Bringing Wireless Sensing to its full potential

”Bringing Wireless Sensing to its full potential”

Wireless Sensor Networks
TKT-2456

Multimedia group
Adrian Hornsby

… bringing wireless sensor to its full potential
Outline

… sensing the future
Search for Wisdom
➢

User and its Desires
➢

Internet of Things
➢

… bringing the web into sensors
6LoWPAN
➢

Semantic Sensor Web
➢

Efficient XML Interchange (EXI)
➢

Search for wisdom
Understanding the User:
Search for wisdom
➢

Leveraging Information
➢
Distil wisdom
Interacting with the world
➢

→ knowledge hierachy Assimilate knowledge

Compare information

Collect data

Observe facts

proto-facts

Climbing the data federation pyramid

Research emphasis within the
➢

biology and computer science
communities in the 1980s.

Extreme diversity in physical
➢

hardware, OS, DBs, software and
immature networking protocols
hampered the sharing of data.

Emergence of the web
➢

Flexible format for data exchange
➢
http://www.mkbergman.com/
http://brightplanet.com/data-federation-a-semantics.html

Annotation and attribute in DB
➢

resulting in new discoveries

A world of data and sensors

Internet of Things = 1012

Fringe Internet = 109

Core Internet
= 106

http://www.sensinode.com/

Desires

… from stove-piped sensor to global sensing

Quickly discover & retrieve information from sensors
➢

Meet user needs
➢

Location, observation, quality, ability to fuse information
➢

Standard sensor descriptions
➢

understandable by users, softwares and other sensors
➢

Subscribe to and receive alerts from sensors
➢

Standardized web-services to access information
➢

Sensor capable of responding to other sensors
➢

Autonomous
➢

Adaptable, mobile and flexible
➢

http://www.janchipchase.com/

Closing the gap between users, Internet and sensors

Web Services
Communication protocol
➢

Interface description and information
➢

URI (Universal Resource Identifier)
➢

User
Internet

Sensors

Benefits of IP

Open, long-lived, reliable standard
➢

Easy learning-curve
➢

Transparent Internet integration
➢

Network maintainability
➢

Proven global scalability
➢

“You never lose with IP”
➢

http://www.janchipchase.com/

IP for smaller devices supported by Standards and Organization:
➢

IPSO alliance
➢

IETF
➢

IEEE
➢

Applications for IP-based WSN

Wireless sensor and actuator networks (WS&AN) , Environmental Sensor Networks (ESN), Object
Sensor Networks (OSN) or Body Sensor Network (BSN)

6LoWPAN – IP for low power devices

IETF Standard for IPv6 over IEEE 802.15.4
80% compression of headers
➢

Rich and flexible features
➢

Auto-configuration
➢

IPv6 fragmentation
➢

UDP + ICMP
➢

Mesh forwarding
➢

Common Socket API
➢

Super compact implementation Sockets
➢

Direct end-to-end Internet integration
➢
UDP + ICMP
Extremely scalable
➢

IPv6 + LoWPAN

802.15.4 MAC

2.4 GHz CSS UWB

http://www.sensinode.com/

6LoWPAN features

Support for 64-bit and 16-bit 802.15.4 addressing
16-bit addresses can automatically be assigned
➢

Extreme header compression
➢

Unicast, broadcast and multicast support
➢

Fragmentation
➢

1260 byte IPv6 frames -> 127 byte 802.15.4 frames
➢

Link-layer mesh routing support
➢

Original source & final destination addresses
➢

Hops left
➢

Routing decision made hop-by-hop
➢

6LoWPAN – IP for low power devices

•

Natural next step

To infer high-level knowledge, sensor data needs to be:
➢

filtered,
➢

aggregated,
➢

correlated
➢

and translated.
➢

Data federation pyramid
➢

After network come the data representation
➢

Data representation: Challenges

Lack of Uniform operations and standard representation of sensor
➢

data
No means for resource reallocation and resource sharing
➢

Deployment and usage tightly coupled with location, application and
➢

device employed

→ Lack of interoperability

Need for Interoperability

Ability for two or more autonomous, heterogeneous, distributed
➢

entities to communicate and cooperate despite differences in
language, context, format or content.

Should be able to interact with one another in meaningful ways
➢

without special effort by the user – the data producer or consumer

→ Standard XML format for data representation

Survey: Sensor data management frameworks

GSN (Global Sensor Network, Digital Enterprise Research Institute)
➢

http:// gsn.sourceforge.net/

Hourglass (Harvard)
➢

http://www.eecs.harvard.edu/~syrah/hourglass/

An Infrastructure for Connecting Sensor Networks and Applications
➢

IrisNet (Intel & Carnegie Mellon University)
Internet-Scale Resource-Intensive Sensor Network Service
http://www.intel-iris.net/

Sensorweb Research Laboratory
➢

http://sensorweb.vancouver.wsu.edu/research.html

… and more !!

