Data Modeling and Knowledge Engineering for the Internet of Things

Data Modelling and Knowledge
Engineering for the Internet of Things

Wei Wang1, Cory Henson2, Payam Barnaghi1
Centre for Communication Systems Research, University of Surrey
Kno.e.sis Center, Wright State University

Part 1: Introduction
to Internet of “Things”

Image source: CISCO

2

Internet of Things

 “sensors and actuators embedded in physical
objects — from containers to pacemakers —
are linked 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 understanding complexity and for
responding to events and irregularities swiftly”
source: http://www.iot2012.org/

“Thing” connected to the internet

Source: CISCO 4

Future Internet - A new dimension

55

Internet of Things - definition

 “A world where physical objects are
seamlessly integrated into the information
network, and where the physical objects can
become active participants in business
processes.”
 “Services are available to interact with these
“smart objects” over the Internet, query and
change their state 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.
6

What “Things” can be connected?

Home/daily-life devices
Business and
Public infrastructure
Health-care
…

Sensor devices are becoming widely
available
- Programmable devices
- Off-the-shelf gadgets/tools

Observation and measurement data

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

Data is important and IoT will produce
lots of it!
 Sensors and devices provide data about the physical world
objects.
 The observation and measurement data related to an “object”
can be related 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 important phase.
 Huge amount of data related to our physical world that need to
be
 Published
 Stored (temporary or for longer term)
 Discovered
 Accessed
 Proceeded
 Utilised in different applications

The “Things”

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

Components related to “Things”

 Physical world objects
 e.g. A room, a car, A person;
 Feature of Interest
 e.g. Temperature of the room, Location of the car,
heart-rate of the person;
 Sensors
 e.g. Temperature sensor, GPS, pulse sensor
 Embedded device
 e.g. WASPmote, SunSPOT, …

Sensors

 Active & Passive Sensors
 Energy Efficiency
 Processing capabilities
 Network communications
 hardware platforms
 software platforms

RFID

 Active Tags and Passive Tags
 Applications: supply chain, inventory tracking, tools
collection, etc.
 Limitations:
 Technology
 Reading range
 Physical limitations
 Interference
 Security and Privacy

Hardware components of sensor
nodes
 Controller
 Memory
 Communication device
 Sensors (or actuators)
 Power supply

Example: Radiation Sensor Board
(Libelium)

Waspmote

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

Energy consumption of the nodes

 Batteries have small capacity and recharging
could be complex (if not impossible) in some
cases.
 The main consumers of the energy are: the
controller, radio, to some extent memory and
depending on the type, the sensor(s).
 A controller can go to:
 “active”, “idle” and “sleep”
 A radio modem could turn transmitter, receiver,
or both on or off,
 sensors and memory can be also turned on
and off.

Beyond common sensors

 Human as a sensor
 e.g. tweeting real world data and/or events
 Virtual sensors
 e.g. Software agents generating data

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

Actuators

[2]

Stepper Motor [1]

[4] [3]

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/

Wireless Sensor Networks (WSN)

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

Wireless Sensor Networks (WSN)-
gateway connection

What are the main issues?

 Heterogeneity
 Interoperability
 Mobility
 Energy efficiency
 Scalability
 Security

What is important?

 Robustness
 Quality of Service
 Scalability
 Seamless integration
 Security, privacy, Trust

In-network processing

 Mobile Ad-hoc Networks are supposed to deliver bits
from one end to the other
 WSNs, on the other end, are expected to provide
information, not necessarily original bits
 Gives addition options
 E.g., manipulate or process the data in the network
 Main example: aggregation
 Applying aggregation functions to a obtain an average
value of measurement data
 Typical functions: minimum, maximum, average, sum, …
 Not amenable functions: median
source: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor Networks

In-network processing- example

Applying Symbolic Aggregate Approximation (SAX)

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

Data-centric networking

 In typical networks (including ad hoc networks),
network transactions are addressed to the identities of
specific nodes
 A “node-centric” or “address-centric” networking paradigm
 In a redundantly deployed sensor networks, specific
source of an event, alarm, etc. might not be important
 Redundancy: e.g., several nodes can observe the same
area
 Thus: focus networking transactions on the data
directly instead of their senders and transmitters !
d a ta -c e ntric ne two rking
 Principal design change
source: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor Networks

