IOT Tutorial

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Data Modelling and Knowledge Engineeringfor the Internet of Things

Wei Wang 1, Cory Henson2, Payam Barnaghi 1

Centre for Communication Systems Research, University of SurreyKno.e.sis Center, Wright State University

Galway City, Ireland, October 8-12, 2012http://knoesis.org/iot-tutorial-ekaw2012/

http://knoesis.org/iot-tutorial-ekaw2012/http://knoesis.org/http://knoesis.org/iot-tutorial-ekaw2012/http://knoesis.org/iot-tutorial-ekaw2012/http://knoesis.org/iot-tutorial-ekaw2012/http://knoesis.org/iot-tutorial-ekaw2012/http://knoesis.org/iot-tutorial-ekaw2012/


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2

Part 1: Introductionto Internet of “Things ”

Image source: CISCO

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Internet of Things

―sensors and actuators embedded in physicalobjects — from containers to pacemakers — arelinked through both wired and wireless networks to

the Internet. ―When objects in the IoT can sense the environment,interpret the data, and communicate with each

other, they become tools for understandingcomplexity and for responding to events andirregularities swiftly

source: http://www.iot2012.org/

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4

―Thing connected to the internet

Source: CISCO

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Future Internet - A new dimension

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Internet of Things - definition

―A world where physical objects are seamlesslyintegrated into the information network, and wherethe physical objects can become active participants

in business processes. ―Services are available to interact with these ―smartobjects over the Internet, query and change theirstate and any information associated with them,taking into account security and privacy issues. ‘ .

Source: Stephan Haller, Internet of Things: An integral Part of the Future Internet, SAP Research, 2009.

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What ―Things can be connected?

Home/daily-life devicesBusiness andPublic infrastructureHealth-care

…

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Sensor devices are becoming widelyavailable

- Programmable devices- Off-the-shelf gadgets/tools

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Application domain

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Why is IoT important?

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Observation and measurement data

Adapted from: W3C Semantic Sensor Networks, SSN Ontology presentation, http://www.w3.org/2005/Incubator/ssn/

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Data is important and IoT will producelots of it!

Sensors and devices provide data about the physical world objects.The observation and measurement data related to an ―object can berelated to an event, situation in the physical world.The processing of turning this data into knowledge/ perception and

using it for decision making, automated control, etc. is another importantphase.Huge amount of data related to our physical world that need to be

PublishedStored (temporary or for longer term)

DiscoveredAccessedProceededUtilised in different applications

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Turning Data into Wisdom

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The ―Things

Embedded device + physical world objectsSensor nodes (e.g. SunSPOT, TelOSB, WASPmote).Mobile devices (e.g. mobile phones, tablets)A set of these that provide information about ( afeature of interest of ) a physical world object (e.g.water level in a tank, temperature of a room).

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Components related to ―Things

Physical world objectse.g. A room, a car, A person;

Feature of Intereste.g. Temperature of the room, Location of the car,heart-rate of the person;

Sensors

e.g. Temperature sensor, GPS, pulse sensorEmbedded device

e.g. WASPmote, SunSPOT, …

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Sensors

Active & Passive SensorsEnergy EfficiencyProcessing capabilitiesNetwork communications

hardware platformssoftware platforms

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RFID

Active Tags and Passive TagsApplications: supply chain, inventory tracking, toolscollection, etc.Limitations:

TechnologyReading rangePhysical limitations

Interference

Security and Privacy

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Hardware components of sensornodes

ControllerMemoryCommunication deviceSensors (or actuators)Power supply

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Example: Radiation Sensor Board(Libelium)

Source: Wireless Sensor Networks to Control Radiation Levels, David Gascón, Marcos Yarza, Libelium, April 2011.

Waspmote

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Energy consumption of the nodes

Batteries have small capacity and recharging couldbe complex (if not impossible) in some cases.The main consumers of the energy are: the

controller, radio, to some extent memory anddepending on the type, the sensor(s).A controller can go to:

―active , ―idle and ―sleep

A radio modem could turn transmitter, receiver, orboth on or off,sensors and memory can be also turned on and off.

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Beyond common sensors

Human as a sensore.g. tweeting real world data and/or events

Virtual sensorse.g. Software agents generating data

Adapted from: The Web of Things, Marko Grobelnik, Carolina Fortuna, Jožef Stefan Institute.

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Actuators

Stepper Motor [1]

Image credits:[1] http://directory.ac/telco-motion.html[2] http://bruce.pennypacker.org/category/theater/

[3] http://www.busytrade.com/products/1195641/TG-100-Linear-Actuator.html[4] http://www.arbworx.com/services/fencing-garden-fencing/

[2]

[3][4]

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Wireless Sensor Networks (WSN)

Image source: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor NetworksHolger Karl, Andreas Willig, chapter 3, Wiley, 2005 .

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Wireless Sensor Networks (WSN)-gateway connection

SunSpots

Information channelControl channel

Directory server

Gateway

Web user/application

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Distributed WSN

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What are the main issues?

HeterogeneityInteroperabilityMobilityEnergy efficiencyScalabilitySecurity

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What is important?

RobustnessQuality of ServiceScalabilitySeamless integrationSecurity, privacy, Trust

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In-network processing

Mobile Ad-hoc Networks are supposed to deliver bits fromone end to the otherWSNs, on the other end, are expected to provideinformation, not necessarily original bits

Gives addition optionsE.g., manipulate or process the data in the network

Main example: aggregation

Applying aggregation functions to a obtain an average value ofmeasurement dataTypical functions: minimum, maximum, average, sum, …

Not amenable functions: median

source: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor NetworksHolger Karl, Andreas Willig, chapter 3, Wiley, 2005 .

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In-network processing- example

Applying Symbolic Aggregate Approximation (SAX)

SAX Pattern (blue) with word length of 20 and a vocabulary of 10 symbolsover the original sensor time-series data (green)

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Data-centric networking

In typical networks (including ad hoc networks), networktransactions are addressed to the identities of specific nodes

A ―node-centric or ―address-centric networking paradigm

In a redundantly deployed sensor networks, specific sourceof an event, alarm, etc. might not be importantRedundancy: e.g., several nodes can observe the same area

Thus: focus networking transactions on the data directly

instead of their senders and transmitters ! data-centricnetworking Principal design change

source: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor NetworksHolger Karl, Andreas Willig, chapter 3, Wiley, 2005 .

