server-side applications.  Interactions between devices
   and services and how their relationship evolves over time is
   complicated by their respective different interaction models.

   The workshop participants discussed various approaches to deal with
   change.  In the most basic case, a developer might use a description
   of an API and implement the protocol steps.  Sometimes the data or
   information model can be used to generate code stubs.  Subsequent
   changes to an API require changes on the clients to upgrade to the
   new version, which requires some development of new code to satisfy
   the needs of the new API.

   These interactions could be made machine-understandable in the first
   place, enabling for changes to happen at runtime.  In that scenario,
   a machine client could discover the possible interactions with a
   service, adapting to changes as they occur without specific code
   being developed to adapt to them.

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   The challenge seems to be to code the human-readable specification
   into a machine-readable format.  Machine-readable languages require a
   shared vocabulary to give meaning to the tags.

   These types of interactions are often based on the REST architectural
   style.  Its principle is that a device or endpoint only needs a
   single entry point with a host providing descriptions of the API in-
   band by means of web links and forms.

   By defining IoT-specific relation types, it is possible to drive
   interactions through links instead of hardcoding URIs into a RESTful
   client, thus making the system flexible enough for later changes.
   The definition of the basic hypermedia formats for IoT is still work
   in progress.  However, some of the existing mechanisms can be reused,
   such as resource discovery, forms, or links.

6.  Security Considerations

   There were two types of security considerations discussed: use of
   formal data models for security configuration, and security of data
   and data models in general.

   It was observed that the security assumptions and configuration, or
   "security model", varies by ecosystem today, making the job of a
   translator difficult.  For example, the types of security principals
   (e.g., user vs. device vs. application), the use of access control
   lists (ACLs) vs. capabilities, and what types of policies can be
   expressed, all vary by ecosystem.  As a result, the security model
   architecture generally dictates where translation can be done.

   One approach discussed was whether two endpoints might be able to use
   some overlay security model, across a translator between two
   ecosystems, which only works if the two endpoints agree on a common
   data model for their communication.  Another approach discussed was
   simply having a translator act as a trusted intermediary, which
   allows the translator to be able to translate between different data

   One suggestion discussed was potentially adding metadata into either
   the formal data model language, or accompanying the data values over
   the wire, tagging the data with privacy levels.  However, sometimes
   even the privacy level of information might itself be sensitive.
   Still, it was observed that being able to dynamically learn security
   requirements might help provide better UIs and translators.

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7.  Collaboration

   The participants discussed how best to share information among their
   various organizations.  One discussion was around having joint
   meetings.  One current challenge reported was that organizations were
   not aware of when and where each others' meetings were scheduled, and
   sharing such information could help organizations better collocate
   meetings.  To facilitate this exchange, the participants agreed to
   add links to their respective meeting schedules from a common page in
   the IOTSI repository [IOTSIGIT].

   Another challenge reported was that organizations did not know how to
   find each others' published data models, and sharing such information
   could better facilitate reuse of the same information model.  To
   facilitate this exchange, this participants discussed whether a
   common repository might be used by multiple organizations.  The OCF's
   OneIoTa repository was discussed as one possibility but it was
   reported that its terms of use at the time of the workshop prevented
   this.  The OCF agreed to take this back and look at updating the
   terms of use to allow other organizations to use it too, as the
   restriction was not the intent. was discussed as another
   possibility.  In the meantime, the participants agreed to add links
   to their respective repositories from a common page in the IOTSI
   repository [IOTSIGIT].

   It was also agreed that the mailing list would remain
   open and available for sharing information between all relevant

8.  Acknowledgements

   We would like to thank all paper authors and participants for their
   contributions, and Ericsson for hosting the workshop.

