Semantic Technolgies for the Internet of Things
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This document discusses semantic technologies for representing and integrating data in the Internet of Things (IoT). It describes how XML, RDF, and ontologies can provide interoperable and machine-interpretable representations of IoT data. Specifically, it explains how these technologies allow defining structured models and vocabularies to annotate sensor data and integrate information from multiple heterogeneous sources. The document also discusses challenges in IoT data such as heterogeneity, multi-modality, and volume, and how semantic technologies can help address issues of data interoperability, discovery, and reasoning.
Semantic Technolgies for the Internet of Things
- 1. 1 Semantic Technologies for the Internet of Things Payam Barnaghi Institute for Communication Systems (ICS) Electrical and Electronic Engineering Department University of Surrey
- 2. The old Internet timeline 2Source: Internet Society
- 3. The World Wide Web 3 Tim Berners-Lee
- 4. The Internet/Web in the early days 44
- 5. And there came Google! 5 Google says that the web has now 30 trillion unique individual pages;
- 6. Wireless Sensor (and Actuator) Networks Sink node Gateway Core network e.g. InternetGateway End-user Computer services - The networks typically run Low Power Devices - Consist of one or more sensors, could be different type of sensors (or actuators) Operating Systems? Services? Protocols? Protocols? In-network Data Processing Data Aggregation/ Fusion Processing of IoT data Interoperable/ Machine- interpretable representations Interoperable/ Machine- interpretable representations “Web of Things” Interoperable/ Machine- interpretable representations
- 7. 7 Sensor devices are becoming widely available - Programmable devices - Off-the-shelf gadgets/tools
- 8. Internet of Things: The story so far RFID based solutions Wireless Sensor and Actuator networks , solutions for communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, early concepts and demos, … Motion sensor Motion sensor ECG sensor Physical-Cyber-Social Systems, Linked-data, semantics, M2M, More products, more heterogeneity, solutions for control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and Industry and B2B services/applications, more Standards…
- 9. Data in the IoT − Data is collected by sensory devices and also crowd sensing sources. − It is time and location dependent. − It can be noisy and the quality can vary. − It is often continuous - streaming data. − Data is gathered from various heterogeneous sources and in various format and representations. − Often the value is in integrating data from different sources and in creating an ecosystem of systems.
- 10. Device/Data interoperability 10 de adapted from the IoT talk given by Jan Holler of Ericsson at IoT Week 2015 in Lisbon.
- 11. Heterogeneity, multi-modality and volume are among the key issues. We need interoperable and machine-interpretable solutions… 11
- 12. Data Lifecycle 12 Source: The IET Technical Report, Digital Technology Adoption in the Smart Built Environment: Challenges and opportunities of data driven systems for building, community and city-scale applications, http://www.theiet.org/sectors/built-environment/resources/digital-technology.cfm
- 13. 13 Observation and measurement data- annotation Tags Data formats Location Source: Cosm.com
- 14. Observation and measurement data 15, C, 08:15, 51.243057, -0.589444 14 value Unit of measurement Time Longitude Latitude How to make the data representations more machine-readable and machine-interpretable?
- 15. Observation and measurement data 15, C, 08:15, 51.243057, -0.589444 15 <value> <unit> <Time> <Longitude> <Latitude> What about this? <value>15</value> <unit>C</unit> <time>08:15</time> <longitude>51.243057</longitude> <latitude>-0.58944</latitude>
- 16. Extensible Markup Language (XML) − XML is a simple, flexible text format that is used for data representation and annotation. − XML was originally designed for large-scale electronic publishing. − XML plays a key role in the exchange of a wide variety of data on the Web and elsewhere. − It is one of the most widely-used formats for sharing structured information. 16
- 17. XML Document Example <?xml version="1.0"?> <measurement> <value>15</value> <unit>C</unit> <time>08:15</time> <longitude>51.243057</longitude> <latitude>-0.58944</latitude> </measurement> 17 XML Prologue- the XML declaration XML elements XML documents MUST be “well formed” Root element
- 18. XML Document Example- with attributes <?xml version="1.0“ encoding="ISO-8859-1"?> <measurement> <value type=“Decimal”>15</value> <unit>C</unit> <time>08:15</time> <longitude>51.243057</longitude> <latitude>-0.58944</latitude> </measurement> 18
- 19. Well Formed XML Documents −A "Well Formed" XML document has correct XML syntax; −XML documents must have a root element; −XML elements must have a closing tag; −XML tags are case sensitive; −XML elements must be properly nested; −XML attribute values must be quoted. 19Source: W3C Schools, http://www.w3schools.com/