→ only localized interoperability

Standard-based frameworks

SensorWeb project at University of Melbourne
➢

http://www.gridbus.org/sensorweb/

52°North's Sensor Web Community
➢

NASA JPL/GSFC SensorWeb, Northrop Grumman's PULSENet
➢

Open Geospatial Consortium (OGC)

Sensor Web Enablement Framework
Consortium of 330+ companies, government agencies, and
➢

academic institutes
Open Standards development by consensus process
➢

Interoperability Programs provide end-to-end implementation
➢

and testing before specification approval
Develop standard encodings and Web service interfaces
➢

Sensor Web Enablement
➢

Sensor Web Enablement - Languages

Information Model and Sensor and
Observations and Processing Description
Sensing Language

Observation
& SensorML
Measurements (SML)
(OM)

GeographyML TransducerML
(GML) (TML)

Common Model for Multiplexed, Real Time
Geographical Streaming Protocol
Information

Sensor Web Enablement – Web Services

Sensor Web Enablement - Components

1. Sensor Model Language (SensorML) – The general models and XML encodings for
sensors and observation processing.
2. Observations & Measurements (O&M) - The general models and XML encodings for
sensor observations and measurements.
3. TransducerML (TML) – A model and encoding for streaming multiplexed data from a
sensor system, and for describing the system and data encoding.
4. Sensor Observation Service (SOS) – A service by which a client can obtain
observations from one or more sensors/platforms (can be of mixed sensor/platform
types).
5. Sensor Planning Service (SPS) – A standard service for requesting user-driven
acquisitions an observations.
6. Sensor Alert Service (SAS) – A service for publishing and subscribing to alert from
sensors.
7. Web Notification Service (WNS) – Standard web service for asynchronous delivery
of messages or alerts.

SensorML: building block

Provides standard models and an XML encoding for describing
sensors and measurement processes.
Can be used to describe a wide range of sensors, including
both dynamic and stationary platforms and both in-situ and
remote sensors.

sensor discovery
➢

sensor geolocation
➢

processing observations
➢

programming mechanism
➢

subscription mechanism
➢

… Semantic Sensor Web
What is it ?

Adding semantic annotations to existing standard Sensor
➢

Web languages in order to provide semantic descriptions and
enhanced access to sensor data

This is accomplished with model-references to ontology
➢

concepts that provide more expressive concept descriptions

What is it ?

RDF: Ressource Description Framework

Used for semantically annotating XML documents.
➢

Several important attributes within RDFa include:
➢

→ about: describes subject of the RDF triple
→ rel: describes the predicate of the RDF triple
→ resource: describes the object of the RDF triple
→ instanceof: describes the object of the RDF triple with the
predicate as “rdf:type”

On going work in W3C

Semantic Sensor Network (SSN) Incubator group:
The mission of the Semantic Sensor Network Incubator
Group, part of the Incubator Activity, is to begin the formal
process of producing ontologies that define the capabilities of
sensors and sensor networks, and to develop semantic
annotations of a key language used by services based
sensor networks.

The big picture

Semantic
Data Storage
Analysis & Query
Knowledge

Data Feature
Detection & Extraction
Semantic
Annotation
Ontologies
Sensor Data

Internet

And for Low Power nodes ??

Transfering XML is costly !! (for ultra low power devices)
→ 1 bit = bandwidth = power

Compression and Binarization of XML
→ Efficient XML Interchange format (EXI)

EXI: knowledge based encoding that uses a set of grammars to
determine which events are most likely to occur at any given point in
an EXI stream and encodes the most likely alternatives in fewer bits
➢


<?xml version=quot;1.0quot; encoding=quot;UTF-8quot;?>
<notebook date=quot;2007-09-12quot;>
➢ <note date=quot;2007-07-23quot; category=quot;EXIquot;>
<subject>EXI</subject>
<body>Do not forget it!</body>
</note>
<note date=quot;2007-09-12quot;>
<subject>Shopping List</subject>
<body>milk, honey</body>
</note>
</notebook>


EXI Grammar (Event Coding)
→ Productions separated according to their popularity
➢

<?xml version=quot;1.0quot; encoding=quot;UTF-8quot;?>
<notebook date=quot;2007-09-12quot;>
<note date=quot;2007-07-23quot; category=quot;EXIquot;>
<subject>EXI</subject>
<body>Do not forget it!</body>
</note>
<note date=quot;2007-09-12quot;>
<subject>Shopping List</subject>
<body>milk, honey</body>
</note>
</notebook>