Implementation options for
data-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
efficient implementation
 Some disparities remain
 Static key in DHT, dynamic changes in WSN
 DHTs typically ignore issues like hop count or distance between nodes when
performing a lookup operation
 Publish/subscribe
 Different interaction paradigm
 Nodes can publish data, can subscribe to any particular kind of data
 Once 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
agnostic of each other (decoupled in identity);
 There is concepts of Semantic Sensor Networks- to annotate sensor resources
and observation and measurement data!
Adapted from: Protocols and Architectures for Wireless Sensor Networks, Protocols and Architectures for Wireless Sensor Networks

IoT and Semantic technologies
 The sensors (and in general “Things”) are increasingly
being integrated into the Internet/Web.
 This can be supported by embedded devices that
directly support IP and web-based connection (e.g.
6LowPAN and CoAp) or devices that are connected
via gateway components.
 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 and academia.
 While such frameworks provide some interoperability,
semantic technologies are increasingly seen as key
enabler for integration of IoT data and broader Web
information systems.

Semantics and IoT resources and
data
 Semantics are machine-interpretable metadata (for mark-up),
logical inference mechanisms, query mechanism, linked data
solutions
 For 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. those available on linked open data)
 Semantic annotation should also supports data represented
using existing forms
 Reasoning /processing to infer relationships and hierarchies
between different resources, data
 Semantics (/ontologies) as meta-data (to describe the IoT
resources/data) / knowledge bases (domain knowledge).

A Few Words
on
Semantic Web

34

Semantic Web
SSW Introduction (a c c o rd ing to Fa rs id e )

Concrete Facts
Concrete Facts
Re ssoourc ee De ssccrip tio nn Fra m eewo rk
Re urc De rip tio Fra m wo rk
lives in
General Knowledge
General Knowledge
We bb O nto lo ggyy La ng ua ggee
We O nto lo La ng ua

has pet
Person
Person Animal
Animal
has pet is a

is a

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

Linked Open Data

~ 50 Billion Statements
~ 50 Billion Statements

SW is moving from academia
to industry

In the last few years, we have
seen many successes …

Apple
Siri

Watson

Knowledge Graph

Sensors and the Web

42

Sensors are small and inexpensive

Digitization of the physical world

Leading to …

 Improved situational
awareness
 Advanced cyber-physical
systems / applications
 Enabling the Internet of
Things

Enabling the Internet of Things

Situational awareness
enables:
 Devices/things to function
and adapt within their
environment
 Devices/things to work
together

Sensor systems are
too often s to ve p ip e d .

Closed centralized
management of sensing
resources

Closed inaccessible
data and sensors

We want to set this data free

With freedom comes
responsibility
Discovery, access, and search
Integration and interpretation

Scalability

Drowning in Data

A cross-country flight from New York to Los Angeles on a
Boeing 737 plane generates a massive 240 terabytes of
data
- G ig a O m ni M d ia
e

Drowning in Data

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

Challenges

To fulfill this vision, there are difficult challenges to overcome such
as the discovery, access, search, integration, and interpretation of
sensors and sensor 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
and
applications
Scalability dealing with data overload and computational complexity
of interpreting the data

Solution

Semantic Sensor Web
Internet Computing, July/Aug.
2008
Uses the Web as platform for
managing sensor resources and
data
 Uses semantic technologies for
representing data and knowledge,
integration, and interpretation

Solution

Discovery, access, and search
 Using standard Web services
 OGC Sensor Web Enablement

Solution

Integration
 Using shared domain models / data
representation
 W3C Semantic Sensor Networks

Solution

Interpretation
 Abstraction – converting low-level data to high-level knowledge
 Machine Perception – w/ prior knowledge and abductive
reasoning
 IntellegO – Ontology of Perception

Solution

Scalability
 Data overload – sensors produce too much data
 Computational complexity of semantic interpretation
 “Intelligence at the edge” – local and distributed integration and
interpretation of sensor data

Part 2: Semantic Modelling
for the Internet of “Things”

