Semantic Definition Format (SDF) Modeling for Digital Twin

4.1. Introduction to sdfContext

A new SDF keyword sdfContext described in [I-D.draft-ietf-asdf-sdf-nonaffordance] 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, manufacturer)¶

These fields can appear in both sdfObject and sdfThing contexts, follow the same structural pattern as sdfData, and are designed for scalability.¶

4.2. 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 Table 1.¶

4.3. Relationship modeling

The sdfRelation, defined in [I-D.draft-laari-asdf-relations], 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 [I-D.draft-laari-asdf-relations]:¶

• relType: Specifies the type of relationship and may refer to a term defined in an external ontology (e.g., SAREF[saref4bldg]).¶

• 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 Figure 2. The sdfProtocolMap in this example is described in [I-D.draft-ietf-asdf-nipc] and [I-D.draft-ietf-asdf-sdf-protocol-mapping]¶

{
   "sdfThing": {
      "Room001": {
          "description": "Contains lightbult and thermostat"
           "sdfObject": {
               "lightbulb": {
                 "description": "A smart lightbulb",
                 "sdfProperty": {
                     "adjacent-node": { "type": "object", "sdfType": "link"}
                  },
                  "sdfRelation": {
                     "sameRoomAsThermostat": {
                        "relType": "saref:isLocatedIn",
                        "target": "#/sdfObject/thermostat",
                        "description": "This lightbulb is located in the same room as the thermostat.",
                        "label": "Located together"
                     }
                  }
             },
             "thermostat": {
               "description": "A thermostat is in the same room as the lightbulb",
               "sdfProperty": {
                  "adjacent-node": {"type": "object","sdfType": "link"}
                }
              },
              "sdfProtocolMap": {
                "description": "Protocol between the lightbulb and thermostat",
                "ble": {
                  "serviceID": "361c9c4f-22d7-4a1e-824b-8b61045a566a",
                  "characteristicID": "b7adf665-37c7-44be-8e1c-651a10573613"
              }
            }
         }
     }
  }
}

5.1. Motivation

Digital twins require continuous and reliable communication with physical objects. To support synchronization, monitoring, control, and event notification, appropriate network protocols should be selected and semantically bound to modeled elements in SDF structures. This clause outlines the main protocol types, roles in digital twin operations, and guidelines for representing these bindings using [I-D.draft-ietf-asdf-sdf-protocol-mapping].¶

5.2. Supported protocol types

Digital twin applications can use different types of protocols depending on device performance, data volume, latency sensitivity, and network topology. The current SDF Protocol Mapping document defines mappings for BLE and Zigbee. Table 2 is intended to provide examples of protocol types that may be used for digital twin implementations; it does not imply that SDF protocol mappings have been defined for all of the listed protocols.¶

5.3. Protocol binding in SDF

To model the way digital twins communicate with their physical objects, sdfProtocolMap can be defined within the sdfProperty, sdfAction or sdfEvent levels. Each protocol entry can specify a communication topic, path, security mechanism, QoS settings, and timing parameters as shown in Figure 3.¶

{
 "sdfProperty": {
   "temperature": {
     "type": "number",
     "unit": "Cel",
     "sdfProtocolMap": {
       "ble": {
         "serviceID": "00001809-0000-1000-8000-00805f9b34fb",
         "characteristicID": "00002a1c-0000-1000-8000-00805f9b34fb"
       }
     }
   }
 }
}

5.4. QoS considerations

Quality of service (QoS) settings define the reliability and frequency of data transfer between digital twins and physical objects. These settings are particularly important for telemetry (e.g., sdfProperty updates) and command response flows (e.g., sdfAction operations). For example, MQTT defines three QoS levels that can be selected according to the reliability requirements of digital twin data exchange, as shown in Table 3.¶

5.5. Security and access considerations

When binding protocols, sdfSecurityMap can be used to include security parameters (e.g., authentication tokens, OSCORE for CoAP, BLE pairing status). Role-based access control for specific protocol endpoints is also recommended.¶

5.6. Implementation guidelines

The protocol is essential to realizing the digital twin in operation, and it is recommended that the following considerations are taken into account:¶

• Use protocol mapping appropriate for device classes and network environments¶

• Provide both push-based (e.g., MQTT) and poll-based (e.g., HTTP) bindings, if applicable¶

• Reuse standardized subject structures, UUIDs, or endpoint URIs to ensure interoperability¶

• Avoid duplicate or conflicting bindings, and define protocol preferences via metadata if necessary.¶

