]> Electronics and Telecommunications Research Institute

218 Gajeong-ro, Yuseong-gu Daejeon 34129 South Korea +82 42 860 1213 hjlee294@etri.re.kr

Electronics and Telecommunications Research Institute

218 Gajeong-ro, Yuseong-gu Daejeon 34129 South Korea +82 42 860 0926 jhong@etri.re.kr

ART ASDF Internet-Draft This memo specifies SDF modeling for a digital twin, i.e. a digital twin system, and its Things. An SDF is a format that is used to create and maintain data and interaction, and to represent the various kinds of data that is exchanged for these interactions. The SDF format can be used to model the characteristics, behavior and interactions of Things, i.e. physical objects, in a digital twin that contain Things as components.

Introduction A digital twin is defined as a digital representation of an object of interest and may require different capabilities, for example, synchronization and real-time support, according to the specific domain of application. . Digital twin help organizations improve important functional objectives, including real-time control, off-line analytics, and predictive maintenance, by modeling and simulating objects in the real world. Therefore, it is important for a digital twin to represent as much real-world information about the object as possible when digitally representing the object. Nowadays, digital twin technologies are applied in various domains including manufacturing, energy, medical, farm, transportation, etc. And a common format is needed to represent the objects in the domains as digital twins. SDF can be used for modeling objects as digital twins. This document specifies the modeling and guidance on how to use SDF to represent objects as digital twins.

Terminology This specification uses the terminology specified in in particular "Class Name Keyword", "Object", and "Affordance". The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

SDF structure for digital twin This section describes SDF structure to represent a thing or an object as a digital twin. The architecture of a digital twin based on the SDF model is illustrated in , following the guidelines of . The physical layer comprises affordance and non-affordance objects. From the real-world objects, only those deemed relevant are selected for representation as digital twins. The digital twin sublayer is structured into three sublayers: the device communication sublayer, the digital twin sublayer, and the application sublayer. The device communication sublayer is responsible for monitoring and collecting data from both affordance and non-affordance objects. This sublayer provides the necessary data to synchronize the physical objects with their digital twin counterparts. The digital twin sublayer ensures synchronization between the affordance and non-affordance objects and their respective digital twins using the data provided by the Device Communication Sublayer. The Application sublayer presents the synchronized values of the digital twins to users to facilitate informed decision making.

Basic Architecture of digital twin

Motivation and design rationale The document is based on the underlying structure defined in , which which standardizes the semantic definition format (SDF) for representing IoT affordance. This specification provides a strong basis for representing individual devices and their features (sdfProperty, sdfAction, sdfEvent, etc.), but additional mechanisms are needed to address the unique requirements of digital twin modeling. Digital twin systems defined in often have to describe virtual representations of various physical assets, including metadata, identity, contextual relationships, historical data, as well as device interfaces.

Introduction of sdfContext A new SDF keyword sdfContext described in is introduced to represent non-functional or metadata elements that describe a device or component without implying direct interaction:

• Identifier (e.g., UUID, URN)
• Location (e.g. site, zone, GPS tag)
• Owner (e.g., representative, ,anufacturer)

These field can appear in both sdfObject and sdfThing contexts and follow the same structural pattern as sdfData and is designed for scalability.

Digital Twin Modeling using SDF elements To support hierarchical representations (e.g., a boat composed of heater, GPS, and battery subsystems), this document encourages use of sdfThing to aggregate related sdfObject components, along with metadata. The example mapping of digital twin attributes to SDF elements is shown in . Digital Twin Modeling using elements of an SDF model

Attribute	Recommended Mapping	Description
Identifier	sdfContext	Globally unique digital twin ID (e.g., URN)
Characteristic	sdfProperty or sdfData	General description or domain properties
Schedule	sdfEvent or sdfData	Time-based actions, availability, or maintenance
Status	sdfAction or sdfProperty	Actual or calculated operating conditions
Location	sdfContext	Physical or logical location information
Report	sdfData	Measurement summaries, analytics, or logs
owner	sdfContext	Organization or entity responsible for the digital twin
Relationship	sdfRelation	Inter-object/inter-twin relationships