Image source: semanticweb.com; CISCO

60

Recall of the Internet of Things

 A primary goal of interconnecting devices and
collecting/processing data from them is to
create situation awareness and enable
applications, machines, and human users to
better understand their surrounding
environments.
 The understanding of a situation, or context,
potentially enables services and applications
to make intelligent decisions and to respond to
the dynamics of their environments.
Barnaghi et al 2012, “Semantics for the Internet of Things: early progress and back to the future”

IoT challenges
 Numbers of devices and different users and interactions required.
 Challenge: Scalability
 Heterogeneity of enabling devices and platforms
 Challenge: Interoperability
 Low power sensors, wireless transceivers, communication, and networking
for M2M
 Challenge: Efficiency in communications
 Huge volumes of data emerging from the physical world, M2M and new
communications
 Challenge: Processing and mining the data, Providing secure access and
preserving and controlling privacy.
 Timeliness of data
 Challenge: Freshness of the data and supporting temporal requirements in
accessing the data
 Ubiquity
 Challenge: addressing mobility, ad-hoc access and service continuity
 Global access and discovery
 Challenge: Naming, Resolution and discovery

IoT: one paradigm, many visions

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

Semantic oriented vision

 “The object unique addressing and the representation
and storing of the exchanged information become the
most challenging issues, bringing directly to a ‘‘Semantic
oriented”, perspective of IoT”, [Atzori et al., 2010]
 Data collected by different sensors and devices is
usually multi-modal (temperature, light, sound, video,
etc.) and diverse in nature (quality of data can vary with
different devices through time and it is mostly location
and time dependent [Barnaghi et al, 2012]
 some of challenging issues: representation, storage, and
search/discovery/query/addressing, and processing IoT
resources and data.

What is expected?

 Unified access to data: unified descriptions
 Deriving additional knowledge (data mining)
 Reasoning support and association to other entities and
resources
 Self-descriptive data an re-usable knowledge
 In general: Large-scale platforms to support discovery
and access to the resources, to enable autonomous
interactions with the resources, to provide self-
descriptive data and association mechanisms to reason
the emerging data and to integrate it into the existing
applications and services.

Semantic technologies and IoT

 There are already Sensor Web Enablement
(SWE) standards developed by the Open
Geospatial Consortium that are widely adopted.
 While such frameworks provide certain levels of
interoperability, semantic technologies are seen
as key enabler for integration of IoT data and
and existing business information systems.
 Semantic technologies provide potential support
for:
 Interoperability and machine automation
 IoT resource and data annotation, logical inference, query and
discovery, linked IoT data

Identify IoT domain concepts

 Users
 Physical entities
 Virtual entities
 Devices
 Resource
 Services
 …

Diagram adapted from IoT-A project D2.1

IoT domain concepts - Entity

 P hysical entities (or entity of interests): objects
in the physical world, features of interest that
are of interests to users (human users or any
digital artifacts).
 Virtual entities: virtual representation of the
physical entities.
 Entities are the main focus of interactions
between humans and/or software agents.
 This interaction is made possible by a hardware
component called Device.
Definition adapted from De et al, 2012, “Service modeling for the Internet of Things”

IoT domain concepts –
Device, Resource and Service
 A Device mediates the interactions between users
and entities.
 The software component that provides information
on the entity or enables controlling of the device, is
called a R esource.
 AS ervice provides well-defined and standardised
interfaces, 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”

Other concepts need to considered

 Gateways
 Directories
 Platforms
 Systems
 Subsystems
 …
 Relationships among them
 A links to e x is ting kno wle d g e ba s e a nd
nd
linke d d a ta

Don’t forget the IoT data

 Sensors and devices provide observation and
measurement data about the physical world objects
which also need to be semantically described and can
be related to an event, situation in the physical world.
 The processing of data into knowledge/ perception and
using it for decision making, automated control, etc.
 Huge amount of data from our physical world that need
to be
 Annotated
 Published
 Stored (temporary or for longer term)
 Discovered
 Accessed
 Proceeded
 Utilised in different applications

