Discovering Things and Things’ data/services

transcript

Discovering Things and

Things’ data/services

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

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

To scale:

Things and their data/service need to beDiscoverable, accessible, interoperable

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.

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

GatewayGateway

Core networkCore network

Network Connection

Logical Connection

− 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)

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

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

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

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

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

Location

Query attributes Information

Repository (IR)(archived data)

# location# type

Discovery Server (DS)

Gateway

Device/Sensor domain

Network/Back-enddomain

Application/userdomain

[ # lo

Distributed/scalable

Meta-data (semantics) plays a key role

- 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

Looking back, looking forward

Data/service discovery is a step forward but the key goal is:

information extraction and knowledge discovery

Large-scale data discovery