… Bringing Wireless Sensor to its full potential
Conclusion

In the near future:

More Users, more Sensors, more Data
➢

Wide Integration with Internet through IP protocol
➢

Advanced data representation with XML
➢

Semantic for better sensor information access
➢

Knowledge even from ultra low power device using EXI
➢

All through global standards (W3C, IETF, ...)
➢

References: Standards & Projects
(1) IPSO Alliance - http://www.ipso-alliance.org

(2) 6LoWPAN: http://www.ietf.org/html.charters/6lowpan-charter.html - http://tools.ietf.org/wg/6lowpan/

(3) W3C Semantic Sensor Network Incubator group - http://www.w3.org/2005/Incubator/ssn/ -
http://www.w3.org/2005/Incubator/ssn/charter

(4) OGC – Sensor Web Enablement WG: http://www.opengeospatial.org/projects/groups/sensorweb

(5) Sensor Standards and Data Harmonization (NIST) -
http://semanticommunity.wik.is/Sensor_Standards_and_Data_Harmonization

(6) Marine Metadata Interoperability - http://marinemetadata.org/

(7) http://ieee1451.nist.gov/

(8) http://www.transducerml.org/

(9) W3C other:
(1) Geospatial Incubator Group - http://www.w3.org/2005/Incubator/geo/
(2) Delivery context ontology http://www.w3.org/TR/dcontology/
(3) Product Modelling Incubator http://www.w3.org/2005/Incubator/w3pm/

(10) EXI: http://www.w3.org/TR/exi-primer/

References: publications
(1) Li Ding, Pranam Kolari, Zhongli Ding, Sasikanth Avancha, Tim Finin, Anupam Joshi. Using Ontologies in the Semantic Web: A Survey

(2) Cory Henson, Josh Pschorr,Amit Sheth, Krishnaprasad Thirunarayan, quot;SemSOS: Semantic Sensor Observation Service,quot; in Proceedings of the
2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Baltimore, MD, May 18-22, 2009.

(3) Payam M. Barnaghi, Stefan Meissner, Mirko Presser, and Klaus Moessner, quot;Sense and Sensíability: Semantic Data Modelling for Sensor Networksquot;,
to appear, in Proceedings of the ICT Mobile Summit 2009, June 2009.

(4) Lily Li, Kerry Taylor: A Framework for Semantic Sensor Network Services. ICSOC 2008: 347-361

(5) Amit Sheth, Cory Henson, and Satya Sahoo, quot;Semantic Sensor Web,quot; IEEE Internet Computing, July/August 2008, p. 78-83.

(6) Alex Wun, Milenko Petrovi, and Hans-Arno Jacobsen. A system for semantic data fusion in sensor networks. In DEBS í07: Proceedings of the 2007
inaugural international conferenceon Distributed event-based systems, pages 75-79, New York, NY, USA, 2007. ACM.

(7) M. Eid, R. Liscano, and A. El Saddik. A universal ontology for sensor networks data. Computational Intel ligence for Measurement Systems and
Applications, 2007. CIMSA 2007. IEEE International Conference on, pages 59–62, June 2007

(8) Micah Lewis, Delroy Cameron, Shaohua Xie, Budak Arpinar,ES3N: A Semantic Approach to Data Management in Sensor Networks. Semantic
Sensor network workshop, the 5th International Semantic Web Conference ISWC 2006, November 5-9, Athens, Georgia, USA 2006

(9) Hideyuki Kawashima, Yutaka Hirota, Satoru Satake, and Michita Imai. Met: A real world oriented metadata management system for semantic sensor
networks. In Proc. of the International Workshop on Data Management for Sensor Networks (DMSN, pages 588{599, 2006.

(10) Russomanno, D.J., Kothari, C., Thomas, O.: Sensor ontologies: from shallow to deep models. Proceedings of the Thirty-Seventh Southeastern
Symposium on System Theory, 2005. SSST '05. 20-22 March 2005.

(11) David J. Russomanno, Cartik R. Kothari, and Omo ju A. Thomas. Building a sensor ontology: A practical approach leveraging iso and ogc models.
In IC-AI, pages 637–643, 2005.

(12) Semantic Sensor Net: An Extensible Framework. In Proceedings of the International Conference on Computer Network and Mobile Computing,
Lecture Notes in Computer Science 3619, pages 1144--1153, 2005.

(13) C. Matheus, D. Tribble, M. Kokar, M. Cerutti and S. McGirr. Towards a Formal Pedigree Ontology for Level-One Sensor Fusion. 10th International
Command and Control Research and Technology Symposium, McClain, Virginia, June 2005.

(14) Holger Neuhaus, Relating Sensor Observations to the Real World, FOIS 2008.

Bringing Wireless Sensing to its full potential

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