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Implementation options fordata-centric networking

Overlay networks & distributed hash tables (DHT)Hash table: content-addressable memory

Retrieve data from an unknown source, like in peer-to-peer networking – with efficientimplementation

Some disparities remain

Static key in DHT, dynamic changes in WSNDHTs typically ignore issues like hop count or distance between nodes when performing alookup operation

Publish/subscribeDifferent interaction paradigm

Nodes can publish data, can subscribe to any particular kind of dataOnce data of a certain type has been published, it is delivered to all subscribes

Subscription and publication are decoupled in time; subscriber and published are agnosticof each other (decoupled in identity);

There is concepts of Semantic Sensor Networks- to annotate sensor resources andobservation and measurement data!

Adapted from: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor NetworksHolger Karl, Andreas Willig, chapter 3, Wiley, 2005 .

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IoT and Semantic technologies

The sensors (and in general ―Things ) are increasingly beingintegrated into the Internet/Web.This can be supported by embedded devices that directlysupport IP and web-based connection (e.g. 6LowPAN andCoAp) or devices that are connected via gatewaycomponents.

Broadening the IoT to the concept of ―Web of Things There are already Sensor Web Enablement (SWE)standards developed by the Open Geospatial Consortium

that 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 information

systems.

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Semantics and IoT resources anddata

Semantics are machine-interpretable metadata (for mark-up), logicalinference mechanisms, query mechanism, linked data solutionsFor IoT this means:

ontologies for: resource (e.g. sensors), observation and measurement

data (e.g. sensor readings), domain concepts (e.g. unit of measurement,location), services (e.g. IoT services) and other data sources (e.g. thoseavailable on linked open data)

Semantic annotation should also supports data represented usingexisting forms

Reasoning /processing to infer relationships and hierarchies betweendifferent resources, dataSemantics (/ontologies) as meta-data (to describe the IoTresources/data) / knowledge bases (domain knowledge).

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A Few Words

onSemantic Web

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SSW Introduction

lives in

has pet

is ahas pet

Person Animal

Concrete FactsResource Description Framework

Semantic Web(according to Farside )

General KnowledgeWeb Ontology Language

Now! – That should clear up a few things around here!

is a

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Semantic Web Stack

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Linked Open Data

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In the last few years, we have seenmany successes …

Knowledge Graph

Watson

AppleSiri

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Google Knowledge Graph

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Sensors and the Web

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Sensors are ubiquitous

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Sensors are small and inexpensive

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Digitization of the physical world

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Leading to …

Improved situationalawareness

Advanced cyber-physicalsystems / applications

Enabling the Internet ofThings

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Enabling the Internet of Things

Situational awareness enables:

Devices/things to function andadapt within their environment

Devices/things to worktogether

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Sensor systems are toooften stovepiped .

Closed centralizedmanagement of sensingresources

Closed inaccessible dataand sensors

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We want to set this data free

With freedom comes responsibilityDiscovery, access, and searchIntegration and interpretationScalability

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Drowning in Data

A cross-country flight from New York to Los Angeles on a Boeing737 plane generates a massive 240 terabytes of data

- GigaOmni Media

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Drowning in Data

In the next few years, sensor networks will produce 10-20time the amount of data generated by social media.

- GigaOmni Media

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Drowning in Data

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Challenges

To fulfill this vision, there are difficult challenges to overcome such as thediscovery, access, search, integration, and interpretation of sensors andsensor data at scale

Discovery finding appropriate sensing resources and data sources

Access sensing resources and data are open and available

Search querying for sensor data

Integration dealing with heterogeneous sensors and sensor data

Interpretation translating sensor data to knowledge usable by people andapplications

Scalability dealing with data overload and computational complexityof interpreting the data

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Solution

Semantic Sensor WebInternet Computing, July/Aug. 2008

Uses the Web as platform formanaging sensor resources and data

Uses semantic technologies forrepresenting data and knowledge,integration, and interpretation

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Solution

Discovery, access, and search Using standard Web services

OGC Sensor Web Enablement

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Solution

Interpretation Abstraction – converting low-level data to high-level knowledge

Machine Perception – w/ prior knowledge and abductive reasoning

IntellegO – Ontology of Perception

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Solution

Scalability Data overload – sensors produce too much data

Computational complexity of semantic interpretation

―Intelligence at the edge – local and distributed integration andinterpretation of sensor data

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SSW Adoption and Applications

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Recall of the Internet of Things

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.

Barnaghi et al 2012, ―Semantics for the Internet of Things: early progress and back to the future

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IoT challenges

Numbers of devices and different users and interactions required.Challenge: Scalability

Heterogeneity of enabling devices and platformsChallenge: Interoperability

Low power sensors, wireless transceivers, communication, and networking for M2MChallenge: Efficiency in communications

Huge volumes of data emerging from the physical world, M2M and newcommunications

Challenge: Processing and mining the data, Providing secure access and preserving andcontrolling privacy.

Timeliness of dataChallenge: Freshness of the data and supporting temporal requirements in accessing thedata

UbiquityChallenge: addressing mobility, ad-hoc access and service continuity

Global access and discoveryChallenge: Naming, Resolution and discovery

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IoT: one paradigm, many visions

Diagram adapted from L. Atzori et al., 2010, ―the Internet of Things: a Survey

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Semantic oriented vision

―The object unique addressing and the representation andstoring of the exchanged information become the mostchallenging issues, bringing directly to a ‗‗Semantic oriented ,perspective of IoT , [Atzori et al., 2010]

Data collected by different sensors and devices is usuallymulti-modal (temperature, light, sound, video, etc.) and diversein nature (quality of data can vary with different devicesthrough time and it is mostly location and time dependent

[Barnaghi et al, 2012]some of challenging issues: representation, storage, andsearch/discovery/query/addressing, and processing IoTresources and data.

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What is expected?

Unified access to data: unified descriptions

Deriving additional knowledge (data mining)

Reasoning support and association to other entities and

resourcesSelf-descriptive data an re-usable knowledge

In general: Large-scale platforms to support discovery andaccess to the resources, to enable autonomous interactions withthe resources, to provide self-descriptive data and associationmechanisms to reason the emerging data and to integrate itinto the existing applications and services.

