Bringing Wireless Sensing to its full potential


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Bringing Wireless Sensing to its full potential

  1. 1. ”Bringing Wireless Sensing to its full potential” Wireless Sensor Networks TKT-2456 Multimedia group Adrian Hornsby
  2. 2. … bringing wireless sensor to its full potential Outline … sensing the future Search for Wisdom ➢ User and its Desires ➢ Internet of Things ➢ … bringing the web into sensors 6LoWPAN ➢ Semantic Sensor Web ➢ Efficient XML Interchange (EXI) ➢
  3. 3. … sensing the future Search for wisdom Understanding the User: Search for wisdom ➢ Leveraging Information ➢ Distil wisdom Interacting with the world ➢ → knowledge hierachy Assimilate knowledge Compare information Collect data Observe facts proto-facts
  4. 4. … sensing the future Climbing the data federation pyramid Research emphasis within the ➢ biology and computer science communities in the 1980s. Extreme diversity in physical ➢ hardware, OS, DBs, software and immature networking protocols hampered the sharing of data. Emergence of the web ➢ Flexible format for data exchange ➢ Annotation and attribute in DB ➢ resulting in new discoveries
  5. 5. … sensing the future A world of data and sensors Internet of Things = 1012 Fringe Internet = 109 Core Internet = 106
  6. 6. … sensing the future Desires … from stove-piped sensor to global sensing Quickly discover & retrieve information from sensors ➢ Meet user needs ➢ Location, observation, quality, ability to fuse information ➢ Standard sensor descriptions ➢ understandable by users, softwares and other sensors ➢ Subscribe to and receive alerts from sensors ➢ Standardized web-services to access information ➢ Sensor capable of responding to other sensors ➢ Autonomous ➢ Adaptable, mobile and flexible ➢
  7. 7. … sensing the future Closing the gap between users, Internet and sensors Web Services Communication protocol ➢ Interface description and information ➢ URI (Universal Resource Identifier) ➢ User Internet Sensors
  8. 8. … bringing the web into sensors Benefits of IP Open, long-lived, reliable standard ➢ Easy learning-curve ➢ Transparent Internet integration ➢ Network maintainability ➢ Proven global scalability ➢ “You never lose with IP” ➢ IP for smaller devices supported by Standards and Organization: ➢ IPSO alliance ➢ IETF ➢ IEEE ➢
  9. 9. … bringing the web into sensors Applications for IP-based WSN Wireless sensor and actuator networks (WS&AN) , Environmental Sensor Networks (ESN), Object Sensor Networks (OSN) or Body Sensor Network (BSN)
  10. 10. … bringing the web into sensors 6LoWPAN – IP for low power devices IETF Standard for IPv6 over IEEE 802.15.4 80% compression of headers ➢ Rich and flexible features ➢ Auto-configuration ➢ IPv6 fragmentation ➢ UDP + ICMP ➢ Mesh forwarding ➢ Common Socket API ➢ Super compact implementation Sockets ➢ Direct end-to-end Internet integration ➢ UDP + ICMP Extremely scalable ➢ IPv6 + LoWPAN 802.15.4 MAC 2.4 GHz CSS UWB
  11. 11. … bringing the web into sensors 6LoWPAN features Support for 64-bit and 16-bit 802.15.4 addressing 16-bit addresses can automatically be assigned ➢ Extreme header compression ➢ Unicast, broadcast and multicast support ➢ Fragmentation ➢ 1260 byte IPv6 frames -> 127 byte 802.15.4 frames ➢ Link-layer mesh routing support ➢ Original source & final destination addresses ➢ Hops left ➢ Routing decision made hop-by-hop ➢
  12. 12. … bringing the web into sensors 6LoWPAN – IP for low power devices •
  13. 13. … bringing the web into sensors Natural next step To infer high-level knowledge, sensor data needs to be: ➢ filtered, ➢ aggregated, ➢ correlated ➢ and translated. ➢ Data federation pyramid ➢ After network come the data representation ➢
  14. 14. … bringing the web into sensors Data representation: Challenges Lack of Uniform operations and standard representation of sensor ➢ data No means for resource reallocation and resource sharing ➢ Deployment and usage tightly coupled with location, application and ➢ device employed → Lack of interoperability
