How to make data more usable on the Internet of Things


Published on

1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

How to make data more usable on the Internet of Things

  1. 1. 1How to make data more usable on theInternet of ThingsPayam BarnaghiCentre for Communication Systems Research (CCSR)Faculty of Engineering and Physical SciencesUniversity of SurreyMarch 2013
  2. 2. 2Network connected Things and DevicesImage courtesy: CISCO
  3. 3. 3Sensor devices are becoming widely available- Programmable devices- Off-the-shelf gadgets/tools
  4. 4. 4More “Things” are being connectedHome/daily-life devicesBusiness andPublic infrastructureHealth-care…
  5. 5. 5People Connecting to ThingsMotion sensorMotion sensorMotion sensorECG sensorInternet
  6. 6. 6Things Connecting to Things- Complex and heterogeneousresources and networks
  7. 7. 7Wireless Sensor Networks (WSN)Sinknode GatewayCore networke.g. InternetGatewayEnd-userComputer services- The networks typically run Low Power Devices- Consist of one or more sensors, could be different type of sensors (or actuators)
  8. 8. 8Key characteristics of IoT devices−Often inexpensive sensors (actuators) equipped with a radiotransceiver for various applications, typically low data rate ~10-250 kbps.−Deployed in large numbers−The sensors should coordinate to perform the desired task.−The acquired information (periodic or event-based) isreported back to the information processing centre (orsometimes in-network processing is required)−Solutions are application-dependent.8
  9. 9. 9Beyond conventional sensors− Human as a sensor (citizen sensors)− e.g. tweeting real world data and/or events− Virtual (software) sensors− e.g. Software agents/services generating/representingdataRoad block, A3Road block, A3Suggest a different route
  10. 10. 10Cyber, Physical and Social Data
  11. 11. 11Citizen SensorsSource: How Crisis Mapping Saved Lives in Haiti, Ushahidi Haiti Project (UHP).
  12. 12. 12Cosm- Air Quality Egg
  13. 13. 13Cosm- data readingsTagsData formatsLocation
  14. 14. 14Making Sense of DataIn the next few years, sensor networks will produce10-20 time the amount of data generated by socialmedia. (source: GigaOmni Media)
  15. 15. 15Things, Data, and lots of itimage courtesy: Smarter Data - I.03_C by Gwen Vanhee
  16. 16. 16Big Data and IoT− "Big data" is a term applied to data sets whose size is beyond theability of commonly used software tools to capture, manage, andprocess the data within a tolerable elapsed time. Big data sizesare a constantly moving target, as of 2012 ranging from a fewdozen terabytes to many petabytes of data in a single data set.”(wikipedia)− Every day, we create 2.5 quintillion bytes of data — so much that90% of the data in the world today has been created in the lasttwo years alone. (source IBM)
  17. 17. 17The seduction of data− Turn 12 terabytes of Tweets created each day into sentimentanalysis related to different events/occurrences or relate them toproducts and services.− Convert (billions of) smart meter readings to better predict andbalance power consumption.− Analyze thousands of traffic, pollution, weather, congestion, publictransport and event sensory data to provide better trafficmanagement.− Monitor patients, elderly care and much more…Adapted from: What is Bog Data?, IBM
  18. 18. 18Do we need all these data?
  19. 19. 19“Raw data is both an oxymoron andbad data”Geoff Bowker, 2005Source: Kate Crawford, "Algorithmic Illusions: Hidden Biases of Big Data", Strata 2013.