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Semantic technologies and IoT

There are already Sensor Web Enablement (SWE)standards developed by the Open GeospatialConsortium that are widely adopted.While such frameworks provide certain levels of

interoperability, semantic technologies are seen askey enabler for integration of IoT data and andexisting business information systems.Semantic technologies provide potential support for:

Interoperability and machine automationIoT resource and data annotation, logical inference, query anddiscovery, linked IoT data

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Identify IoT domain concepts

UsersPhysical entitiesVirtual entitiesDevicesResourceServices…

Diagram adapted from IoT-A project D2.1

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IoT domain concepts –


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IoT domain concepts Device, Resource and Service

A Device mediates the interactions between users andentities.The software component that provides information on theentity or enables controlling of the device, is called aResource .A Service provides well-defined and standardisedinterfaces, offering all necessary functionalities for

interacting with entities and related processes.

Definition adapted from De et al, 2012, “ Service modeling for the Internet of Things ”

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Other concepts need to considered

GatewaysDirectoriesPlatformsSystemsSubsystems…

Relationships among themAnd links to existing knowledge base and linked data

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Don‘t forget the IoT data

Sensors and devices provide observation and measurementdata about the physical world objects which also need to besemantically described and can be related to an event,situation in the physical world.

The processing of data into knowledge/ perception and usingit for decision making, automated control, etc.Huge amount of data from our physical world that need to be

AnnotatedPublished

Stored (temporary or for longer term)DiscoveredAccessedProceededUtilised in different applications

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Semantics for IoT resources and data

Semantics are machine-interpretable metadata, logical inferencemechanisms, query and search mechanism, linked data…

For IoT this means:ontologies for: resource (e.g. sensors), observation and measurementdata (e.g. sensor readings), services (e.g. IoT services), domain concepts(e.g. unit of measurement, location) and other data sources (e.g. thoseavailable on linked open data)

Semantic annotation should also supports data represented using existingforms

Reasoning/processing to infer relationships between different resourcesand services, detecting patterns from IoT data

f

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Characteristics of IoT resources

Extraordinarily large numberLimited computing capabilitiesLimited memoryResource constrained environments (e.g., batterylife, signal coverage)Location is important

Dynamism in the physical environmentsUnexpected disruption of services…

h f d

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Characteristics of IoT data

Stream data (depends on time)Transient natureAlmost always related to a phenomenon or qualityin our physical environmentsLarge amountQuality in many situations cannot be assured (e.g.,

accuracy and precision)Abstraction levels (e.g., raw, inferred or derived)…

ili i

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Utilise semantics

Find all available resources (which can provide data)and data related to

“

Room A”

(which is an object inthe linked data)?

What is“

Room A”

? What is its location? returns“

location”

dataWhat type of data is available for

“

Room A”

or that“

location”

?(sensor category types )

Predefined Rules can be applied based on availabledata

(TempRoom_A > 80°

C) AND (SmokeDetectedRoom_A position==TRUE) FireEventRoom_ALearning these rules needs data mining or pattern recognition techniques

S i d lli

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Semantic modelling

Lightweight: experiences show that a lightweight ontologymodel that well balances expressiveness and inferencecomplexity is more likely to be widely adopted and reused;also large number of IoT resources and huge amount of data

need efficient processingCompatibility: an ontology needs to be consistent with thosewell designed, existing ontologies to ensure compatibilitywherever possible.

Modularity: modular approach to facilitate ontology evolution,extension and integration with external ontologies.

E i i d l f d d

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Existing models for resources and data

W3C Semantic Sensor Network Incubator Group ‘s SSN ontology (mainly for sensors and sensornetworks, observation and measurement, and

platforms and systems) Quantity Kinds and Units Used together with the SSN ontologybased on QUDV model OMG SysML(TM)Working group of the SysML 1.2 Revision Task Force(RTF) and W3C Semantic Sensor Network IncubatorGroup

E i i d l f i

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Existing models for services

OWL-S and WSMO are heavy weight models: practical use?Minimal service model

DeprecatedProcedure-Oriented Service Model (POSM) and Resource-OrientedService Model (ROSM): two different models for different servicetechnologiesDefines Operations and MessagesNo profile, no grounding

SAWSDL: mixture of XML, XML schema, RDF and OWLhRESTS and SA-REST: mixture of HTML and reference to asemantic model; sensor services are not anticipated to haveHTML

W3C ‘S SSN l

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W3C ‘S SSN ontology

Diagram adapted from SSN report

S i ti I T d l d t l i

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Some existing IoT models and ontologie s

FP7 IoT-A project ‘s Entity-Resource-Service ontologyA set of ontologies for entities, resources, devices andservices

Based on the SSN and OWL-S ontologyFP7 IoT.est project ‘s service description framework

A modular approach for designing a descriptionframeworkA set of ontologies for IoT services, testing andQoS/QoITechnology independent modelling for services

I T A d l



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IoT-A resource model


I T A d i ti

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IoT-A resource description


I T A i d l

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IoT-A service model


I T A i d i ti

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IoT-A service description


Service modelling in IoT est

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Service modelling in IoT.est

Diagrams adapted from Iot.est D3.1

IoT est service profile highlight

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IoT.est service profile highlight

ServiceType class represents the service technologies: RESTfuland SOAP/WSDL services.

serviceQos and serviceQoI are defined as subproperty ofserviceParameter; they link to concepts in the QoS/QoI

ontology. serviceArea : the area where the service is provided; differentfrom the sensor observation areaLinks to the IoT resources through―exposedBy propertyFuture extension:

serviceNetwork , servicePlatform and serviceDeploymentService lifecycle, SLA…

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Linked data in IoT


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Linked data in IoT

Using URI’

s as names for things;- URI

’

s for naming M2M resources and data (and also streaming data);

Using HTTP URI’

s to enable people to look up those names;- Web-level access to low level sensor data and real world resource

descriptions (gateway and middleware solutions);Providing useful RDF information related to URI

’

s that are looked up bymachine or people;- publishing semantically enriched resource and data descriptions in the

form of linked RDF data;

Including RDF statements that link to other URI’ s to enable discovery ofother related things of the web of data;- linking and associating the real world data to the existing data on the

Web;

Linked data layer for not only IoT

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Linked data layer for not only IoT…

Images from Stefan Decker, http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.png ; linked data diagram: http://richard.cyganiak.de/2007/10/lod/

Creating and using linked sensor data

http://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://fi-ghent.fi-week.eu/files/2010/10/Linked-Data-scheme1.pnghttp://knoesis.org/


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Creating and using linked sensor data

http://ccsriottb3.ee.surrey.ac.uk:8080/IOTA/

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Semantics in IoT reality

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Semantics in IoT - reality

If we create an Ontology our data is interoperableReality: there are/could be a number of ontologies for a domain

Ontology mappingReference ontologies

Standardisation efforts

Semantic data will make my data machine-understandable and my system will beintelligent.Reality: it is still meta-data, machines don ‘t understand it but can interpret it. It still does needintelligent processing, reasoning mechanism to process and interpret the data.