Relationship modeling The sdfRelation, defined in , is a structure for specifying logical or physical relationships between objects within an SDF model. If conventional sdfThing, sdfObject, and sdfProperty focus on defining the properties of individual digital twins, sdfRelation is a means of expressing interactions and structural links between them. Since these relationships go beyond a single digital twin definition, they must be managed in a separate structure, where sdfRelation is used. The sdfRelation keyword allows describing complex relationships beyond just the parent-child hierarchy. These relationships can include:

• Physical relations (e.g., "inside", "next to")
• Functional relations (e.g., "controls", "is controlled by")
• Semantic relations (e.g., "similar to", "same as")

The sdfRelation definition can include the following fields as defined in :

• relType: Specifies the type of relationship that can an external ontologies (e.g., SAREF) can refer to.
• target: Points to the SDF object or an external ontology term that is the target of the relationship.
• description: Provides a detailed textual explanation of the relationship.
• label: A short human-readable label for the relationship.
• property: Additional properties describing the relationship context.
• $comment: Optional properties including implementers notes.

An example of sdfRelation is shown in . The sdfProtocolMap in this example is defined in and

An example of sdfRelation

Examples of digital twin system

Overview Domain-specific examples are provided to illustrates how SDF-based Digital Twin representations can be modeled across various application areas. Each example is structured using the sdfThing construct to represent the physical entity, with associated sdfObject, sdfProperty, sdfAction, sdfEvent, and optional sdfContext or sdfRelation entries. These examples cover multiple domains such as maritime, healthcare, smart building and energy systems, enabling standardized modeling and interoperability across diverse use cases.

Marine system illustrates how various components of a maritime vessel—specifically Boat007—can be represented as a structured Digital Twin using the Semantic Definition Format (SDF) model. Each physical component, such as a heater or battery, is abstracted as an sdfObject, while the overall vessel is modeled as an sdfThing. For each component, key SDF elements such as sdfProperty, sdfAction, or sdfEvent are defined to describe the operational and contextual aspects of the system. Relationships such as sdfRelation are used to express functional connections (e.g., the battery is connectedTo the controller), enabling richer modeling of interactions between components. This structure allows developers and systems integrators to:

• Seamlessly capture and communicate device capabilities and states.
• Integrate operational data for real-time monitoring and analysis.
• Enable interoperability with other domains through standardized semantics.

Components and SDF elements of a marine system

Attribute	SDF element	Example properties
Boat007	sdfThing	id, name, model, includes heater1 and battery1
Heater1	sdfObject	status (sdfProperty), temperature (sdfProperty), turnOn (sdfAction)
Thermostat1	sdfObject	setPoint, mode (sdfProperty)
Battery1	sdfObject	voltage (sdfProperty), chargeLevel (sdfProperty), battery-to-controller (sdfRelation)
Controller	sdfObject	status (sdfProperty), controlMode (sdfProperty)
Temp-to-Thermostat	sdfRelation	source: heater1.temperature, target: thermostat1.setPoint, relationType: regulatedBy
Battery-to-Controller	sdfRelation	source: batterySensor, target: powerController, relationType: connectedTo
Location	sdfContext	latitude, longitude, dockedAt (e.g., port007)

In the context of Boat007, shown in , such a Digital Twin can support various applications, including predictive maintenance, energy optimization, and fleet-level coordination, demonstrating the practicality and scalability of SDF-based Digital Twin modeling for mobility and transportation systems.

An example of marine system

Healthcare system This example represents a digital twin for a patient health monitor system (patientMonitor001) assigned to a patient. The system reports real-time health properties while referencing contextual patient information with the components and elements shown in . Components and SDF elements of a healthcare system

Attribute	SDF element	Example properties
Patient monitor	sdfThing	patientMonitor001 as a digital twin
ECG Module	sdfObject	heartRate, rhythmType, signalStrength
Infusion Pump	sdfObject	flowRate, volumeRemaining, alarmStatus
Property	sdfProperty	e.g., temperature, bloodPressureSystolic, oxygenSaturation
Context info	sdfContext	bedNumber, wardLocation, patientID, usageScenario
Identity info	identityManifest	systemType, firmwareVersion, hospitalAssetTag
Relations	sdfRelation	ECG → AlarmSystem (relationType: monitoredBy)

A digital twin example of a patient monitoring system with ECG and infusion pump components is illustated in . in the healthcare domain, where a biosensor measuring the heart rate is functionally connected to an alert system that emits a high heart rate warning. This enables real-time patient monitoring in medical environments.

