A Web of Things Based Eco-System for Urban Computing - Towards Smarter Cities
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The document discusses the development of a Web of Things (WoT)-based ecosystem aimed at enhancing urban computing for smarter cities, focusing on real-time data discovery, processing, and user-information delivery. Key components include sensor data streams, middleware for big data analysis, and service composition to provide contextual mobile applications. It emphasizes the need for semantic interoperability and advanced search mechanisms to improve urban environments through better data integration and user engagement.
A Web of Things Based Eco-System for Urban Computing - Towards Smarter Cities
- 1. A Web of Things Based Eco-System for Urban Computing - Towards Smarter Cities Andreas Kamilaris Marie Curie Postdoc Fellow IRTA-UAB Barcelona, Spain ICT2017 Limassol, Cyprus May 4, 2017
- 2. • Real-time discovery of data streams and sensing of the environment • Understanding of data discovered • Fast data processing • Efficient processing of complicated event logics • Filtering of relevant data • Useful information to the user in different urban scenarios. Requirements for Smart City Frameworks
- 3. Web of Things • Designed to connect “things” to the Web • A combination of • Approaches • Software Architectures • Interfaces
- 4. • Increase Interoperability among IoT platforms • Mitigate Silo Architecture • Avoid Multiple and Conflicting Standards • Global and Easy Discovery of Devices • Datasets (produced by WoT devices) available as Open Data on the Web Why we need Web of Things?
- 5. A WoT-Based Eco-System for Smart Cities
- 6. Eco-System Components 1. WoT-based sensor data streams 2. Discovery of WoT devices, services and sensor data streams 3. Middleware performing big data analysis and CEP 4. Publish/subscribe messaging queues 5. ICT technologies such as mobile applications 6. Service composition, i.e. urban mashups 7. Semantic web technologies
- 7. Component #1: WoT-based sensor data streams • Devices fully integrated to the Web. – Directly by embedding Web servers on them. – Indirectly by means of gateways. • Expose their sensing services as RESTful web services.
- 8. Component #2: Discovery of WoT devices & services • Machines needs to automatically discover devices/things and their description • Search Space is the whole Web • Geo-Spatial Mapping • Movable Objects/Things • Require Frequent Updates in Indexes • Semantic Annotation to describe things
- 9. Component #2: WOTS2E • A Search engine to discover semantic meta-description of things • Crawls the Web to discover Linked Data Sources • Analyzes Linked Data sources to identify relevant WoT devices • SPARQL queries and data endpoints Andreas Kamilaris, Semih Yumusak and Muhammad Intizar Ali. WOTS2E: A Search Engine for a Semantic Web of Things. In Proc. of the IEEE World Forum on Internet of Things (WF-IoT), Reston, VA, USA, December 2016.
- 10. Component #3: Middleware for big data analysis • Middleware between smart city applications and sensor data streams are needed for processing complex events and for analyzing big data. • Real-world applications in the WoT space require reasoning capabilities that can handle incomplete, diverse and unreliable input. • The Automated Complex Event Implementation System (ACEIS) is a quality-aware adaptive CEP platform for urban data streams. 1. Each sensor data stream is annotated with QoS and QoI metrics. 2. ACEIS receives an event service request and composes the most suitable data streams. 3. It then transforms the event service composition into a stream query to be deployed and executed on a stream engine (i.e. CQELS, C-SPARQL) to evaluate the complex event pattern specified in the event service request.
- 11. Component #3: ACEIS Semantic Annotation ACEIS Core Resource Management Application Interface Knowledge Base QoI/QoS Stream Description Data Mgmt, Indexing, Caching User Input Event Request Data Federation Resource Discovery Event Service Composer Composition Plan Subscription Manager Query Transformer Query Engine Query Results Constraint Validation Constraint Violation Adaptation Manager Data Store IoT Data Stream Social Data Stream F. Gao, M. I. Ali, and A. Mileo. Semantic Discovery and Integration of Urban Data Streams. In Proc. of the Fifth International Conference on Semantics for Smarter Cities, 2014.