  15. 15. … bringing the web into sensors Need for Interoperability Ability for two or more autonomous, heterogeneous, distributed ➢ entities to communicate and cooperate despite differences in language, context, format or content. Should be able to interact with one another in meaningful ways ➢ without special effort by the user – the data producer or consumer → Standard XML format for data representation
  16. 16. … bringing the web into sensors Survey: Sensor data management frameworks GSN (Global Sensor Network, Digital Enterprise Research Institute) ➢ http:// Hourglass (Harvard) ➢ An Infrastructure for Connecting Sensor Networks and Applications ➢ IrisNet (Intel & Carnegie Mellon University) Internet-Scale Resource-Intensive Sensor Network Service Sensorweb Research Laboratory ➢ … and more !! → only localized interoperability
  17. 17. … bringing the web into sensors Standard-based frameworks SensorWeb project at University of Melbourne ➢ 52°North's Sensor Web Community ➢ NASA JPL/GSFC SensorWeb, Northrop Grumman's PULSENet ➢
  18. 18. … bringing the web into sensors Open Geospatial Consortium (OGC) Sensor Web Enablement Framework Consortium of 330+ companies, government agencies, and ➢ academic institutes Open Standards development by consensus process ➢ Interoperability Programs provide end-to-end implementation ➢ and testing before specification approval Develop standard encodings and Web service interfaces ➢ Sensor Web Enablement ➢
  19. 19. … bringing the web into sensors Sensor Web Enablement - Languages Information Model and Sensor and Observations and Processing Description Sensing Language Observation & SensorML Measurements (SML) (OM) GeographyML TransducerML (GML) (TML) Common Model for Multiplexed, Real Time Geographical Streaming Protocol Information
  20. 20. … bringing the web into sensors Sensor Web Enablement – Web Services
  21. 21. … bringing the web into sensors Sensor Web Enablement - Components 1. Sensor Model Language (SensorML) – The general models and XML encodings for sensors and observation processing. 2. Observations & Measurements (O&M) - The general models and XML encodings for sensor observations and measurements. 3. TransducerML (TML) – A model and encoding for streaming multiplexed data from a sensor system, and for describing the system and data encoding. 4. Sensor Observation Service (SOS) – A service by which a client can obtain observations from one or more sensors/platforms (can be of mixed sensor/platform types). 5. Sensor Planning Service (SPS) – A standard service for requesting user-driven acquisitions an observations. 6. Sensor Alert Service (SAS) – A service for publishing and subscribing to alert from sensors. 7. Web Notification Service (WNS) – Standard web service for asynchronous delivery of messages or alerts.
  22. 22. … bringing the web into sensors SensorML: building block Provides standard models and an XML encoding for describing sensors and measurement processes. Can be used to describe a wide range of sensors, including both dynamic and stationary platforms and both in-situ and remote sensors. sensor discovery ➢ sensor geolocation ➢ processing observations ➢ programming mechanism ➢ subscription mechanism ➢
  23. 23. … Semantic Sensor Web What is it ? Adding semantic annotations to existing standard Sensor ➢ Web languages in order to provide semantic descriptions and enhanced access to sensor data This is accomplished with model-references to ontology ➢ concepts that provide more expressive concept descriptions
  24. 24. … Semantic Sensor Web What is it ?
  25. 25. … Semantic Sensor Web RDF: Ressource Description Framework Used for semantically annotating XML documents. ➢ Several important attributes within RDFa include: ➢ → about: describes subject of the RDF triple → rel: describes the predicate of the RDF triple → resource: describes the object of the RDF triple → instanceof: describes the object of the RDF triple with the predicate as “rdf:type”
  26. 26. … Semantic Sensor Web On going work in W3C Semantic Sensor Network (SSN) Incubator group: The mission of the Semantic Sensor Network Incubator Group, part of the Incubator Activity, is to begin the formal process of producing ontologies that define the capabilities of sensors and sensor networks, and to develop semantic annotations of a key language used by services based sensor networks.