  20. 20. 20IoT Data in the CloudImage courtesy: http://images.mathrubhumi.com
  21. 21. 21Perceptions and IntelligenceDataInformationKnowledgeWisdomRaw sensory dataStructured data (withsemantics)Abstraction and perceptionsActionable intelligence
  22. 22. 22Change in communication paradigmSinknode GatewayCore networke.g. Internet End-userDataSenderDataReceiverA sample data communication in conventional networksA sample data communication in WSNFire! Some bits01100011100
  23. 23. 23− Collaboration and in-network processing− In some applications a single sensor node is not able to handlethe given task or provide the requested information.− Instead of sending the information form various source to anexternal network/node, the information can be processed inthe network itself.− e.g. data aggregation, summarisation and then propagating theprocessed data with reduced size (hence improving energyefficiency by reducing the amount of data to be transmitted).− Data-centric− Conventional networks often focus on sending data betweentwo specific nodes each equipped with an address.− Here what is important is data and the observations andmeasurements not the node that provides it.Required mechanisms
  24. 24. “People want answers, not numbers”(Steven Glaser, UC Berkley)Sinknode GatewayCore networke.g. InternetWhat is the temperature at home?Freezing!
  25. 25. 25IoT Data alone is not enough− Domain knowledge− Machine interpretable meta data− Delivery, sharing and representation services− Query, discovery, aggregation services− Publish, subscribe, notification, and accessinterfaces/services
  26. 26. 26Storing, Handling and Processingthe DataImage courtesy: IEEE Spectrum
  27. 27. 27IoT Data Challenges− Discovery: finding appropriate device and data sources− Access: Availability and (open) access to IoT resources anddata− Search: querying for data− Integration: dealing with heterogeneous device, networksand data− Interpretation: translating data to knowledge usable bypeople and applications− Scalability: dealing with large number of devices andmyriad of data and computational complexity ofinterpreting the data.
  28. 28. 28Interpretation of data− A primary goal of interconnecting devices andcollecting/processing data from them is to createsituation awareness and enable applications,machines, and human users to better understandtheir surrounding environments.− The understanding of a situation, or context,potentially enables services and applications tomake intelligent decisions and to respond to thedynamics of their environments.
  29. 29. 29Observation and measurement dataSource: W3C Semantic Sensor Networks, SSN Ontology presentation, Laurent Lefort et al.
  30. 30. 30How to say what a sensor is andwhat it measures?SinknodeGateway
  31. 31. 31Data/Service description frameworks− There are standards such as Sensor Web Enablement(SWE) set developed by the Open Geospatial Consortiumthat are widely being adopted in industry, government andacademia.− While such frameworks provide some interoperability,semantic technologies are increasingly seen as key enablerfor integration of IoT data and broader Web informationsystems.
  32. 32. 32Sensor Markup Language (SensorML)Source:
  33. 33. 33W3C SSN OntologyOntology Link: Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
  34. 34. 3434W3C SSN Ontologymakes observationsof this typeWhere it isWhat itmeasuresunitsSSN-XG ontologiesSSN-XG annotationsSSN-XG Ontology Scope
  35. 35. 35Semantics and IoT data− Creating ontologies and defining data models is not enough− tools to create and annotate data− data handling components− Complex models and ontologies look good, but− design lightweight versions for constrained environments− think of practical issues− make it as compatible as possible and/or link it to the otherexisting ontologies− Domain knowledge and instances− Common terms and vocabularies− Location, unit of measurement, type, theme, …− Link it to other resources− Linked-data− URIs and naming
  36. 36. 36Semantics and sensor dataSource: W. Wang, P. Barnaghi, "Semantic Annotation and Reasoning for Sensor Data", In proceedings of the 4th European Conference on SmartSensing and Context (EuroSSC2009), 2009.
  37. 37. 37Semantics and Linked-data− The principles in designing the linked data aredefined as:− using URI’s as names for things;− using HTTP URI’s to enable people to look up thosenames;− provide useful RDF information related to URI’s that arelooked up by machine or people;− including RDF statements that link to other URI’s toenable discovery of other related concepts of the Web ofData;
  38. 38. 38Linked Sensor data
  39. 39. 39Linked Open DataCollectively, the 203 data sets consist of over 25 billion RDF triples,which are interlinked by around 395 million RDF links (September2010).