- 12. Component #4: Publish/subscribe messaging queues • Offload total network traffic. • Decouple producers from consumers. • RabbitMQ RabbitMQ - Messaging that just works: https://www.rabbitmq.com/
- 13. Component #5: ICT technologies • Mobile apps • Web apps • Big data analysis on the cloud or regional (fog) • Pervasive apps – augmented reality
- 14. Component #6: Service composition • Mobile apps targeting smart cities locate and interact with environmental services, provided by sensors installed at various urban locations. • Informing the user about existing environmental conditions: – A local view of the urban environment, and are able to take only local decisions, – Communicate with smart city middleware (such as ACEIS), which would assist them in taking more informed, broader decisions, taking into account the whole city infrastructure
- 15. Component #6: UrbanRadar A. Kamilaris and A. Pitsillides. The Impact of Remote Sensing on the Everyday Lives of Mobile Users in Urban Areas. In Proc. of the International Conference on Mobile Computing and Ubiquitous Networking (ICMU), Singapore, January 2014. • Urban Mashups: Web mashups involving real entities • Opportunistic physical mashups, validated only when the local environmental conditions support the sensor-based services defined by the mashups.
- 16. Component #7: Semantic web technologies • Semantics provide seamless data integration, combination and reuse with minimal effort. • Use of lightweight information models that are developed on top of well-known ontologies, such as SSN and OWL-S. – Streams coming from urban sensors using the Stream Annotation Ontology (SAO) – Events detected relating to smart cities using the Complex Event Ontology (CEO)
- 17. Component #7: Semantic web technologies
- 18. Component #7: Semantics in ACEIS • A sensor service description is annotated as: sdesc = (td, g, qd, Pd, FoId, fd) type grounding QoS Observed Properties Feature Of Iterest Pd → FoId • Similarly, a sensor service request is annotated: sr = (tr, Pr, FoIr, fr, pref, C) type Requested Properties Feature of Interest Pd → FoId no grounding NFP Constraint and Preferences
- 19. All Components of the WoT-Based Eco-System 1 2 3 45 6 7
- 20. Case Study: Traffic Monitoring and Journey Planner • 449 pairs of traffic sensors were deployed at the city of Aarhus, Denmark • Travel Planner mobile app • ACEIS calculated the ideal route for its users while commuting, taking into account their current context • User preferences: weather conditions, traffic and people intensity, traffic schedules, QoS and QoI. • Real-time data analytics, continuously monitoring user context and relevant events (e.g. traffic accidents) on the planned route.
- 21. Case Study: Traffic Monitoring and Journey Planner 1 3 CityPulse-Journey-Planner: https://github.com/CityPulse/CityPulse-Journey-Planner 2 4
- 22. Conclusion • Semantic search and real-time discovery are essential for Web of Things, especially in smart city scenarios. • Mobile location-based services and real-time big data analytics will facilitate the filtering of vast amount of sensory data into relevant information that would enhance the quality of life of citizens, while moving within their cities. • Semantic interoperability is key for future intelligence in urban eco-systems. • Technology is already here!
- 23. Future Work • Improve the search mechanism of WOTS2E. • A user-friendly website, to incrementally let users to access the discovered lists of services in a well-organized way. • Larger-scale case studies/deployments in various cities, involving thousands of sensor devices/services such as dust, water pollution, radiation, dangerous chemicals and heavy metals in foods. • Assess the eco-system’s acceptance to citizens involved, their potential engagement and behavioral change in a participatory-based model. • Privacy and security.
- 24. Thank you! Andreas Kamilaris (andreas.kamilaris@irta.cat) ICT2017 Limassol, Cyprus May 4, 2017
- 25. WOTS2E: ArchitectureWOTS2E: Implementation/Analysis • Discovered patterns are used as an input to our web crawlers, in order to search the web for available SPARQL endpoints. • For web crawling, we used a meta-crawling service called SpEnD. • SpEnD exploits the search functionality available over popular search engines to accelerate the performance of web crawling.
- 26. WOTS2E: ArchitectureWOTS2E: Evaluation • From the discovered 638 active SPARQL endpoints, we examined them one by one for relevance to IoT/WoT Ontology Number of Endpoints SSN 13 DBPedia 13 SmartBuilding 3 DogOnt 2 DUL 2 km4city 2 OpenEI 2 RDFS, SKOS 4 Fan Fpai, Fiemser, IoT, PROV, SAREF 5 (once each ontology)
- 27. WOTS2E: ArchitectureWOTS2E: Evaluation • IoT/WoT-specific triples from the endpoints Ontology Number of Triples SSN 1.433,248 DUL 182 km4city 56 Fiemser 50 OpenIoT 44 SmartBuilding 36 DogOnt 24 SAREF 4 Fan Fpai 2