Semantics for IoT resources and data

 Semantics are machine-interpretable metadata, logical inference
mechanisms, query and search mechanism, linked data…
 For IoT this means:
 ontologies for: resource (e.g. sensors), observation and
measurement data (e.g. sensor readings), services (e.g. IoT
services), domain concepts (e.g. unit of measurement, location)
and other data sources (e.g. those available on linked open data)
 Semantic annotation should also supports data represented using
existing forms
 Reasoning/processing to infer relationships between different
resources and services, detecting patterns from IoT data

Characteristics of IoT resources

 Extraordinarily large number
 Limited computing capabilities
 Limited memory
 Resource constrained environments (e.g.,
battery life, signal coverage)
 Location is important
 Dynamism in the physical environments
 Unexpected disruption of services
 …

Characteristics of IoT data

 Stream data (depends on time)
 Transient nature
 Almost always related to a phenomenon or
quality in our physical environments
 Large amount
 Quality in many situations cannot be assured
(e.g., accuracy and precision)
 Abstraction levels (e.g., raw, inferred or
derived)
 …

Utilise semantics

 Find all available resources (which can provide
data) and data related to “Ro o m A (which is an
”
object in the linked data)?
 What is “Room A”? What is its location? returns “location” data
 What type of data is available for “Room A” or that “location”?
(s e ns o r c a te g o ry ty p e s )
 Predefined Rules can be applied based on
available data
 (TempRoom_A > 80°C) AND (SmokeDetectedRoom_A position==TRUE) 
FireEventRoom_A
 Learning these rules needs data mining or pattern recognition
techniques

Semantic modelling

 Lightweight: experiences show that a lightweight
ontology model that well balances expressiveness and
inference complexity is more likely to be widely adopted
and reused; also large number of IoT resources and
huge amount of data need efficient processing
 Compatibility: an ontology needs to be consistent with
those well designed, existing ontologies to ensure
compatibility wherever possible.
 Modularity: modular approach to facilitate ontology
evolution, extension and integration with external
ontologies.

Existing models for resources and data

 W3C Semantic Sensor Network Incubator
Group’s S N ontology (mainly for sensors and
S
sensor networks, observation and
measurement, and platforms and systems)
 Quantity Kinds and Units
 Used together with the SSN ontology
 based on QUDV model OMG SysML(TM)
 Working group of the SysML 1.2 Revision Task
Force (RTF) and W3C Semantic Sensor Network
Incubator Group

Existing models for services

 OWL-S and WSMO are heavy weight models: practical
use?
 Minimal service model
 Deprecated
 Procedure-Oriented Service Model (POSM) and Resource-
Oriented Service Model (ROSM): two different models for
different service technologies
 Defines Operations and Messages
 No profile, no grounding
 SAWSDL: mixture of XML, XML schema, RDF and OWL
 hRESTS and SA-REST: mixture of HTML and reference
to a semantic model; sensor services are not anticipated
to have HTML

W3C’S SSN ontology

Diagram adapted from SSN report

Some existing IoT models and
ontologies
 FP7 IoT-A project’s Entity-Resource-Service
ontology
 A set of ontologies for entities, resources, devices
and services
 Based on the SSN and OWL-S ontology
 FP7 IoT.est project’s service description
framework
 A modular approach for designing a description
framework
 A set of ontologies for IoT services, testing and
QoS/QoI

IoT-A resource model


IoT-A resource description


IoT-A service model


IoT-A service description


Service modelling in IoT.est

Diagrams adapted from Iot.est D3.1

IoT.est service profile highlight

 ServiceType class represents the service technologies:
RESTful and SOAP/WSDL services.
 serviceQos and serviceQoI are defined as subproperty of
serviceParameter; they link to concepts in the QoS/QoI
ontology.
 serviceArea: the area where the service is provided;
different from the sensor observation area
 Links to the IoT resources through “exposedB property
y”
 Future extension:
 serviceNetwork, servicePlatform and serviceDeployment
 Service lifecycle, SLA…

Linked data principles

 using URI’s as names for things: Everything is
addressed using unique URI’s.
 using HTTP URI’s to enable people to look up
those names: All the URI’s are accessible via
HTTP interfaces.
 provide useful RDF information related to
URI’s that are looked up by machine or
people;
 including RDF statements that link to other
URI’s to enable discovery of other related
concepts of the Web of Data: The URI’s are
linked to other URI’s.