  40. 40. 4040Linked IoT DataInternal locationontology (local)Lined-data location(external)
  41. 41. 41Myth and reality− #1: If we create an Ontology our data is interoperable− Reality: there are/could be a number of ontologies for a domain− Ontology mapping− Reference ontologies− Standardisation efforts− #2: Semantic data will make my data machine-understandableand my system will be intelligent.− Reality: it is still meta-data, machines don’t understand it but can interpret it. Itstill does need intelligent processing, reasoning mechanism to process andinterpret the data.− #3: It’s a Hype! Ontologies and semantic data are too muchoverhead; we deal with tiny devices in IoT.− Reality: Ontologies are a way to share and agree on a common vocabulary andknowledge; at the same time there are machine-interpretable and represented ininteroperable and re-usable forms;− You don’t necessarily need to add semantic metadata in the source- it could beadded to the data at a later stage (e.g. in a gateway);− Legacy applications can ignore it or to be extended to work with it.
  42. 42. 42Processing Streaming Sensor Data
  43. 43. 4343Symbolic Aggregate Approximation (SAX)Variable String Length and Vocabulary size.Length: 10, VocSize: 10 Length: 10, VocSize: 4“gijigdbabd” “cdddcbaaab”Green Curve: consists of 100 Samples, Blue Curve: SAX
  44. 44. 44SAX representationSAX Pattern (blue) with word length of 20 and a vocabulary of 10 symbolsover the original sensor time-series data (green)P. Barnaghi, F. Ganz, C. Henson, A. Sheth, "Computing Perception from Sensor Data",in Proc. of the IEEE Sensors 2012, Oct. 2012.fggfffhfffffgjhghfffjfhiggfffhfffffgjhgifggfffhfffffgjhghfff
  45. 45. 45Data Processing Frameworkfggfffhfffffgjhghfff dddfffffffffffddd cccddddccccdddccc aaaacccaaaaaaaaccccdddcdcdcdcddasdddPIR Sensor Light SensorTemperatureSensorRaw sensor datastreamRaw sensor datastreamRaw sensor datastreamAttendance PhoneHotTemperatureColdTemperatureBrightDay-timeNight-timeOffice roomBA0121On goingmeetingWindow hasbeen left open….Temporal data(extracted fromSSN descriptions)Spatial data(extracted fromSSN descriptions)Thematic data(low levelabstractions)ParsimoniousCovering TheoryObservationsPerceptionsDomain knowledgeSAX PatternsRaw Sensor Data(Annotated with SSNOntology)…….PerceptionComputationHigh-levelPerceptions
  46. 46. 46SensorSAXF. Ganz, P. Barnaghi, F. Carrez, “Information Abstraction for Heterogeneous Real World InternetData”, Feb. 2013.
  47. 47. 47Evaluation results of abstractioncreationF. Ganz, P. Barnaghi, F. Carrez, “Information Abstraction for Heterogeneous Real World Internet Data”, Feb. 2013.
  48. 48. 48Data size reductionF. Ganz, P. Barnaghi, F. Carrez, “Information Abstraction for Heterogeneous Real World Internet Data”, Feb. 2013.
  49. 49. 49Enabling the Internet of Things- Diversity range of applications- Interacting with large numberof devices with various types-Multiple heterogeneousnetworks-Deluge of data-Processing and interpretation ofthe IoT data
  50. 50. 50Challenges and opportunities− Providing infrastructure− Publishing, sharing, and access solutions on a global scale− Indexing and discovery (data and resources)− Aggregation and fusion− Trust, privacy and security− Data mining and creating actionable knowledge− Integration into services and applications in e-health, the publicsector, retail, manufacturing and personalized apps.− Mobile apps, location-based services, monitoring control etc.− New business models
  51. 51. 51EventsSemantic Interop event, European Wireless Conference,Guildford, April 2013. at WIMS13: Data Processing and Semantics forAdvanced Internet of Things (IoT) Applications:modeling, annotation, integration, and perception, P.Anantharam, P. Barnaghi, A. Sheth, seminar on Cyber-Physical-Social Computing, Sept.30- Oct. 04, 2013, Organizers: Payam Barnaghi, RameshJain, Amit Sheth, Steffen Staab, Markus Strohmaier.
  52. 52. 52Thank you.
  53. 53. 53Payam BarnaghiCentre for Communication Systems ResearchFaculty of Engineering and Physical SciencesUniversity of