An example of healthcare

Smart building system This example shows a digital twin representing a smart lighting control system within a smart building domain. The system uses both the MQTT protocol and the CoAP protocol to integrate lighting devices and occupancy-based controls. Contextual information such as room number, zone, and usage scenario is included to support location-based control and analysis. The SDF elements and related components used in this domain are described in . Components and SDF elements of a smart building system

Attribute	SDF element	Example properties
Smart room	sdfThing	roomControl001 as a digital twin, including lightController and sensorUnit
Light controller	sdfObject	brightness (sdfProperty), toggle (sdfAction)
Sensor unit	sdfObject	occupancy (sdfProperty), motionDetected (sdfEvent)
Property	sdfProperty	brightness:percent, occupancy:boolean
Action	sdfAction	toggle (on/off), dimTo (level)
Context info	sdfContext	roomNumber: “101”, zone: “eastWing”, usage: “office”
Identity info	identityManifest	vendor: “SmartBuild Inc.”, firmware: “v2.1.0”
Protocol	sdfProtocolMap	MQTT + CoAP for monitoring and control
Relations	sdfRelation	sensor-to-lightController (relationType: triggers)

A digital twin representation of the smart building example is shown in . In this configuration, occupancy sensors trigger lighting control action through functional relationships, demonstrating real-time and context-aware behavior. Such modeling can be applicable to energy optimization, comfort control, and responsive automation in smart buildings.

An example of smart building lighting system

Requirements for implenmenting digital twin A digital twin is a partial representation of sdfThing or sdfObject that contains attributes such as sdfProperty, sdfAction and sdfEvent. By representing sdfThing as a digital twin, crucial events that require appropriate action can be quickly detected and controlled. The requirements defined in are applied to represent sdfThings and sdfObjects as digital twins.

• Identification: sdfThings and sdfObjects should contain data that uniquely identify them as digital twins.
• Data acquisition: data related to sdfThing and sdfObject, such as sdfProperty, sdfEvent, and sdfAction, should be collected from IP and non-IP systems.
• Data analysis: collected data needs to be analyzed to understand the state of sdfThing and sdfObject.
• Accuracy: The sdfThings and sdfObjects should be represented as digital twins with appropriate levels of detail and accuracy, depending on the application.
• Synchronization: sdfThings and sdfObjects should be synchronized with the digital twin at intervals appropriate to the requirements of each application. Newly added or deleted sdfThings and sdfObjects should be recognized and reflected in the digital twin.

Procedure for digital twin implementation

Overview It is essential to define a standardized implementation procedure to ensure interoperability, scalability, and effective lifecycle management across digital twin systems. This section outlines a step-by-step approach aligned with the Semantic Definition Format (SDF) model and its architecture, enabling consistent modeling, integration, and operation of digital twins in IoT environments. A general principles for representing an sdfThing as a digital twin within a specific domain is outlined as follows:

• defining a purpose for expressing the observable object as a digital twin in the domain
• collecting and mapping data based on the roles of the observable object in the domain
• configuring the observable object into the digital twin based on the data for the purposes
• interworking among digital twins reflecting various roles of the observable object
• synchronizing the observable object and the digital twin

Procedure The procedure of digitally twinning the space and the objects contained in it is described.

