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@Discover lots of things in the Internet of Things session at IoTWeek 2014, London, http://www.iot-week.eu/csiro.html
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Discovering Things and
Things’ data/services
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Payam Barnaghi
Centre for Communication Systems Research (CCSR)
Faculty of Engineering and Physical Sciences
University of Surrey
Guildford, United Kingdom
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…
We have lots of things, large volumes of data and/or services
related to things
Diffusion of innovation
image source: Wikipedia
IoT
To scale:
Things and their data/service need to beDiscoverable, accessible, interoperable
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Storing, handling and processing the data
Image courtesy: IEEE Spectrum
Search and Discovery:
We have sophisticated search algorithms for the Web data
But Web search is mainly tuned for:
Text-based data, archival data
Web search engines are often Information locators rather than information discovery.
Google knowledge graph, Wolfram alpha are some examples towards information/knowledge discovery.
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Thing’s Data
timetime
locationlocation
typetype
Query formulatingQuery formulating
[#location | #type | time][#location | #type | time]
Discovery IDDiscovery ID
Discovery/DHT ServerDiscovery/DHT Server
Data repository(archived data)Data repository(archived data)
#location#type
#location#type
#location#type
GatewayGateway
Core networkCore network
Network Connection
Logical Connection
Data
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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)
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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
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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)
How to index, search and discover:
-Dynamic- Multi-modal, - and large-scale (streaming) data
Common Data Models
− (semantic) models (W3C SSN, HyperCat, …)− SensorML, OGC/SWE models− Several other ontologies/Semantic models
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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.
Stream annotation
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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.
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
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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
Data discovery in IoT: A schematic view
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Time
Location
Type
Qu
ery
pre
-p
roce
ssin
g
Query attributes Information
Repository (IR)(archived data)
# location# type
Discovery Server (DS)
Gateway
Device/Sensor domain
Network/Back-enddomain
Application/userdomain
[ # lo
catio
n |#
Tim
e | T
ype
]
Distributed/scalable
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
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
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Looking back, looking forward
Data/service discovery is a step forward but the key goal is:
information extraction and knowledge discovery
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Large-scale data discovery
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timetime
locationlocation
typetype
Query formulatingQuery formulating
[#location | #type | time][#location | #type | time]
Discovery IDDiscovery ID
Discovery/DHT ServerDiscovery/DHT Server
Data repository(archived data)Data repository(archived data)
#location#type
#location#type
#location#type
GatewayGateway
Core networkCore 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.