It‘s a Hype! Ontologies and semantic data are too much overhead; we deal withtiny devices in IoT.

Reality: Ontologies are a way to share and agree on a common vocabulary and knowledge; atthe same time there are machine-interpretable and represented in interoperable and re-usableforms;

You don‘t necessarily need to add semantic metadata in the source- it could be added to thedata at a later stage (e.g. in a gateway);



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Part 3: Semantic Sensor Web

andPerception

Image source: semanticweb.com; CISCO

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What is the Sensor Web?

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What is the Sensor Web?

Sensor Web is an additional layer connecting sensor networksto the World Wide Web.

Enables an interoperable usage of sensor resources byenabling web based discovery, access, tasking, and alerting.

Enables the advancement of

cyber-physical applications throughimproved situation awareness.

Why is the Sensor Web important?



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Why is the Sensor Web important?

In generalEnable tight coupling of the cyber and physicalworld

In relation to IoTEnable shared situation awareness (or context)

between devices/things

Bridging the Cyber-Physical Divide



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Psyleron ’ s Mind-Lamp (Princeton U),connections between the mind and the

physical world.

Neuro Sky's mind-controlled headset to

play a video game.

MIT ’ s Fluid Interface Group: wearabledevice with a projector for deepinteractions with the environment




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Foursquare is an online application which

integrates a persons physical location andsocial network.Community of enthusiasts that share experiences ofself-tracking and measurement.

FitBit Community allows theautomated collection andsharing of health-related data,goals, and achievements




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Bridging the Cyber Physical Divide

Tweeting Sensorssensors are becoming social

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Role of OGC SWE



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Role of OGC SWE

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Principles of Sensor Web

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Principles of Sensor Web

Sensors will be web accessible

Sensors and sensor data will be discoverable

Sensors will be self-describing to humans and software (using astandard encoding)

Most sensor observations will be easily accessible in real time

over the web

OGC SWE Services



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OGC SWE Services

Sensor Observation Service (SOS) access sensor information (SensorML) and sensor observations (O&M

Sensor Planning Service (SPS) task sensors or sensor systems

Sensor Alert Service (SAS) asynchronous notification of sensor events (tasks, observation of

phenomena)

Sensor Registries discovery of sensors and sensor data

OGC SWE Services



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OGC SWE Services

OGC SWE Languages



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OGC SWE Languages

Sensor Model Language (SensorML)

Models and schema for describing sensor characteristics

Observation & Measurement (O&M)

Models and schema for encoding sensor observations

OCG SWE Observation



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OCG SWE Observation



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We want to set this data free

With freedom comes responsibilityDiscovery, access, and searchIntegration and interpretation

Semantic Sensor Web



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RDF OWL

OGC Sensor WebEnablement

Sensor Web + Semantic Web



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Semantic WebThe web of data where web content is processed bymachines, with human actors at the end of the chain.

The web as a huge, dynamic, evolving database offacts, rather than pages, that can be interpreted andpresented in many ways (mashups).

Fundamental importance of ontologies to describe thefact that represents the data. RDF(S) emphasiseslabelled links as the source of meaning: essentially agraph model . A label (URI) uniquely identifies aconcept.

OWL emphasises inference as the source of meaning:a label also refers to a package of logical axiomswith a proof theory.

Usually, the two notions of meaning fit.

Goal to combine information and services fortargeted purpose and new knowledge

Sensor WebThe internet of things made up of Wireless SensorNetworks, RFID, stream gauges, orbiting satellites,weather stations, GPS, traffic sensors, ocean buoys,animal and fish tags, cameras, habitat monitors,recording data from the physical world.

Today there are 4 billion mobile sensing devices pluseven more fixed sensors. The US National ResearchCouncil predicts that this may grow to trillions by 2020,and they are increasingly connected by internet andWeb protocols.

Record observations of a wide variety of modalities:

but a big part is time-series of numeric measurements.The Open Geospatial Consortium has some web-servicestandards for shared data access (Sensor WebEnablement).

Goal is to open up access to real-time and archivaldata, and to combine in applications.

So, what is a Semantic Sensor Web?



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,

Reduce the difficulty and open up sensor networks by:

Allowing high-level specification of the data collection process;Across separately deployed sensor networks;Across heterogeneous sensor types; andAcross heterogeneous sensor network platforms;Using high-level descriptions of sensor network capability; andInterfacing to data integration methods using similar query andcapability descriptions.

To create a Web of Real Time Meaning!

W3C SSN Incubator Group



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p

SSN-XG commenced: 1 March 2009

Chairs:Amit Sheth, Kno.e.sis Center, Wright State UniversityKerry Taylor, CSIROAmit Parashar Holger Neuhaus Laurent Lefort, CSIRO

Participants: 39 people from 20 organizations, including:Universities in: US, Germany, Finland, Spain, Britain, IrelandMultinationals: Boeing, EricssonSmall companies in semantics, communications, softwareResearch institutes: DERI (Ireland), Fraunhofer (Germany), ETRI (Korea),MBARI (US), SRI International (US), MITRE (US), US Defense, CTIC(Spain), CSIRO (Australia), CESI (China)

W3C SSN Incubator Group



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p

Two main objectives:

The development of an ontology for describingsensing resources and data, and

The extension of the SWE languages to supportsemantic annotations.

Sensor Standards Landscape



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p

SSN Ontology



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gy

OWL 2 DL ontology

Authored by the XGparticipants

Edited by Michael Compton

Driven by Use Cases

Terminology carefully trackedto sources through annotationproperties

MetricsClasses: 117Properties: 148DL Expressivity: SIQ(D)

SSN Ontology – http://purl.oclc.org/NET/ssnx/ssn

SSN Use Cases



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SSN Use Cases



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SSN Ontology



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Stimulus-Sensor-Observation



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The SSO Ontology Design Pattern is developed following the principle of minimalontological commitments to make it reusable for a variety of application areas.Introduces a minimal set of classes and relations centered around the notions of stimuli,sensor, and observations. Defines stimuli as the (only) link to the physical environment.Empirical science observes these stimuli using sensors to infer information aboutenvironmental properties and construct features of interest.