- 20. Validating XML Documents − A "Valid" XML document is a "Well Formed" XML document, which conforms to the structure of the document defined in an XML Schema. − XML Schema defines the structure and a list of defined elements for an XML document. 20
- 21. XML Schema- example <xs:element name=“measurement"> <xs:complexType> <xs:sequence> <xs:element name=“value" type="xs:decimal"/> <xs:element name=“unit" type="xs:string"/> <xs:element name=“time" type="xs:time"/> <xs:element name=“longitude" type="xs:double"/> <xs:element name=“latitude" type="xs:double"/> </xs:sequence> </xs:complexType> </xs:element> 21 - XML Schema defines the structure and elements - An XML document then becomes an instantiation of the document defined by the schema;
- 22. XML Documents– revisiting the example <?xml version="1.0"?> <measurement> <value>15</value> <unit>C</unit> <time>08:15</time> <longitude>51.243057</longitude> <latitude>-0.58944</latitude> </measurement> 22 <?xml version="1.0"?> <sensor_data> <reading>15</reading> <u>C</u> <timestamp>08:15</timestamp> <long>51.243057</long> <lat>-0.58944</lat> </sensor_data> How about this one?
- 23. 23 XML − Meaning of XML-Documents is intuitively clear − due to "semantic" Mark-Up − tags are domain-terms − But, computers do not have intuition − tag-names do not provide semantics for machines. − DTDs or XML Schema specify the structure of documents, not the meaning of the document contents − XML lacks a semantic model − has only a "surface model”, i.e. tree Source: Semantic Web, John Davies, BT, 2003.
- 24. XML: limitations for semantic markup − XML representation makes no commitment on: − Domain specific ontological vocabulary − Which words shall we use to describe a given set of concepts? − Ontological modelling primitives − How can we combine these concepts, e.g. “car is a-kind-of (subclass-of) vehicle” requires pre-arranged agreement on vocabulary and primitives Only feasible for closed collaboration agents in a small & stable community pages on a small & stable intranet .. not for sharable Web-resources Source: Semantic Web, John Davies, BT, 2003. 24
- 25. Sematic Web technologies −XML provide a metadata format. −It defines the elements but does not provide any modelling primitive nor describes the meaningful relations between different elements. −Using semantic technologies can help to solve some of these issues. 25
- 26. The Semantic Web − “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in co-operation.“ (Tim Berners-Lee et al, 2001) 26 Image source: Miller 2004
- 27. Resource Description Framework (RDF) − A world Wide Web Consortium (W3C) recommendation − Relationships between documents − Consisting of triples or sentences: − <subject, property, object> − <“Sensor”, hasType, “Temperature”> − <“Node01”, hasLocation, “Room_BA_01” > − RDFS extends RDF with standard “ontology vocabulary”: − Class, Property − Type, subClassOf − domain, range 27
- 28. RDF for semantic annotation − RDF provides metadata about resources − Object -> Attribute-> Value triples or − Object -> Property-> Subject − It can be represented in XML − The RDF triples form a graph 28
- 30. RDF Graph- an instance 30 15 Measurement#0 001 hasValue hasTime -0.589444 08:15 51.243057 C hasLongitude hasLatitude hasUnit
- 32. Let’s add a bit more structure (complexity/modularity?) 32 xsd:decimal Location hasValue hasTime xsd:double xsd:time xsd:double xsd:string hasLongitude hasLatitude hasUnit Measurement hasLocation
- 33. An instance of our model 33 15 Location #0126 hasValue hasTime 51.243057 08:15 -0.589444 C hasLongitude hasLatitude hasUnit Measurement#0 001 hasLocation
- 34. RDF: Basic Ideas −Resources −Every resource has a URI (Universal Resource Identifier) −A URI can be a URL (a web address) or a some other kind of identifier; −An identifier does not necessarily enable access to a resources −We can think of a resources as an object that we want to describe it. −Car −Person −Places, etc. 34
- 35. RDF: Basic Ideas − Properties − Properties are special kind of resources; − Properties describe relations between resources. − For example: “hasLocation”, “hasType”, “hasID”, “sratTime”, “deviceID”,. − Properties in RDF are also identified by URIs. − This provides a global, unique naming scheme. − For example: − “hasLocation” can be defined as: − URI: http://www.loanr.it/ontologies/DUL.owl#hasLocation − SPARQL is a query language for the RDF data. − SPARQL provide capabilities to query RDF graph patterns along with their conjunctions and disjunctions. 35
- 36. Ontologies − The term ontology is originated from philosophy. In that context it is used as the name of a subfield of philosophy, namely, the study of the nature of existence. − In the Semantic Web: − An ontology is a formal specification of a domain; concepts in a domain and relationships between the concepts (and some logical restrictions). 36