6.1. Overview

Various examples are included to show how SDF-based digital twin models can be applied to real-world scenarios. These examples show how to represent physical objects using SDF elements. In addition, sdfContext and sdfRelation are used to describe additional information such as the context of components, and the relationship between components (sdfRelation). The examples cover several domains, including marine systems, healthcare systems, smart buildings, and smart networks. This consistent modeling approach can support interoperability across applications.¶

6.2. Marine system

Table 4 describes an example of how a maritime vessel, referred to as Boat007, can be described as a digital twin using the SDF model. In this example, individual physical parts such as heaters and batteries are treated as separate sdfObjects, while the entire vessel is represented as a single sdfThing that groups these components together.¶

In a vessel modeled with SDF, each component is described using elements such as attributes, actions, and events. These elements are used to represent how the component behaves and what state it is in at a given time. The connections between components are also included in the model. For example, a battery may be linked to a controller, which helps show how different parts are related and interact with each other. This kind of representation makes it easier to follow the condition of devices over time. It also allows operational data to be used more effectively for monitoring, while keeping the model compatible with other systems built in a similar way.¶

This structure enables developers and systems integrators to:¶

• Seamlessly capture and communicate the features and state of the device¶

• Consolidate operational data for real-time monitoring and analysis¶

• Standardized semantics enable interoperability with other domains.¶

In the context of Boat007, shown in Figure 4, 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.¶

{
  "sdfThing": {
      "boat007": {
      "label": "Boat #007 with a heater",
      "description": "Contains heaters, fans, battery, etc."
      "sdfProperty": {
         "status": {
            "type": "boolean",
            "description": "Indicates if the boat is powered"
          }
      },
      "sdfObject": {
         "heater1": {
          "description": "A heater ",
          "identityManifest": {
             "manufacturer": "HeaterTech Inc.",
              "model": "HEATER-2025-V1",
              "firmwareVersion": "1.4.3",
              "dateOfManufacture": "2025-04-20T09:00:00Z",
              "certifications": [
                { "scheme": "KS", "certId": "KS123", "region": "KR" } ]
            },
            "contextSnapshot": {
              "thingId": "heater:unit5689",
              "timestamp": "2025-05-23T10:20:00Z",
              "installationInfo": {
                 "room": "kitchen",
                  "floor": 1,
                  "mountType": "freestanding",
                  "installationDate": "2025-06-01"
              },
              "usageProfile": {
                  "type": "residential",
                  "powerCircuit": "230V@60Hz",
                  "energyRating": "A++"
              },
              "location": {"lat": 35.1796, "lon": 129.0756 }
            },
            "sdfProperty": {
                "status": {
                  "type": "boolean"
                  "description":"Whether the heater is powered"
                },
                "temperature": {
                   "type": "number",
                   "unit": "degreeCelsius",
                   "description": "Temperature of the heater"
                  }
            },
            "sdfAction": {
                "turnOn": { "description": "Activate the heater" },
                "turnOff": { "description": "Deactivate the heater" }
            },
            "contextPatch": {
                "thingId": "heater:unit5689",
                "timestamp": "2025-06-20T09:00:00Z",
                "location": {"lat": "35.2988", "lon": "129.2547" },
                "installationInfo": {"floor": 1, "mountType": "wall" }
            }
          },
          "thermostat": {
               "maintenanceSchedule": {
                  "timestamp": "2025-05-20T10:00:00Z"
                  "description": "Last maintained date"
              }
          },
          "batterySensor1": {
            "sdfProperty": {
                "chargeLevel": {
                  "type": "number",
                  "unit": "percent",
                  "description": "Battery charge level"
                },
                "voltage": {
                  "type": "number",
                  "unit": "volt",
                  "description": "Battery voltage"
              }
            }
          },
          "powerController1": {
             "sdfAction": {
                "connect": {"description": "Connect power from the battery" },
                "disconnect": {"description": "Disconn power from the battery"}
                }
            }
        },
        "sdfRelation": {
          "temperature-control": {
             "source": "#/sdfObject/heater1/sdfProperty/temperature",
             "target": "#/sdfObject/thermostat1/sdfProperty/setPoint",
             "relType": "regulatedBy",
             "directionality": "unidirectional",
             "description": "The current temperature of the heater is regulated by the thermostat's setPoint value."
          },
          "battery-to-controller": {
             "source": "#/sdfObject/batterySensor",
             "target": "#/sdfObject/powerController",
             "relType": "connectedTo",
             "directionality": "unidirectional"
         }
      }
    }
  }
}