• Identifying and scoping physical assets: The first step is to clearly identify the physical assets that will be represented as digital twins. This step includes assigning a globally unique identifier, such as a URN or UUID, and determining the extent of modeling. It also involves deciding whether the unique identifier will cover the entire system or focus on a specific subsystem or component. Although all assets in space can be represented by digital twins, it is cost-effective to select assets for implementation purposes and configure them as digital twins.
• Defining a digital twin: A detailed digital twin should be defined using SDF structures, including sdfThing and sdfObject. This step requires specifying affordances such as sdfProperty, sdfAction, and sdfEvent, as well as non-affordance metadata like location, owner, and other descriptive elements through sdfContext.
• Metadata and contextualization: This step adds metadata that enriches the context of the digital twin, such as geographic location, ownership details, manufacturing information, and feature summaries. It can also support advanced analytics and management, including contextual attributes such as production schedules or maintenance periods.
• Binding interfaces and communications: Digital twins are bound to real-world communication interfaces and protocols such as MQTT, CoAP, and HTTP. This allows affordance of SDF models to interact with real-world data sources, APIs, and physical assets in a smooth and reliable manner.
• Verification and compliance: Once an asset is defined and bound as a digital twin, it should be validated against syntax and semantic rules using tools such as JSON schema validators or CDDL definitions. Compliance with specific SDF profiles or domain-specific standards must also be verified to ensure interoperability.
• Deployment and registration: After verification, the digital twins are deployed in a digital twin registry, edge system, or cloud infrastructure. This step involves registering the model with the discovery service for integration and use by other systems or stakeholders.
• Runtime monitoring and updating: During operations, digital twins need to continuously monitor real data and update their status accordingly. Properties updates, event processing, and partial updates using contextPatch messages should be supported for efficient and lightweight synchronization.
• Lifecycle and governance management: The life cycle of the digital twin is managed through version tracking, audit logs, and compliance documents. This step ensures safe and transparent governance and enables proper disposal and deregistration when assets are no longer available.

Security Considerations Only authorized users should have the authority to manage digital twins, sdfThings and sdfObjects. Also, Secure communication and metadata integrity are essential when implementing digital twins. All context messages, including contextPatch and identityManifest, must have mechanisms such as authentication and authorization applied.

IANA Considerations This document has no IANA actions.

References Normative References KTC Control AB Universität Bremen TZI Ericsson The Semantic Definition Format (SDF) is concerned with Things, namely physical objects that are available for interaction over a network. SDF is a format for domain experts to use in the creation and maintenance of data and interaction models that describe Things. An SDF specification describes definitions of SDF Objects/SDF Things and their associated interactions (Events, Actions, Properties), as well as the Data types for the information exchanged in those interactions. Tools convert this format to database formats and other serializations as needed. ETRI ETRI This document describes an extension to the Semantic Definition Format (SDF) for representing non-affordance information of Things, such as physical, contextual, and descriptive metadata. This extension introduces a new class keyword, sdfContext, that enables comprehensive modeling of Things and improves semantic clarity. Ericsson The Semantic Definition Format (SDF) base specification defines set of basic information elements that can be used for describing a large share of the existing data models from different ecosystems. While these data models are typically very simple, such as basic sensors definitions, more complex models, and in particular bigger systems, benefit from ability to describe additional information on how different definitions relate to each other. This document specifies an extension to SDF for describing complex relationships in class level descriptions. This specification does not consider instance- specific information. Cisco Systems Cisco Systems Philips This memo describes an API that allows applications to perform operations against a gateway serving one or more devices described by an SDF model. The document describes RESTful application layer interface to perform operations on those devices, as well as a CBOR- based publish-subscribe interface for streaming data. Cisco Systems Cisco Systems Ericsson This document defines protocol mapping extensions for the Semantic Definition Format (SDF) to enable mapping of protocol-agnostic SDF affordances to protocol-specific operations. The protocol mapping mechanism allows SDF models to specify how properties, actions, and events should be accessed using specific IP and non-IP protocols such as Bluetooth Low Energy, Zigbee or HTTP and CoAP. In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. Informative References

Acknowledgements This specification is based on work by the One Data Model group.

Contributors DONG-EUI University

176 Eomgwangno Busan_jin_gu Busan 47340 South Korea +82 51 890 1993 joosang.youn@gmail.com

Daejeon University

62 Daehak-ro, Dong-gu Daejeon 34520 South Korea +82 42 280 4841 yonggeun.hong@gmail.com