- 37. Ontologies and Semantic Web − In general, an ontology formally describes a domain of discourse. − An ontology consists of a finite list of terms and the relationships between the terms. − The terms denote important concepts (classes of objects) of the domain. − For example, in a university setting, staff members, students, courses, modules, lecture theatres, and schools are some important concepts. 37
- 38. Web Ontology Language (OWL) − RDF(S) is useful to describe the concepts and their relationships, but does not solve all possible requirements − Complex applications may want more possibilities: − similarity and/or differences of terms (properties or classes) − construct classes, not just name them − can a program reason about some terms? e.g.: − each «Sensor» resource «A» has at least one «hasLocation» − each «Sensor» resource «A» has maximum one ID − This lead to the development of Web Ontology Language or OWL. 38
- 39. OWL −OWL provide more concepts to express meaning and semantics than XML and RDF(S) −OWL provides more constructs for stating logical expressions such as: Equality, Property Characteristics, Property Restrictions, Restricted Cardinality, Class Intersection, Annotation Properties, Versioning, etc. Source: http://www.w3.org/TR/owl-features/ 39
- 40. Ontology engineering − An ontology: classes and properties (also referred to as schema ontology) − Knowledge base: a set of individual instances of classes and their relationships − Steps for developing an ontology: − defining classes in the ontology and arranging the classes in a taxonomic (subclass–superclass) hierarchy − defining properties and describing allowed values and restriction for these properties − Adding instances and individuals
- 41. Basic rules for designing ontologies − There is no one correct way to model a domain; there are always possible alternatives. − The best solution almost always depends on the application that you have in mind and the required scope and details. − Ontology development is an iterative process. − The ontologies provide a sharable and extensible form to represent a domain model. − Concepts that you choose in an ontology should be close to physical or logical objects and relationships in your domain of interest (using meaningful nouns and verbs).
- 42. A simple methodology 1. Determine the domain and scope of the model that you want to design your ontology. 2. Consider reusing existing concepts/ontologies; this will help to increase the interoperability of your ontology. 3. Enumerate important terms in the ontology; this will determine what are the key concepts that need to be defined in an ontology. 4. Define the classes and the class hierarchy; decide on the classes and the parent/child relationships. 5. Define the properties of classes; define the properties that relate the classes. 6. Define features of the properties; if you are going to add restriction or other OWL type restrictions/logical expressions. 7. Define/add instances. 42
- 43. Other representation forms- JSON − JSON (JavaScript Object Notation) is a lightweight data- interchange/representation format. − JSON is easy to read and publish for human users. − It is also easy for machines to parse and generate. − JSON is based on the JavaScript Programming Language. − JSON is built on two structures: − A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array. − An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence. 43Source: http://www.json.org
- 45. JSON-LD − JSON-LD is a lightweight syntax to serialize Linked Data (e.g. RDF data connected to other resources) in JSON. − JSON-LD allows existing JSON to be interpreted as Linked Data with minimal changes. − It is primarily intended to be a way to use Linked Data in Web-based programming environments, to build interoperable Web services, and to store Linked Data in JSON-based storage engines. − JSON-LD is 100% compatible with JSON and the large number of JSON parsers and libraries available today can be reused. 45 Source: https://www.w3.org/TR/json-ld/
- 46. JSON-LD Example 46 A context is used to map terms to IRIs. Terms are case sensitive and any valid string that is not a reserved JSON-LD keyword can be used as a term. JSON-LD Context: https://www.w3.org/TR/json-ld/#the-context
- 47. Semantic technologies in the IoT −Applying semantic technologies to the IoT can support: −interoperability −effective data access and integration −resource discovery −reasoning and processing of data −information extraction (for automated decision making and management) 47
- 48. 48 Data/Service description frameworks −There are standards such as Sensor Web Enablement (SWE) set developed by the Open Geospatial Consortium that are being adopted in industry and academia. −While such frameworks provide some interoperability, semantic technologies are increasingly seen as key enabler for integration of IoT data and broader Web systems.