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 by machine 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 of other 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…

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

Sensor discovery using linked sensor
data

Semantics in IoT - reality

 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
 Semantic data will make my data machine-understandable and my system
will be intelligent.
 Reality: it is still meta-data, machines don’t understand it but can interpret it. It still
does need intelligent processing, reasoning mechanism to process and interpret the
data.
 It’s a Hype! Ontologies and semantic data are too much overhead; we deal
with tiny devices in IoT.
 Reality: Ontologies are a way to share and agree on a common vocabulary and
knowledge; at the same time there are machine-interpretable and represented in
interoperable and re-usable forms;
 You don’t necessarily need to add semantic metadata in the source- it could be added
to the data at a later stage (e.g. in a gateway);

Part 3: Semantic Sensor Web
and
Perception

Image source: semanticweb.com; CISCO

93

What is the Sensor Web?

 Sensor Web is an additional layer connecting sensor
networks to the World Wide Web.

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

 Enables the advancement of
cyber-physical applications through
improved situation awareness.

Why is the Sensor Web important?

 In general
 Enable tight coupling of the cyber and
physical world

 In relation to IoT
 Enable shared situation awareness (or
context) between devices/things

Bridging the Cyber-Physical Divide

Psyleron’s Mind-Lamp (Princeton U),
connections between the mind and the
physical world.
MIT’s Fluid Interface Group: wearable
device with a projector for deep
interactions with the environment

Neuro Sky's mind-controlled headset to
play a video game.


FitBit Community allows the
automated collection and
sharing of health-related data,
goals, and achievements

Foursquare is an online application which
integrates a persons physical location and
social network.
Community of enthusiasts that share experiences of
self-tracking and measurement.


Tweeting Sensors
sensors are becoming social

How do we design the Sensor Web?

 Integration through shared semantics
 W3C SSN ontology and Semantic Annotation

 Interpretation through integration of
heterogeneous data and reasoning with prior
knowledge
 Semantic Perception/Abstraction
 Linked Open Data as prior knowledge

 Scale through distributed local interpretation
 “intelligence at the edge”

Vision of Sensor Web

 Quickly discover sensors (secure or public) that can meet
my needs – location, observables, quality, ability to task
 Obtain sensor information in a standard encoding that is
understandable by me and my software
 Readily access sensor observations in a common manner,
and in a form specific to my needs
 Task sensors, when possible, to meet my specific needs
 Subscribe to and receive alerts when a sensor measures a
particular phenomenon

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 a standard encoding)
 Most sensor observations will be easily accessible in real
time over the web

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 Languages

 Sensor Model Language (SensorML)
 Models and schema for describing sensor
characteristics

 Observation & Measurement (O&M)
 Models and schema for encoding sensor
observations

Semantic Sensor Web

OGC Sensor Web
Enablement

RDF OWL

Sensor Web + Semantic Web

Semantic Web Sensor Web

The web of data where web content is processed The internet of things made up of Wireless Sensor
by machines, with human actors at the end of the Networks, RFID, stream gauges, orbiting satellites,
chain. weather stations, GPS, traffic sensors, ocean buoys,
animal and fish tags, cameras, habitat monitors,
The web as a huge, dynamic, evolving database recording data from the physical world.
of facts, rather than pages, that can be interpreted
and presented in many ways (mashups). Today there are 4 billion mobile sensing devices
plus even more fixed sensors. The US National
Fundamental importance of ontologies to describe Research Council predicts that this may grow to
the fact that represents the data. RDF(S) trillions by 2020, and they are increasingly connected
emphasises labelled links as the source of meaning: by internet and Web protocols.
essentially a graph model . A label (URI) uniquely
identifies a concept. Record observations of a wide variety of modalities:
but a big part is time-series‟ of numeric
OWL emphasises inference as the source of measurements.
meaning: a label also refers to a package of logical
axioms with a proof theory. The Open Geospatial Consortium has some web-
service standards for shared data access (Sensor
Usually, the two notions of meaning fit. Web Enablement).