9.  Appendix A: Program Committee

   This workshop was organized by the following individuals: Jari Arkko,
   Ralph Droms, Jaime Jimenez, Michael Koster, Dave Thaler, and Hannes

10.  Appendix B: Accepted Position Papers

   o  Jari Arkko, "Gadgets and Protocols Come and Go, Data Is Forever"

   o  Carsten Bormann, "Noise in specifications hurts"

   o  Benoit Claise, "YANG as the Data Modelling Language in the IoT

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   o  Robert Cragie, "The ZigBee Cluster Library over IP"

   o  Dee Denteneer, Michael Verschoor, Teresa Zotti, "Fairhair:
      interoperable IoT services for major Building Automation and
      Lighting Control ecosystems"

   o  Universal Devices, "Object Oriented Approach to IoT

   o  Bryant Eastham, "Interoperability and the OpenDOF Project"

   o  Stephen Farrell, Alissa Cooper, "It's Often True: Security's
      Ignored (IOTSI) - and Privacy too"

   o  Christian Groves, Lui Yan, ang Weiwei, "Overview of IoT semantics

   o  Ted Hardie, "Loci of Interoperability for the Internet of Things"

   o  Russ Housley, "Vehicle-to-Vehicle and Vehicle-to-Infrastructure

   o  Jaime Jimenez, Michael Koster, Hannes Tschofenig, "IPSO Smart

   o  David Jones, IOTDB - "Interoperability Through Semantic

   o  Sebastian Kaebisch, Darko Anicic, "Thing Description as Enabler of
      Semantic Interoperability on the Web of Things"

   o  Achilleas Kemos, "Alliance for Internet of Things Innovation
      Semantic Interoperability Release 2.0, AIOTI WG03 - IoT

   o  Ari Keraenen, Cullen Jennings, "SenML: simple building block for
      IoT semantic interoperability"

   o  Dongmyoung Kim, Yunchul Choi, Yonggeun Hong, "Research on Unified
      Data Model and Framework to Support Interoperability between IoT

   o  Michael Koster, "Model-Based Hypertext Language"

   o  Matthias Kovatsch, Yassin N.  Hassan, Klaus Hartke, "Semantic
      Interoperability Requires self describing Interaction Models"

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   o  Kai Kreuzer, "A Pragmatic Approach to Interoperability in the
      Internet of Things"

   o  Barry Leiba, "Position Paper"

   o  Marcello Lioy, "AllJoyn"

   o  Kerry Lynn, Laird Dornin, "Modeling RESTful APIs with JSON Hyper-

   o  Erik Nordmark, "Thoughts on IoT Semantic Interoperability: Scope
      of security issues"

   o  Open Geospatial Consortium, "OGC SensorThings API: Communicating
      "Where" in the Web of Things"

   o  Jean Paoli, Taqi Jaffri, "IoT Information Model Interoperability:
      An Open, Crowd-Sourced Approach in Three Parallel Parti"

   o  Joaquin Prado, "OMA Lightweight M2M Resource Model"

   o  Dave Raggett, Soumya Kanti Datta, "Input paper for IAB Semantic
      Interoperability Workshop"

   o  Pete Rai, Stephen Tallamy, "Semantic Overlays Over Immutable Data
      to Facilitate Time and Context Specific Interoperability"

   o  Jasper Roes, Laura Daniele, "Towards semantic interoperability in
      the IoT using the Smart Appliances REFerence ontology (SAREF) and
      its extensions"

   o  Max Senges, "Submission for IAB IoT Sematic Interoperability

   o  Bill Silverajan, Mert Ocak, Jaime Jimenez, "Implementation
      Experiences of Semantic Interoperability for RESTful Gateway

   o  Ned Smith, Jeff Sedayao, Claire Vishik, "Key Semantic
      Interoperability Gaps in the Internet-of-Things Meta-Models"

   o  Robert Sparks and Ben Campbell, "Considerations for certain IoT
      based services"

   o  J.  Clarke Stevens, "Open Connectivity Foundation oneIoTa Tool"

   o  J.  Clarke Stevens, Piper Merriam, "Derived Models for
      Interoperability Between IoT Ecosystems"