6.3. Healthcare system

This case 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 Table 5.¶

A digital twin example of a patient monitoring system with ECG and infusion pump components is illustrated in Figure 5. In this healthcare scenario, a biosensor measuring the heart rate is functionally connected to an alert system that emits a high-heart-rate warning, enabling real-time patient monitoring in medical environments.¶

{
  "sdfThing": {
    "patientMonitor001": {
    "sdfObject": {
        "ecg": {
            "sdfProperty": {
                "heartRate": { "type": "number", "unit": "bpm" },
                "rhythmType": { "type": "string" }
              }
          },
          "infusionPump": {
              "sdfProperty": {
                "flowRate": { "type": "number", "unit": "ml/h" },
                "volumeRemaining": { "type": "number", "unit": "ml" }
              }
          }
        },
        "sdfContext": {
            "wardLocation": { "const": "ICU-5A" },
            "patientID": { "const": "PT123456" }
        },
        "identityManifest": {
          "manufacturer": "MediTech",
          "model": "IM-500",
          "serialNumber": "MT-IM500-00789"
        },
        "sdfRelation": {
          "heartRate-to-alertSystem": {
              "description": "The heart rate data from the biosensor is monitored by the alert system, which triggers a warning event when a high heart rate is detected.",
              "source": "#/sdfObject/biosensor/sdfProperty/heartRate",
              "target": "#/sdfObject/alertSystem/sdfEvent/highHeartRateAlert",
              "relType": "monitoredBy",
              "directionality": "unidirectional"
          }
      }
    }
  }
}

6.4. Smart building system

This case shows a digital twin representing a smart lighting control system within a smart building domain. Contextual information such as room number, zone, and usage scenario is included to support location-based control and analysis. The MQTT and CoAP mappings in this example are illustrative and are not currently defined by the SDF Protocol Mapping specification.¶

The SDF elements and related components used in this domain are described in Table 6.¶

A digital twin representation of the smart building example is shown in Figure 6. 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.¶

{
  "sdfThing": {
    "roomControl001": {
      "sdfContext": {
        "roomNumber": "101",
        "zone": "eastWing",
        "usage": "office"
      },
      "sdfObject": {
        "lightController": {
          "sdfProperty": {
            "brightness": {
              "type": "integer",
              "unit": "percent",
              "description": "Current brightness level of the light"
            }
          },
          "sdfAction": {
            "toggle": {
              "description": "Turns the light on or off"
            },
            "dimTo": {
              "description": "Dims the light to the specified brightness"
            }
          },
          "sdfProtocolMap": {
            "mqtt": {
              "topic": "building/room101/light",
              "qos": 1,
              "updateInterval": 5,
              "unit": "seconds"
            },
            "coap": {
              "method": "POST",
              "href": "/room101/light/toggle"
            }
          }
        },
        "sensorUnit": {
          "sdfProperty": {
            "occupancy": {
              "type": "boolean",
              "description": "Whether the room is currently occupied"
            }
          },
          "sdfEvent": {
            "motionDetected": {
              "description": "Triggered when motion is detected"
            }
          }
        }
      },
      "sdfRelation": {
        "sensorToLight": {
          "source": "#/sdfThing/roomControl001/sdfObject/sensorUnit",
          "target": "#/sdfThing/roomControl001/sdfObject/lightController",
          "relType": "triggers",
          "directionality": "unidirectional"
        }
      }
    }
  }
}

6.5. Smart network system

This case shows a digital twin-based smart network system that obtains traffic information associated with a network device and generates digital twins respectively corresponding to the network device and the traffic information. The current traffic information of the network device is synchronized with the digital twin corresponding to the traffic information. Based on the synchronized digital twins, a prediction model can be trained to predict an abnormal condition associated with the network device. In an SDF representation, the network device can be modeled as an sdfObject and the traffic information can be represented by an associated sdfObject or sdfData. Current traffic values can be represented using sdfProperty, and the association and synchronization between the network-device twin and the traffic-information twin can be expressed using sdfRelation. Model training and inference are performed by an analytics function in the application sublayer, while the SDF model represents the synchronized input state and the predicted abnormal condition.¶

The SDF elements and related components used in this domain are described in Table 7.¶

A digital twin representation of the smart network system is shown in Figure 7. The case illustrates its core model to the elements of traffic information associated with a network device, digital twins corresponding to the network device and the traffic information, synchronization of current traffic information with the traffic-information twin, and model training for prediction of an abnormal condition.¶