  27. 27. … Semantic Sensor Web The big picture Semantic Data Storage Analysis & Query Knowledge Data Feature Detection & Extraction Semantic Annotation Ontologies Sensor Data Internet
  28. 28. … bringing the web into sensors And for Low Power nodes ?? Transfering XML is costly !! (for ultra low power devices) → 1 bit = bandwidth = power Compression and Binarization of XML → Efficient XML Interchange format (EXI) EXI: knowledge based encoding that uses a set of grammars to determine which events are most likely to occur at any given point in an EXI stream and encodes the most likely alternatives in fewer bits ➢
  29. 29. … bringing the web into sensors And for Low Power nodes ?? <?xml version=quot;1.0quot; encoding=quot;UTF-8quot;?> <notebook date=quot;2007-09-12quot;> ➢ <note date=quot;2007-07-23quot; category=quot;EXIquot;>   <subject>EXI</subject>   <body>Do not forget it!</body>  </note>  <note date=quot;2007-09-12quot;>   <subject>Shopping List</subject>   <body>milk, honey</body>  </note> </notebook>
  30. 30. … bringing the web into sensors And for Low Power nodes ?? EXI Grammar (Event Coding) → Productions separated according to their popularity ➢ <?xml version=quot;1.0quot; encoding=quot;UTF-8quot;?> <notebook date=quot;2007-09-12quot;>  <note date=quot;2007-07-23quot; category=quot;EXIquot;>   <subject>EXI</subject>   <body>Do not forget it!</body>  </note>  <note date=quot;2007-09-12quot;>   <subject>Shopping List</subject>   <body>milk, honey</body>  </note> </notebook>
  31. 31. … Bringing Wireless Sensor to its full potential Conclusion In the near future: More Users, more Sensors, more Data ➢ Wide Integration with Internet through IP protocol ➢ Advanced data representation with XML ➢ Semantic for better sensor information access ➢ Knowledge even from ultra low power device using EXI ➢ All through global standards (W3C, IETF, ...) ➢
  32. 32. … Bringing Wireless Sensor to its full potential References: Standards & Projects (1) IPSO Alliance - (2) 6LoWPAN: - (3) W3C Semantic Sensor Network Incubator group - - (4) OGC – Sensor Web Enablement WG: (5) Sensor Standards and Data Harmonization (NIST) - (6) Marine Metadata Interoperability - (7) (8) (9) W3C other: (1) Geospatial Incubator Group - (2) Delivery context ontology (3) Product Modelling Incubator (10) EXI:
  33. 33. … Bringing Wireless Sensor to its full potential References: publications (1) Li Ding, Pranam Kolari, Zhongli Ding, Sasikanth Avancha, Tim Finin, Anupam Joshi. Using Ontologies in the Semantic Web: A Survey (2) Cory Henson, Josh Pschorr,Amit Sheth, Krishnaprasad Thirunarayan, quot;SemSOS: Semantic Sensor Observation Service,quot; in Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Baltimore, MD, May 18-22, 2009. (3) Payam M. Barnaghi, Stefan Meissner, Mirko Presser, and Klaus Moessner, quot;Sense and Sensíability: Semantic Data Modelling for Sensor Networksquot;, to appear, in Proceedings of the ICT Mobile Summit 2009, June 2009. (4) Lily Li, Kerry Taylor: A Framework for Semantic Sensor Network Services. ICSOC 2008: 347-361 (5) Amit Sheth, Cory Henson, and Satya Sahoo, quot;Semantic Sensor Web,quot; IEEE Internet Computing, July/August 2008, p. 78-83. (6) Alex Wun, Milenko Petrovi, and Hans-Arno Jacobsen. A system for semantic data fusion in sensor networks. In DEBS í07: Proceedings of the 2007 inaugural international conferenceon Distributed event-based systems, pages 75-79, New York, NY, USA, 2007. ACM. (7) M. Eid, R. Liscano, and A. El Saddik. A universal ontology for sensor networks data. Computational Intel ligence for Measurement Systems and Applications, 2007. CIMSA 2007. IEEE International Conference on, pages 59–62, June 2007 (8) Micah Lewis, Delroy Cameron, Shaohua Xie, Budak Arpinar,ES3N: A Semantic Approach to Data Management in Sensor Networks. Semantic Sensor network workshop, the 5th International Semantic Web Conference ISWC 2006, November 5-9, Athens, Georgia, USA 2006 (9) Hideyuki Kawashima, Yutaka Hirota, Satoru Satake, and Michita Imai. Met: A real world oriented metadata management system for semantic sensor networks. In Proc. of the International Workshop on Data Management for Sensor Networks (DMSN, pages 588{599, 2006. (10) Russomanno, D.J., Kothari, C., Thomas, O.: Sensor ontologies: from shallow to deep models. Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05. 20-22 March 2005. (11) David J. Russomanno, Cartik R. Kothari, and Omo ju A. Thomas. Building a sensor ontology: A practical approach leveraging iso and ogc models. In IC-AI, pages 637–643, 2005. (12) Semantic Sensor Net: An Extensible Framework. In Proceedings of the International Conference on Computer Network and Mobile Computing, Lecture Notes in Computer Science 3619, pages 1144--1153, 2005. (13) C. Matheus, D. Tribble, M. Kokar, M. Cerutti and S. McGirr. Towards a Formal Pedigree Ontology for Level-One Sensor Fusion. 10th International Command and Control Research and Technology Symposium, McClain, Virginia, June 2005. (14) Holger Neuhaus, Relating Sensor Observations to the Real World, FOIS 2008.