SSN Ontology Modules



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SSN Ontology Modules



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SSN Sensor



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A sensor can do (implements) sensing: that is, a sensor is any entity that can follow asensing method and thus observe some Property of a FeatureOfInterest.Sensors may be physical devices, computational methods, a laboratory setup with aperson following a method, or any other thing that can follow a Sensing Method to

observe a Property.

SSN Measurement Capability



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Collects together measurement properties (accuracy, range, precision, etc) and theenvironmental conditions in which those properties hold, representing a specification of asensor's capability in those conditions.

SSN Observation



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An Observation is a Situation in which a Sensing method has been used to estimate or calculate avalue of a Property.Links to Sensing and Sensor describe what made the Observation and how; links to Property andFeature detail what was sensed; the result is the output of a Sensor; other metadata gives thetime(s) and the quality.Different from OGC ‘s O&M, in which an―observation is an act or event, although it also provides

the record of the event.

Alignment with DOLCE



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What SSN does not model



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Sensor types and models

Networks: communication, topology

Representation of data and units of measurement

Location, mobility or other dynamic behaviours

Animate sensors

Control and actuation

….

Semantic Annotation of SWE



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Recommended techniquevia Xlink attributes requiresno change to SWE

xlink:href - link toontology individual

xlink:role - link toontology class

xlink:arcrole - link toontology objectproperty

How do we design the Sensor Web?



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Integration through shared semanticsOGC Sensor Web EnablementW3C SSN ontology and Semantic Annotation

Interpretation through integration of heterogeneousdata and reasoning with prior knowledge

Semantic Perception/AbstractionLinked Open Data as prior knowledge

Scale through distributed local interpretation―intelligence at the edge

Abstraction



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Abstraction provides the ability to interpret and synthesize information in a waythat affords effective understanding and communication of ideas, feelings,perceptions, etc. between machines and people.

Abstraction



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People are excellent at abstraction; ofsensing and interpreting stimuli tounderstand and interact with the world.

The process of interpreting stimuli iscalled perception ; and studying thisextraordinary human capability canlead to insights for developing effectivemachine perception.

Abstraction



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observe perceive

conceptualizationof “ real-world ”

“ real-world ”

Semantic Perception/Abstraction

http://www.google.com/imgres?imgurl=http://c2.api.ning.com/files/-gO6ebjV*05Uzl2rtNN0bbSUxR*yYyrHyjwiVdUK3q-4BgU9*cxkO-Ty8urRxFpWjE7LC5BlELmnMkHDLxuum62NpiCm2xYh/APPLE.jpg&imgrefurl=http://nerdfighters.ning.com/profile/mattdenaro&usg=__mpVu0j4ae691D_sXrZpBIDR79Z4=&h=348&w=345&sz=11&hl=en&start=3&um=1&itbs=1&tbnid=ZNd25DpnMp8byM:&tbnh=120&tbnw=119&prev=/images?q=apple&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.clker.com/cliparts/c/2/7/a/1216137653542424074narrowhouse_cartoon_eye.svg.hi.png&imgrefurl=http://www.clker.com/clipart-23244.html&usg=__LrV6uU7AYSoyh_DtdCOyuYFoXIg=&h=600&w=600&sz=94&hl=en&start=54&um=1&itbs=1&tbnid=SY6If_wIwJv8YM:&tbnh=135&tbnw=135&prev=/images?q=big+eyes+cartoon&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://1.bp.blogspot.com/_wssoejhm2w8/S79HIixFYzI/AAAAAAAAAq4/oyC906ibY0s/s320/cartoon-brain.jpg&imgrefurl=http://youthguy07.blogspot.com/2010/04/thinkingabout-thinking.html&usg=__cL5RC5dk9eN9xKpggQ3vn5y1npA=&h=231&w=241&sz=19&hl=en&start=1&um=1&itbs=1&tbnid=6j1TKxEtIhCLKM:&tbnh=105&tbnw=110&prev=/images?q=brain+cartoon&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://blogs.skokielibrary.info/radar/files/2010/04/Computer1.jpg&imgrefurl=http://blogs.skokielibrary.info/radar/&usg=__fEgZk9abvC5gx6uTJbuOrJN0_k4=&h=377&w=353&sz=20&hl=en&start=1&um=1&itbs=1&tbnid=JY0zA4972ttCLM:&tbnh=122&tbnw=114&prev=/images?q=computer&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.sjcseagles.org/clip-art-(camera1).gif&imgrefurl=http://www.sjcseagles.org/garland-home-page.htm&usg=__WGD06Pvt0knf_728KLo5u5d8xn8=&h=234&w=302&sz=7&hl=en&start=58&um=1&itbs=1&tbnid=R4CK1ZpEYZk3iM:&tbnh=90&tbnw=116&prev=/images?q=camera+clip+art&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.mysterycheckup.com/pics/magnifyingglass.gif&imgrefurl=http://mysterycheckup.com/&usg=__1N3UTwMHsK40fYw_QlWMgdegdzU=&h=600&w=600&sz=25&hl=en&start=8&um=1&itbs=1&tbnid=f0yOx__-yrfFwM:&tbnh=135&tbnw=135&prev=/images?q=magnifying+glass&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.mysterycheckup.com/pics/magnifyingglass.gif&imgrefurl=http://mysterycheckup.com/&usg=__1N3UTwMHsK40fYw_QlWMgdegdzU=&h=600&w=600&sz=25&hl=en&start=8&um=1&itbs=1&tbnid=f0yOx__-yrfFwM:&tbnh=135&tbnw=135&prev=/images?q=magnifying+glass&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://knoesis.org/http://knoesis.org/


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Fundamental Questions

What is perception, and how can wedesign machines to perceive?

What can we learn from cognitivemodels of perception?

Is the Semantic Web up to the task ofmodeling perception?

What is Perception?



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Perception is the act of

Abstracting

Explaining

Discriminating

Choosing

What can we learn from CognitiveModels of Perception?



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Models of Perception?

A-priori background knowledge is a key enablerPerception is a cyclical, active process

Ulric Neisser (1976) Richard Gregory (1997)

Is Semantic Web up to the task ofmodeling perception?



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modeling perception?

RepresentationHeterogeneous sensors, sensing, and observation recordsBackground knowledge (observable properties,objects/events, etc.)