Goal to combine information and services for Goal is to open up access to real-time and archival
targeted purpose and new knowledge data, and to combine in applications.

So, what is a Semantic Sensor Web?

 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; and
 Across heterogeneous sensor network platforms;
 Using high-level descriptions of sensor network capability; and
 Interfacing to data integration methods using similar query and
capability descriptions.

 To create a Web of Real Time Meaning!

W3C SSN Incubator Group

 SSN-XG commenced: 1 March 2009

 Chairs:
 Amit Sheth, Kno.e.sis Center, Wright State University
 Kerry Taylor, CSIRO
 Amit Parashar  Holger Neuhaus  Laurent Lefort, CSIRO

 Participants: 39 people from 20 organizations, including:
 Universities in: US, Germany, Finland, Spain, Britain, Ireland
 Multinationals: Boeing, Ericsson
 Small companies in semantics, communications, software
 Research 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

Two main objectives:

The development of an ontology for describing
sensing resources and data, and
The extension of the SWE languages to support
semantic annotations.

SSN Ontology

 OWL 2 DL ontology

 Authored by the XG
participants

 Edited by Michael
Compton

 Driven by Use Cases

 Terminology carefully
tracked to sources through
annotation properties

 Metrics
 Classes: 117
 Properties: 148
 DL Expressivity:
SSN Ontology –
SIQ(D)

Stimulus-Sensor-Observation

 The SSO Ontology Design Pattern is developed following the principle of
minimal ontological 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
about environmental properties and construct features of interest.

SSN Sensor

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

SSN Measurement Capability

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

SSN Observation

 An Observation is a Situation in which a Sensing method has been used to estimate or
calculate a value of a Property.
 Links to Sensing and Sensor describe what made the Observation and how; links to
Property and Feature detail what was sensed; the result is the output of a Sensor; other
metadata gives the time(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.

What SSN does not model

 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

Recommended
technique via Xlink
attributes requires no
change to SWE

xlink:href - link to
ontology individual

xlink:role - link to
ontology class

xlink:arcrole - link to
ontology object property

Abstraction

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

Abstraction

 People are excellent at abstraction;
of sensing and interpreting stimuli
to understand and interact with the
world.

 The process of interpreting stimuli
is called perception; and studying
this extraordinary human capability
can lead to insights for developing
effective machine perception.

Abstraction

conceptualization
of “real-world”

observe perceive

“real-world”

Semantic Perception/Abstraction

Fundamental Questions

What is perception, and how can
we design machines to perceive?

What can we learn from cognitive
models of perception?

Is the Semantic Web up to the task
of modeling perception?

What is Perception?

Perception is the act of

 Abstracting

 Explaining

 Discriminating

 Choosing

What can we learn from
Cognitive Models of
Perception?

Ulric Neisser (1976)
Ulric Neisser (1976) Richard Gregory (1997)
Richard Gregory (1997)

 A-priori background knowledge is a key
enabler
 Perception is a cyclical, active process

Is Semantic Web up to the task
of modeling perception?

Representation
Heterogeneous sensors, sensing, and observation
records
Background knowledge (observable properties,
objects/events, etc.)

Inference
Explain observations (hypothesis building)
Focus attention by seeking additional stimuli (that
discriminate between explanations)

Difficult Issues to Overcome
Perception is an infe re nc e to the be s t e x p la na tio n
Handle streaming data
Real-time processing (or nearly)

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

observes
Observer Quality

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

The ability to perceive is afforded through the use of
background knowledge, relating observable qualities to
entities in the world.

Quality

* Formally described in
inheres in domain ontologies
(and knowledge bases)

Entity

With the help of sophisticated inference, both people and
machines are also capable of perceiving entities, such as
apples.
perceives
Perceiver Entity

 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)

Perceptual Inference

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

minimize
explanations tractabl
e

Web
degrade gracefully reasoning

Perceptual Inference
(i.e., abstraction)

The ability to perceive e ffic ie ntly is afforded through the
cyclical exchange of information between observers and
perceivers.