{
   "sdfThing": {
     "smartNetwork001": {
       "sdfObject": {
         "networkDeviceTwin": {
           "sdfProperty": {
               "currentTraffic": { "type": "number", "unit": "Mbit/s" },
               "utilization": { "type": "number", "unit": "percent" }
             }
         },
         "trafficTwin": {
           "sdfData": {
             "trafficHistory": { "type": "array" },
             "trafficPattern": { "type": "string" }
           },
           "sdfProperty": {
              "synchronizedCurrentTraffic": { "type": "number", "unit": "Mbit/s" }
           }
        }
      },
     "sdfRelation": {
         "trafficAssociation": {
           "source": "#/sdfObject/networkDeviceTwin",
           "target": "#/sdfObject/trafficTwin",
           "relType": "associatedWith"
         },
         "trafficSynchronization": {
           "source": "#/sdfObject/networkDeviceTwin/sdfProperty/currentTraffic",
           "target": "#/sdfObject/trafficTwin/sdfProperty/synchronizedCurrentTraffic",
           "relType": "synchronizedWith"
         }
      },
      "sdfProperty": {
         "anomalyScore": { "type": "number" }
      },
      "sdfEvent": {
         "abnormalConditionPredicted": {
            "description": "Raised when the trained model predicts an abnormal condition"
         }
       }
     }
  }
}

8.1. 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. The general principles for representing an sdfThing as a digital twin within a specific domain are 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 as a digital twin based on the collected data and intended purposes¶

• enabling interworking among digital twins that represent different roles of the observable object¶

• synchronizing the observable object and the digital twin¶

8.2. Procedure

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

• Identifying and scoping physical objects: The first step is to clearly identify the physical objects 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 objects in space can be represented by digital twins, it is cost-effective to select objects 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 objects in a smooth and reliable manner.¶

• Verification and compliance: Once an object 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 objects are no longer available.¶

11.1. Normative References

[I-D.draft-ietf-asdf-nipc]

Brinckman, B., Mohan, R., and B. Sanford, "An Application Layer Interface for Non-IP device control (NIPC)", Work in Progress, Internet-Draft, I-D.draft-ietf-asdf-nipc-13, 20 September 2025, <https://datatracker.ietf.org/doc/html/I-D.draft-ietf-asdf-nipc-13>.

[I-D.draft-ietf-asdf-sdf-nonaffordance]

Hong, J. and H. Lee, "Semantic Definition Format (SDF) Extension for Non-Affordance Information", Work in Progress, Internet-Draft, I-D.draft-ietf-asdf-sdf-nonaffordance-04, 25 July 2026, <https://datatracker.ietf.org/doc/html/I-D.draft-ietf-asdf-sdf-nonaffordance-04>.

[I-D.draft-ietf-asdf-sdf-protocol-mapping]

Mohan, R., Brinckman, B., and L. Corneo, "SDF Protocol Mapping", Work in Progress, Internet-Draft, I-D.draft-ietf-asdf-sdf-protocol-mapping-11, 27 August 2026, <https://datatracker.ietf.org/doc/html/I-D.draft-ietf-asdf-sdf-protocol-mapping-11>.

[I-D.draft-laari-asdf-relations]

Laari, P., "Extended relation information for Semantic Definition Format (SDF)", Work in Progress, Internet-Draft, I-D.draft-laari-asdf-relations-04, 28 January 2025, <https://datatracker.ietf.org/doc/html/I-D.draft-laari-asdf-relations-04>.

[ISO23247-1]

"Automation systems and integration Digital twin framework for manufacturing - Part 1: Overview and general principles, ISO 23247-1.", October 2021, <https://www.iso.org/standard/75066.html>.

[ISO23247-3]

"Automation systems and integration Digital twin framework for manufacturing - Part 3: Digital representation of manufacturing elements, ISO 23247-3.", October 2021, <https://www.iso.org/standard/78744.html>.

[RFC2119]

Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, <https://www.rfc-editor.org/rfc/rfc2119>.

[RFC8174]

Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.

[RFC9880]

Koster, M., Bormann, C., and A. Keränen, "Semantic Definition Format (SDF) for Data and Interactions of Things", BCP 14, RFC 9880, DOI 10.17487/RFC9880, January 2026, <https://www.rfc-editor.org/rfc/rfc9880>.

[Y.4600]

Union, I. T., ""Recommendation ITU-T Y.4600 (2022), Requirements and capabilities of a digital twin system for smart cities.", August 2022.

11.2. Informative References

[saref4bldg]

Poveda-Villaln, M. and R. Garcia-Castro, "SAREF extension for building", 5 June 2020, <https://saref.etsi.org/saref4bldg>.