Discovering Things and Things’ data/services

27. Jun 2014

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Discovering Things and Things’ data/services

  1. Discovering Things and Things’ data/services 1 Payam Barnaghi Centre for Communication Systems Research (CCSR) Faculty of Engineering and Physical Sciences University of Surrey Guildford, United Kingdom
  2. Internet of Things RFID oriented WSAN oriented, Distributed WANs, Communication technologies, energy efficiency, routing, … Smart Devices/ Web-enabled Apps/Services, initial products, vertical applications, concepts and demos, … Motion sensor Motion sensor ECG sensor Physical-Cyber-Social Data, Linked-data, semantics, M2M, More products, more heterogeneity, control and monitoring, … Future: Cloud, Big (IoT) Data Analytics, Interoperability, Enhanced Cellular/Wireless Com. for IoT, Real-world operational use-cases and commercial services/applications, more Standards…
  3. We have lots of things, large volumes of data and/or services related to things
  4. Diffusion of innovation image source: Wikipedia IoT
  5. To scale: Things and their data/service need to be Discoverable, accessible, interoperable
  6. 6 Storing, handling and processing the data Image courtesy: IEEE Spectrum
  7. Search and Discovery: We have sophisticated search algorithms for the Web data
  8. But Web search is mainly tuned for: Text-based data, archival data
  9. Web search engines are often Information locators rather than information discovery. Google knowledge graph, Wolfram alpha are some examples towards information/knowledge discovery.
  10. 10 Thing’s Data time location type Query formulating [#location | #type | time][#location | #type | time] Discovery ID Discovery/ DHT Server Data repository (archived data) #location #type #location #type #location #type Gateway Core network Network Connection Logical Connection Data
  11. 11 Query − The typical types of data query for sensory data: − Query based on − Location − Type − Time (freshness of data/historical data) − One of the above + Value range [+ Unit of Measurement] − Type/Location/Time + A combination of Quality of Information attributes − An entity of interest (a feature of an entity on interest)
  12. 12 Types of queries − Exact Query − Q (target, metadata) both target and metadata are known − Target, Type, Location, Time − Meta data: QoI/Unit attributes − Proximate Query − Q (target, metadata) − e.g. approximate Location (location range) − QoI range − Range Query − Q (target, metadata) − Time Range − Queries can be Ad-hoc or they can be based on Pub/Sub
  13. 13 Hashing and Indexing − One method is that each node (Gateway?) contains its own index and search mechanism − Large decentralised data/index structure − Using distributed hash table − Using Hashing the key(s) and querying the network to find the node that contains the key − In conventional ICN often one dimensional key space − In M2M/IoT we need multi-dimensional hash/key space − Proposal: Hashing Type and Location − But then the key challenge is how to decide where to look for data − Split the space − Duplicate the query − How to split the space − Location data − Type − Hierarchical index (hash)
  14. How to index, search and discover: -Dynamic - Multi-modal, - and large-scale (streaming) data
  15. Common Data Models − (semantic) models (W3C SSN, HyperCat, …) − SensorML, OGC/SWE models − Several other ontologies/Semantic models 15
  16. 16 SSN Ontology Ontology Link: M. Compton, et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
  17. Stream annotation 17 Sefki Kolozali, Maria Bermudez-Edo, Daniel Puschmann, Frieder, Ganz, Payam Barnaghi, “A Knowledge-based Approach for Real- Time IoT Data Stream Annotation and Processing”, IEEE iThings 2014.
  18. Data Discovery - Mechanisms that enable the clients to access the IoT data without requiring knowing the actual source of information −Index the available data −Heterogeneous −Distributed −Large scale −Dynamic −Updates the indices −Process the user queries −Search and discover the IoT data 18
  19. Data Discovery Challenges − Indexing each individual data point is computationally expensive and maintaining these indices across the network is problematic − Dynamicity, mobility and unreliability of the data attributes requires the indices to be updated frequently which in turn adds considerable traffic to the network − Searching the attribute space at DS level could be computationally expensive
  20. Data discovery in IoT: A schematic view 20 Time Location Type Query pre- processing Query attributes Information Repository (IR) (archived data) # location # type Discovery Server (DS) Gateway Device/Sensor domain Network/Back-end domain Application/user domain [#location|#Time |Type] Distributed/scalable
  21. Meta-data (semantics) plays a key role But: - Current solutions are often centralised - Use logical reasoning, graph processing - Scalability, especially with large set of updates, is a key challenge
  22. Looking back, looking forward − Data Modelling, semantics are important − Attribute indexing/selection using the semantics − How to index/discover the distributed data? − Data/index distribution − Effective semantics and efficient use of semantics − Reasoning and query processing mechanisms − Data abstraction and pre-processing techniques 22
  23. Looking back, looking forward Data/service discovery is a step forward but the key goal is: information extraction and knowledge discovery 23
  24. Large-scale data discovery 24 time location type Query formulating [#location | #type | time][#location | #type | time] Discovery ID Discovery/ DHT Server Data repository (archived data) #location #type #location #type #location #type Gateway Core network Network Connection Logical Connection Data Seyed Amir Hoseinitabatabaei, Payam Barnaghi, Chonggang Wang, Rahim Tafazolli, Lijun Dong, "A Distributed Data Discovery Mechanism for the Internet of Things", 2014.
  25. − Thank you. @pbarnaghi