InferenceExplain observations (hypothesis building)Focus attention by seeking additional stimuli (thatdiscriminate between explanations)

Difficult Issues to OvercomePerception is an inference to the best explanation Handle streaming dataReal-time processing (or nearly)



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Both people and machines are capable of observing qualities,such as redness.

* Formally described in a sensor/ontology (SSN ontology)

observesObserver Quality

http://www.google.com/imgres?imgurl=http://www.clker.com/cliparts/c/2/7/a/1216137653542424074narrowhouse_cartoon_eye.svg.hi.png&imgrefurl=http://www.clker.com/clipart-23244.html&usg=__LrV6uU7AYSoyh_DtdCOyuYFoXIg=&h=600&w=600&sz=94&hl=en&start=54&um=1&itbs=1&tbnid=SY6If_wIwJv8YM:&tbnh=135&tbnw=135&prev=/images?q=big+eyes+cartoon&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.sjcseagles.org/clip-art-(camera1).gif&imgrefurl=http://www.sjcseagles.org/garland-home-page.htm&usg=__WGD06Pvt0knf_728KLo5u5d8xn8=&h=234&w=302&sz=7&hl=en&start=58&um=1&itbs=1&tbnid=R4CK1ZpEYZk3iM:&tbnh=90&tbnw=116&prev=/images?q=camera+clip+art&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://knoesis.org/http://knoesis.org/


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The ability to perceive is afforded through the use ofbackground knowledge , relating observable qualities to entitiesin the world.

* Formally described indomain ontologies

(and knowledge bases)inheres in

Quality

Entity

http://www.google.com/imgres?imgurl=http://c2.api.ning.com/files/-gO6ebjV*05Uzl2rtNN0bbSUxR*yYyrHyjwiVdUK3q-4BgU9*cxkO-Ty8urRxFpWjE7LC5BlELmnMkHDLxuum62NpiCm2xYh/APPLE.jpg&imgrefurl=http://nerdfighters.ning.com/profile/mattdenaro&usg=__mpVu0j4ae691D_sXrZpBIDR79Z4=&h=348&w=345&sz=11&hl=en&start=3&um=1&itbs=1&tbnid=ZNd25DpnMp8byM:&tbnh=120&tbnw=119&prev=/images?q=apple&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://knoesis.org/http://knoesis.org/


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With the help of sophisticated inference, both people andmachines are also capable of perceiving entities, such as apples.

the ability to degrade gracefully with incomplete information

the ability to minimize explanations based on new information

the ability to reason over data on the Web

fast (tractable)

perceivesEntityPerceiver

Perceptual Inference

http://www.google.com/imgres?imgurl=http://1.bp.blogspot.com/_wssoejhm2w8/S79HIixFYzI/AAAAAAAAAq4/oyC906ibY0s/s320/cartoon-brain.jpg&imgrefurl=http://youthguy07.blogspot.com/2010/04/thinkingabout-thinking.html&usg=__cL5RC5dk9eN9xKpggQ3vn5y1npA=&h=231&w=241&sz=19&hl=en&start=1&um=1&itbs=1&tbnid=6j1TKxEtIhCLKM:&tbnh=105&tbnw=110&prev=/images?q=brain+cartoon&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://c2.api.ning.com/files/-gO6ebjV*05Uzl2rtNN0bbSUxR*yYyrHyjwiVdUK3q-4BgU9*cxkO-Ty8urRxFpWjE7LC5BlELmnMkHDLxuum62NpiCm2xYh/APPLE.jpg&imgrefurl=http://nerdfighters.ning.com/profile/mattdenaro&usg=__mpVu0j4ae691D_sXrZpBIDR79Z4=&h=348&w=345&sz=11&hl=en&start=3&um=1&itbs=1&tbnid=ZNd25DpnMp8byM:&tbnh=120&tbnw=119&prev=/images?q=apple&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://blogs.skokielibrary.info/radar/files/2010/04/Computer1.jpg&imgrefurl=http://blogs.skokielibrary.info/radar/&usg=__fEgZk9abvC5gx6uTJbuOrJN0_k4=&h=377&w=353&sz=20&hl=en&start=1&um=1&itbs=1&tbnid=JY0zA4972ttCLM:&tbnh=122&tbnw=114&prev=/images?q=computer&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://knoesis.org/http://knoesis.org/


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minimize

explanations

degrade gracefully

tractable

Abductive Logic (e.g., PCT)high complexity

Deductive Logic (e.g., OWL)(relatively) low complexity

Web reasoning

Perceptual Inference(i.e., abstraction)



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The ability to perceive efficiently is afforded through the cyclicalexchange of information between observers and perceivers.

Traditionally called thePerceptual Cycle

(or Active Perception)

sendsfocus

sendsobservation

Observer

Perceiver

Neisser‘s Perceptual Cycle

http://www.google.com/imgres?imgurl=http://www.clker.com/cliparts/c/2/7/a/1216137653542424074narrowhouse_cartoon_eye.svg.hi.png&imgrefurl=http://www.clker.com/clipart-23244.html&usg=__LrV6uU7AYSoyh_DtdCOyuYFoXIg=&h=600&w=600&sz=94&hl=en&start=54&um=1&itbs=1&tbnid=SY6If_wIwJv8YM:&tbnh=135&tbnw=135&prev=/images?q=big+eyes+cartoon&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://1.bp.blogspot.com/_wssoejhm2w8/S79HIixFYzI/AAAAAAAAAq4/oyC906ibY0s/s320/cartoon-brain.jpg&imgrefurl=http://youthguy07.blogspot.com/2010/04/thinkingabout-thinking.html&usg=__cL5RC5dk9eN9xKpggQ3vn5y1npA=&h=231&w=241&sz=19&hl=en&start=1&um=1&itbs=1&tbnid=6j1TKxEtIhCLKM:&tbnh=105&tbnw=110&prev=/images?q=brain+cartoon&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://blogs.skokielibrary.info/radar/files/2010/04/Computer1.jpg&imgrefurl=http://blogs.skokielibrary.info/radar/&usg=__fEgZk9abvC5gx6uTJbuOrJN0_k4=&h=377&w=353&sz=20&hl=en&start=1&um=1&itbs=1&tbnid=JY0zA4972ttCLM:&tbnh=122&tbnw=114&prev=/images?q=computer&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.sjcseagles.org/clip-art-(camera1).gif&imgrefurl=http://www.sjcseagles.org/garland-home-page.htm&usg=__WGD06Pvt0knf_728KLo5u5d8xn8=&h=234&w=302&sz=7&hl=en&start=58&um=1&itbs=1&tbnid=R4CK1ZpEYZk3iM:&tbnh=90&tbnw=116&prev=/images?q=camera+clip+art&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://knoesis.org/http://knoesis.org/


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Cognitive Theories of Perception



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1970 ’s – Perception is an active, cyclical process ofexploration and interpretation.