Observer

sends sends Traditionally called the
observation focus Perceptual Cycle
(or Active Perception)

Perceiver

Cognitive Theories of Perception

 1970’s – Perception is an active, cyclical process of
exploration and interpretation.
- N s s ie r’s Pe rc e p tio n Cy c le
e

 1980’s – The perception cycle is driven by
background knowledge in order to generate and test
hypotheses.
- Ric ha rd G re g o ry (o p tic a l illus io ns )

 1990’s – In order to effectively test hypotheses, some
observations are more informative than others.
- N rwic h’s Entro p y The o ry o f Pe rc e p tio n
o

Key Insights
Background 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 Issues
Internet/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 our perception

Integrated together, we have an general model – capable of
abstraction – relating observers, perceivers, and background
knowledge.

observes
Observer Quality

sends sends
observation inheres in
focus

perceives
Perceiver Entity

 Ontology of Perception – as an extension of SSN

 Provides abstraction of sensor data through perceptual
inference of semantically annotated data

Prior Knowledge

W3C SSN Ontology Bi-partite Graph

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

Semantics of Explanation

Ex p la na tio n 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 observed
properties.

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

Semantics of Explanation

Example
Assume 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
ExplanatoryFeature class is constructed:

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

Given the KB, executing the query
ExplanatoryFeature(?y) can infer the features,
Hypertension and Hyperthyroidism, as explanations:

ExplanatoryFeature(Hypertension)
ExplanatoryFeature(Hyperthyroidism)

Semantics of Discrimination

Dis c rim ina tio n is the act of deciding how to narrow down the multitude of
explanatory features through further observation.

Expected Property: A property is e x p e c te d with respect to (w.r.t.) a set
of features if it is a property of every feature in the set.
ExpectedProperty ≡ ∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ∃ssn:isPropertyOf.{fn}

NotApplicable Property: A property is no t-a p p lic a ble w.r.t. a set of
features if it is not a property of any feature in the set.
NotApplicableProperty ≡ ¬∃ssn:isPropertyOf.{f1} ⊓ … ⊓
¬∃ssn:isPropertyOf.{fn}

Discriminating Property: A property is d is c rim ina ting w.r.t. a set of
features if it is neither expected nor not-applicable.
DiscriminatingProperty ≡ ¬ExpectedProperty ⊓ ¬NotApplicableProperty

Semantics of Discrimination

Example
Given the explanatory features from the previous
example, Hypertension and Hyperthyroidism, the following
classes are constructed:

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)

Efficient Algorithms for IntellegO

 Use of OWL-DL reasoner too resource-intensive for use in
resource constrained devices (such as sensor nodes, mobile
phones, IoT devices)
 Runs out of resources for problem size (prior knowledge) > 20
concepts
 Asymptotic complexity: O(n3) [Experimentally determined]

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

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


Semantic (RDF) Bit Vector Encoding
Encoding

Lower

Lift

 First, developed lifting and
lowering algorithms to translate
between RDF and bit vector
encodings of observations.


Explanation Algorithm Utilize bit vector operators to efficiently
compute explanation and
discrimination
Explanation: Use of the bit vector AND
operation to discover and d is m is s those
features that cannot explain the set of
Discrimination Algorithm observed properties

Discrimination: Use of the bit vector AND
operation to discover and indirectly a s s e m ble
those properties that discriminate between a
set of explanatory features. The discriminating
properties are those that are determined to be
neither expected nor not-applicable


Evaluation: The bit vector encodings and algorithms yield significant and
necessary computational enhancements – including asymptotic order of
magnitude improvement, with running times reduced from minutes to
milliseconds, and problem size increased from 10’s to 1000’s.

Linked Sensor Data

Linked Sensor Data
(~2 Billion Statements)

Sensor Discovery Application

Query w/ location name to find nearby sensors

SSN Applications

Applications of
SSN
Weather Rescue Healthcare

SSN Application: Weather

 50% savings in sensing
resource requirements during
the detection of a blizzard

 Order of magnitude resource
savings between storing observations
vs. relevant abstractions

SSN Application: Fire Detection

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

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

SSN Application: Health Care

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

Passive Monitoring Phase
Passive Monitoring Phase

Observed Symptoms Possible Explanations

• Abnormal heart rate • Panic Disorder
• Clammy skin • Hypoglycemia
• Hyperthyroidism
• Heart Attack
• Septic Shock