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   o  Ravi Subramaniam, "Semantic Interoperability in Open Connectivity
      Foundation (OCF) - formerly Open Interconnect Consortium (OIC)""

   o  Andrew Sullivan, "Position paper for IOTSI workshop"

   o  Darshak Thakore, "IoT Security in the context of Semantic

   o  Dave Thaler, "IoT Bridge Taxonomy"

   o  Dave Thaler, S"ummary of AllSeen Alliance Work Relevant to
      Semantic Interoperability"

   o  Mark Underwood, Michael Gruninger, Leo Obrst, Ken Baclawski, Mike
      Bennett, Gary Berg-Cross, Torsten Hahmann, Ram Sriram, "Internet
      of Things: Toward Smart Networked Systems and Societies"

   o  Peter van der Stok, Andy Bierman, "YANG-Based Constrained
      Management Interface (CoMI)"

11.  Appendix C: List of Participants

   o  Andy Bierman, YumaWorks

   o  Carsten Bormann, Uni Bremen/TZI

   o  Ben Campbell, Oracle

   o  Benoit Claise, Cisco

   o  Alissa Cooper, Cisco

   o  Robert Cragie, ARM Limited

   o  Laura Daniele, TNO

   o  Bryant Eastham, OpenDOF

   o  Christian Groves, Huawei

   o  Ted Hardie, Google

   o  Yonggeun Hong, ETRI

   o  Russ Housley, Vigil Security

   o  David Janes, IOTDB

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   o  Jaime Jimenez, Ericsson

   o  Shailendra Karody, Catalina Labs

   o  Ari Keraenen, Ericsson

   o  Michael Koster, SmartThings

   o  Matthias Kovatsch, Siemens

   o  Kai Kreuzer, Deutsche Telekom

   o  Barry Leiba, Huawei

   o  Steve Liang, Uni Calgary

   o  Marcello Lioy, Qualcomm

   o  Kerry Lynn, Verizon

   o  Mayan Mathen, Catalina Labs

   o  Erik Nordmark, Arista

   o  Jean Paoli, Microsoft

   o  Joaquin Prado, OMA

   o  Dave Raggett, W3C

   o  Max Senges, Google

   o  Ned Smith, Intel

   o  Robert Sparks, Oracle

   o  Ram Sriram, NIST

   o  Clarke Stevens

   o  Ram Subramanian, Intel

   o  Andrew Sullivan, DIN

   o  Darshak Thakore, CableLabs

   o  Dave Thaler, Microsoft

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   o  Hannes Tschofenig, ARM Limited

   o  Michael Verschoor, Philips Lighting

12.  Informative References

              Microsoft, "AllJoyn Explorer", 2016.

   [AllSeen]  Thaler, D., "Summary of AllSeen Alliance Work Relevant to
              Semantic Interoperability", 2016, <

              Rockwell, B., "Using the AllJoyn Studio Extension", 2016.

              Thaler, D., "IoT Bridge Taxonomy", 2016,

   [HATEOAS]  Kovatsch, M., "Semantic Interoperability Requires Self-
              describing Interaction Models - HATEOAS for the Internet
              of Things", Proceedings of the IoT Semantic
              Interoperability Workshop 2016, 2016,

   [IOTSIAG]  IAB, "IoT Workshop for Semantic Interoperability (IOTSI) -
              Agenda and Slides", 2016,

              IOTSI, "Github Collaborative Repository", 2016,

   [IOTSIWS]  IAB, "IoT Workshop for Semantic Interoperability (IOTSI)
              2016 - Main Page and Position Papers", 2016,

              OMA, "OMA LWM2M XML Schema", 2018.

              Nordic Semiconductor, "nRF Sniffer - Smart/Bluetooth low
              energy packet sniffer", 2016.

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   [OMNA]     OMA, "OMNA Lightweight M2M (LWM2M) Object & Resource
              Registry", 2018.

   [OpenDOF]  OpenDOF, "The OpenDOF Project", 2015,

   [PYANG]    Bjorklund, M., "An extensible YANG validator and converter
              in python", 2016.

   [RFC3444]  Pras, A. and J. Schoenwaelder, "On the Difference between
              Information Models and Data Models", RFC 3444,
              DOI 10.17487/RFC3444, January 2003, <https://www.rfc-

   [SIG]      Bluetooth SIG, "GATT Specifications", 2018,

Authors' Addresses

   Jaime Jimenez


   Hannes Tschofenig
   Arm Ltd.


   Dave Thaler


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