- Nessier ’s Perception Cycle

1980 ’s – The perception cycle is driven by backgroundknowledge in order to generate and test hypotheses.

- Richard Gregory (optical illusions )

1990 ’s – In order to effectively test hypotheses, someobservations are more informative than others.

- Norwich ’s Entropy Theory of Perception



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Key InsightsBackground knowledge plays a crucial role in perception; what we know(or think we know/believe) influences our perception of the world.Semantics will allow us to realize computational models of perception

based on background knowledge.

Contemporary IssuesInternet/Web expands our background knowledge to a global scope;thus our perception is global in scope

Social networks influence our knowledge and beliefs, thus influencing ourperception



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observes

inheres in

Integrated together, we have an general model – capable ofabstraction – relating observers, perceivers, and backgroundknowledge.

perceives

sendsfocus

sendsobservation

Observer Quality

EntityPerceiver

http://www.google.com/imgres?imgurl=http://www.clker.com/cliparts/c/2/7/a/1216137653542424074narrowhouse_cartoon_eye.svg.hi.png&imgrefurl=http://www.clker.com/clipart-23244.html&usg=__LrV6uU7AYSoyh_DtdCOyuYFoXIg=&h=600&w=600&sz=94&hl=en&start=54&um=1&itbs=1&tbnid=SY6If_wIwJv8YM:&tbnh=135&tbnw=135&prev=/images?q=big+eyes+cartoon&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://www.google.com/imgres?imgurl=http://1.bp.blogspot.com/_wssoejhm2w8/S79HIixFYzI/AAAAAAAAAq4/oyC906ibY0s/s320/cartoon-brain.jpg&imgrefurl=http://youthguy07.blogspot.com/2010/04/thinkingabout-thinking.html&usg=__cL5RC5dk9eN9xKpggQ3vn5y1npA=&h=231&w=241&sz=19&hl=en&start=1&um=1&itbs=1&tbnid=6j1TKxEtIhCLKM:&tbnh=105&tbnw=110&prev=/images?q=brain+cartoon&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://c2.api.ning.com/files/-gO6ebjV*05Uzl2rtNN0bbSUxR*yYyrHyjwiVdUK3q-4BgU9*cxkO-Ty8urRxFpWjE7LC5BlELmnMkHDLxuum62NpiCm2xYh/APPLE.jpg&imgrefurl=http://nerdfighters.ning.com/profile/mattdenaro&usg=__mpVu0j4ae691D_sXrZpBIDR79Z4=&h=348&w=345&sz=11&hl=en&start=3&um=1&itbs=1&tbnid=ZNd25DpnMp8byM:&tbnh=120&tbnw=119&prev=/images?q=apple&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://blogs.skokielibrary.info/radar/files/2010/04/Computer1.jpg&imgrefurl=http://blogs.skokielibrary.info/radar/&usg=__fEgZk9abvC5gx6uTJbuOrJN0_k4=&h=377&w=353&sz=20&hl=en&start=1&um=1&itbs=1&tbnid=JY0zA4972ttCLM:&tbnh=122&tbnw=114&prev=/images?q=computer&um=1&hl=en&rls=com.microsoft:*&tbs=isch:1http://www.google.com/imgres?imgurl=http://www.sjcseagles.org/clip-art-(camera1).gif&imgrefurl=http://www.sjcseagles.org/garland-home-page.htm&usg=__WGD06Pvt0knf_728KLo5u5d8xn8=&h=234&w=302&sz=7&hl=en&start=58&um=1&itbs=1&tbnid=R4CK1ZpEYZk3iM:&tbnh=90&tbnw=116&prev=/images?q=camera+clip+art&start=40&um=1&hl=en&sa=N&rls=com.microsoft:*&ndsp=20&tbs=isch:1http://knoesis.org/http://knoesis.org/


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Ontology of Perception – as an extension of SSN

Provides abstraction of sensor data through perceptualinference of semantically annotated data

Prior Knowledge



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W3C SSN Ontology Bi-partite Graph

Prior knowledge conformant to SSN ontology (left),structured as a bipartite graph (right)

Semantics of Explanation



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Explanation is the act of accounting for sensory observations (i.e.,abstraction); often referred to as hypothesis building.

Observed Property : A property that has been observed.

ObservedProperty ≡ ssn:observedProperty — .{o1} … ssn:observedProperty — .{on}

Explanatory Feature : A feature that explains the set of observedproperties.

ExplanatoryFeature ≡ ssn:isPropertyOf — .{p1} … ssn:isPropertyOf — .{pn}

Semantics of Explanation



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ExampleAssume the properties elevated blood pressure and

palpitations have been observed, and encoded in RDF(conformant with SSN):

ssn:Observation(o1), ssn:observedProperty(o1, elevated blood pressure)ssn:Observation(o2), ssn:observedProperty(o2, palpitations)

Given these observations, the following ExplanatoryFeatureclass is constructed:

ExplanatoryFeature ≡ ssn:isPropertyOf — .{elevated blood pressure} ssn:isPropertyOf — .{palpitations}

Given the KB, executing the query ExplanatoryFeature(?y) caninfer the features, Hypertension and Hyperthyroidism, asexplanations:

ExplanatoryFeature(Hypertension)ExplanatoryFeature(Hyperthyroidism)

Semantics of Discrimination



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Discrimination is the act of deciding how to narrow down the multitude ofexplanatory features through further observation.

Expected Property : A property is expected with respect to (w.r.t.) a set offeatures if it is a property of every feature in the set.

ExpectedProperty≡

ssn:isPropertyOf.{f1} … ssn:isPropertyOf.{fn}

NotApplicable Property : A property is not-applicable w.r.t. a set of features if itis not a property of any feature in the set.

NotApplicableProperty ≡ ¬ ssn:isPropertyOf.{f1} …

¬ ssn:isPropertyOf.{fn}

Discriminating Property : A property is discriminating w.r.t. a set of features if itis neither expected nor not-applicable.