Passive Sensors – heart rate, galvanic skin
response

Active Monitoring Phase
Active Monitoring Phase
Are you feeling lightheaded?
Are you feeling lightheaded?

yes
yes

Are you have trouble taking deep
Are you have trouble taking deep Observed Symptoms Possible Explanations
breaths?
breaths?
• Abnormal heart rate • Panic Disorder
yes
yes • Hypoglycemia
• Clammy skin
• Lightheaded • Hyperthyroidism
Do you have low blood pressure? • Trouble breathing • Heart Attack
Do you have low blood pressure?
• Low blood pressure • Septic Shock

yes
yes

Have you taken your Methimazole
Have you taken your Methimazole
medication?
medication?

no
no

Active Sensors – blood pressure, weight scale, pulse

Future work

 Creating ontologies and defining data models are not
enough
 tools to create and annotate data
 Tools for publishing linked IoT data
 Designing lightweight versions for constrained
environments
 think of practical issues
 make it as much as possible compatible and/or link it to the other
existing ontologies
 Linking to domain knowledge and other resources
 Location, unit of measurement, type, theme, …
 Linked-data
 URIs and naming

Some of the open issues

 Efficient real-time IoT resource/service
query/discovery
 Directory
 Indexing
 Abstraction of IoT data
 Pattern extraction
 Perception creation
 IoT service composition and compensation
 Integration with existing Web services
 Service adaptation

Selected references
 Payam Barnaghi, Wei Wang, Cory Henson, Kerry Taylor, "Semantics for the Internet of Things: early progress and back to the future", (to
appear) International Journal on Semantic Web and Information Systems (special issue on sensor networks, Internet of Things and
smart devices), 2012.
 Atzori, L., Iera, A. & Morabito, G. , “The Internet of Things: A survey”, Computer Networks, Volume 54, Issue 15, 28 October 2010, 2787-
2805.
 Suparna De, Tarek Elsaleh, Payam Barnaghi , Stefan Meissner, "An Internet of Things Platform for Real-World and Digital Objects",
Journal of Scalable Computing: Practice and Experience, vol 13, no.1, 2012.
 Suparna De, Payam Barnaghi, Martin Bauer, Stefan Meissner, "Service modelling for the Internet of Things", in Proceedings of the
Conference on Computer Science and Information Systems (FedCSIS), pp.949-955, Sept. 2011.
 Cory Henson, Amit Sheth, and Krishnaprasad Thirunarayan, “Semantic Perception: Converting Sensory Observations to Abstractions”,
IEEE Internet Computing, Special Issue on Context-Aware Computing, March/April 2012.
 Payam Barnaghi, Frieder Ganz, Cory Henson, Amit Sheth, “Computing Perception from Sensor Data”, In proceedings of the 2012 IEEE
Sensors Conference, Taipei, Taiwan, October 28-31, 2012.
 Michael Compton et al, “The SSN Ontology of the W3C Semantic Sensor Network Incubator Group”, Journal of Web Semantics, 2012.
 Harshal Patni, Cory Henson, and Amit Sheth , “Linked Sensor Data”, in Proceedings of 2010 International Symposium on Collaborative
Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.
 Amit Sheth, Cory Henson, and Satya Sahoo , “Semantic Sensor Web IEEE Internet Computing”, vol. 12, no. 4, July/August 2008, pp. 78-
83.
 Wei Wang, Payam Barnaghi, Gilbert Cassar, Frieder Ganz, Pirabakaran Navaratnam, "Semantic Sensor Service Networks", (to appear)
in Proceedings of the IEEE Sensors 2012 Conference, Taipei, Taiwan, October 2012.
 Wang W, De S, Toenjes R, Reetz E, Moessner K, "A Comprehensive Ontology for Knowledge Representation in the Internet of Things",
International Workshop on Knowledge Acquisition and Management in the Internet of Things (KAMIoT 2012) in conjunction with IEE
IUCC-2012, Liverpool, UK. Liverpool. 25-27 June, 2012.

Some useful links related to IoT
 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/

Data Modeling and Knowledge Engineering for the Internet of Things

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