- 49. 49 Sensor Markup Language (SensorML) Source: http://www.mitre.org/ The Sensor Model Language Encoding (SensorML) defines models and XML encoding to represent the geometric, dynamic, and observational characteristics of sensors and sensor systems.
- 50. Semantic modelling − Lightweight: experiences show that a lightweight ontology model that well balances expressiveness and inference complexity is more likely to be widely adopted and reused; also large number of IoT resources and huge amount of data need efficient processing − Compatibility: an ontology needs to be consistent with those well designed, existing ontologies to ensure compatibility wherever possible. − Modularity: modular approach to facilitate ontology evolution, extension and integration with external ontologies. 50
- 51. Existing models- SSN Ontology − W3C Semantic Sensor Network Incubator Group’s SSN ontology (mainly for sensors and sensor networks, platforms and systems). http://www.w3.org/2005/Incubator/ssn/
- 52. 52 SSN Ontology Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
- 53. 53 53 W3C SSN Ontology V.1 makes observations of this type Where it is What it measures units SSN-XG ontologies SSN-XG annotations SSN-XG Ontology Scope
- 54. What old SSN did not model − Sensor types and models − Networks: communication, topology − Representation of data and units of measurement − Location, mobility or other dynamic behaviours − Control and actuation − …. 54
- 55. Web of Things − Integrating the real world data into the Web and providing Web-based interactions with the IoT resources is also often discussed under umbrella term of “Web of Things” (WoT). − WoT data is not only large in scale and volume, but also continuous, with rich spatiotemporal dependency. 55
- 56. Web of Things − Connecting sensor, actuator and other devices to the World Wide Web. −Things‘ data and capabilities are exposed as web data/services. − Enables an interoperable usage of IoT resources (e.g. sensors, devices, their data and capabilities) byproviding web based discovery, access, tasking, and alerting. 56
- 57. Web of Things WSN WSN WSN WSN WSN Network-enabled Devices Semantically annotate data 57 Gateway CoAP HTTP CoAP CoAP HTTP 6LowPAN Semantically annotate data http://mynet1/snodeA23/readTemp? WSN MQTT MQTT Gateway
- 58. IoT data: semantic related issues − The current IoT data communications often rely on binary or syntactic data models which lack of providing machine interpretable meanings to the data. −Syntactic representation or in some cases XML-based data −Often no general agreement on annotating the data; −requires a pre-agreement between different parties to be able to process and interpret the data; −Limited reasoning based on the content and context data −Limited interoperability in data and resource/device description level; −Data integration and fusion issues.
- 59. Requirements −Structured representation of concepts − Machine-interpretable descriptions − Processing and interpretation mechanisms −Access mechanism to heterogeneous resource descriptions with diverse capabilities. −Automated interactions and integration with existing applications.
- 60. What are the challenges? − The models provide the basic description frameworks, but alignment between different models and frameworks are required. − Semantics are the starting point, reasoning and interpretation of data is required for automated processes. − Real interoperability happens when data/services from different frameworks and providers can be interchanged and used with minimised intervention.
- 61. 61 There are several good models and description frameworks; The problem is that having good models and developing ontologies are not enough. Semantic descriptions are intermediary solutions, not the end product. They should be transparent to the end-user and probably to the data producer as well.
- 62. Possible solutions −The semantic Web has faced similar problems in the past. −Proposed solution: using machine-readable and machine-interpretable meta-data −Important: not all machine-readable are machine- interpretable. −Well defined standards and description frameworks: RDF, OWL, SPARQL. −Variety of open-source, commercial tools for creating/managing/querying and accessing semantic data. −Some of the tools/libraries include: Jena, Sesame, Protégé, …
- 63. Possible solutions −An Ontology defines conceptualisation of a domain. −Terms and concepts −A common vocabulary −Relationships between the concepts −There are several existing and emerging ontologies in the IoT domain. −Automated annotation methods, dynamic semantics; 63
- 64. How to adapt the solutions −Creating ontologies and defining data models are not enough −tools to create and annotate data −data handling components −Complex models and ontologies may look good, but −design lightweight versions for constrained environments −think of practical issues −make it as much as possible compatible and/or link it to the other existing ontologies
- 65. How to adapt the solutions −Domain knowledge and instances −Common terms and vocabularies −Location, unit of measurement, type, theme, … −Link it to other resources −In many cases, semantic annotations and semantic processing should be intermediary not the end products. 65