DiscriminatingProperty ≡ ¬ExpectedProperty ¬NotApplicableProperty

Semantics of Discrimination



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ExampleGiven the explanatory features from the previous example,

Hypertension and Hyperthyroidism, the following classes areconstructed:

ExpectedProperty ≡ ssn:isPropertyOf.{Hypertension} ssn:isPropertyOf.{Hyperthyroidism}

NotApplicableProperty ≡ ¬ ssn:isPropertyOf.{Hypertension} ¬ ssn:isPropertyOf.{Hyperthyroidism}

Given the KB, executing the query DiscriminatingProperty(?x)can infer the property clammy skin as discriminating:

DiscriminatingProperty(clammy skin)

How do we design the Sensor Web?



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Integration through shared semanticsOGC Sensor Web EnablementW3C SSN ontology and Semantic Annotation

Interpretation through integration of heterogeneousdata and reasoning with prior knowledge

Semantic Perception/AbstractionLinked Open Data as prior knowledge

Scale through distributed local interpretation―intelligence at the edge

Efficient Algorithms for IntellegO



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Use of OWL-DL reasoner too resource-intensive for use in resourceconstrained devices (such as sensor nodes, mobile phones, IoT devices)

Runs out of resources for problem size (prior knowledge) > 20 conceptsAsymptotic complexity: O(n3) [Experimentally determined]

To enable their use on resource-constrained devices, we now describealgorithms for efficient inference of explanation and discrimination.

These algorithms use bit vector encodings and operations, leveraging a-priori knowledge of the environment.


http://knoesis.org/


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Semantic (RDF) Encoding Bit Vector Encoding

Lower

Lift

First, developed lifting and loweringalgorithms to translate between RDFand bit vector encodings ofobservations.


http://knoesis.org/


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Explanation Algorithm

Discrimination Algorithm

Utilize bit vector operators to efficientlycompute explanation and discrimination

Explanation: Use of the bit vector ANDoperation to discover and dismiss those features

that cannot explain the set of observedproperties

Discrimination: Use of the bit vector ANDoperation to discover and indirectly assemble

those properties that discriminate between a setof explanatory features. The discriminatingproperties are those that are determined to beneither expected nor not-applicable


http://knoesis.org/


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Evaluation : The bit vector encodings and algorithms yield significant and necessarycomputational enhancements – including asymptotic order of magnitude improvement , withrunning times reduced from minutes to milliseconds, and problem size increased from 10 ‘sto 1000 ‘s.

Adoption of SSN

http://knoesis.org/


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SSN Applications

http://knoesis.org/


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Linked Sensor Data

http://knoesis.org/


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Linked Sensor Data(~2 Billion Statements)

Sensor Discovery Application

http://knoesis.org/


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Query w/ location name to find nearby sensors

SSN Applications

http://knoesis.org/


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Applications of SSN

HealthcareWeather Rescue

SSN Application: Weather

http://knoesis.org/


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50% savings in sensing resource

requirements during the detection of ablizzard

Order of magnitude resourcesavings between storing observations vs.relevant abstractions

SSN Application: Fire Detection

http://knoesis.org/


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Weather ApplicationSECURE: Semantics-empowered Rescue Environment(detect different types of fires)

DEMO: http://www.youtube.com/watch?v=in2KMkD_uqg

SSN Application: Health Care

http://www.youtube.com/watch?v=in2KMkD_uqghttp://www.youtube.com/watch?v=in2KMkD_uqghttp://knoesis.org/


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MOBILEMD: Mobile app to help reduce re-admissionof patients with Chronic Heart Failure


http://knoesis.org/


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Passive Monitoring Phase

• Abnormal heart rate• Clammy skin

• Panic Disorder• Hypoglycemia• Hyperthyroidism• Heart Attack

• Septic Shock

Observed Symptoms Possible Explanations

Passive Sensors – heart rate, galvanic skin response


http://knoesis.org/


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Active Monitoring Phase

Are you feeling lightheaded?

Are you have trouble taking deep breaths?

yes

yes

Have you taken your Methimazolemedication?

Do you have low blood pressure?

yes

• Abnormal heart rate

• Clammy skin• Lightheaded• Trouble breathing• Low blood pressure

• Panic Disorder•

Hypoglycemia• Hyperthyroidism• Heart Attack• Septic Shock

Observed Symptoms Possible Explanations

no

Active Sensors – blood pressure, weight scale, pulse oxymeter

Future work

http://knoesis.org/


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Creating ontologies and defining data models are not enoughtools to create and annotate dataTools for publishing linked IoT data

Designing lightweight versions for constrained environmentsthink of practical issuesmake it as much as possible compatible and/or link it to the otherexisting ontologies

Linking to domain knowledge and other resourcesLocation, unit of measurement, type, theme, …

Linked-dataURIs and naming

Some of the open issues

http://knoesis.org/


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Efficient real-time IoT resource/servicequery/discoveryDirectoryIndexing

Abstraction of IoT dataPattern extractionPerception creation

IoT service composition and compensationIntegration with existing Web servicesService adaptation


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Some useful links related to IoT

http://knoesis.org/


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Internet of Things, ITU

http://www.itu.int/osg/spu/publications/internetofthings/InternetofThings_summary.pdf IoT Comic Book

http://www.theinternetofthings.eu/content/mirko-presser-iot-comic-book

Internet of Things Europe,http://www.internet-of-things.eu/

Internet of Things Architecture (IOT-A)

http://www.iot-a.eu/public/public-documents

W3C Semantic Sensor Networks

http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/

Kno.e.sis Semantic Sensor Web Group

http://knoesis.org/projects/ssw
http://www.itu.int/osg/spu/publications/internetofthings/InternetofThings_summary.pdfhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.internet-of-things.eu/http://www.iot-a.eu/public/public-documentshttp://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://knoesis.org/projects/sswhttp://knoesis.org/projects/sswhttp://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/http://www.iot-a.eu/public/public-documentshttp://www.iot-a.eu/public/public-documentshttp://www.iot-a.eu/public/public-documentshttp://www.iot-a.eu/public/public-documentshttp://www.iot-a.eu/public/public-documentshttp://www.internet-of-things.eu/http://www.internet-of-things.eu/http://www.internet-of-things.eu/http://www.internet-of-things.eu/http://www.internet-of-things.eu/http://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.theinternetofthings.eu/content/mirko-presser-iot-comic-bookhttp://www.itu.int/osg/spu/publications/internetofthings/InternetofThings_summary.pdf

IOT Tutorial

Documents

Transcript of IOT Tutorial