S. Kafetzoglou, C. Papagianni [email protected] Network Management & Optimal Design...

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S. Kafetzoglou, C. Papagianni [email protected] Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing, Social and Big Data as Innovation Enablers for Future Internet Cloud-based Architectures and Services Athens - March 18, 2014 Resource Abstraction & Virtualization

Transcript of S. Kafetzoglou, C. Papagianni [email protected] Network Management & Optimal Design...

Page 1: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

S. Kafetzoglou, C. [email protected] Management & Optimal Design LaboratoryNational Technical University of Athens

Mobile Crowdsensing, Social and Big Data as Innovation Enablers for Future Internet Cloud-based Architectures and Services

Athens - March 18, 2014

Resource Abstraction & Virtualization

Page 2: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Virtualization technologies:• mechanisms for decoupling hardware and software resources

from their physical implementation.

Key point to resource virtualization: • introduction of an indirection layer below the execution

environment seen by applications and operating systems.

Virtualization is mainly applied and refers to network, storage and computing resources

Resource Virtualization

Page 3: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Advent of IoT concept

The IoT vision: allow connectivity of anything from anywhere at anytime

Sensors and actuators play a vital role in this new digital ecosystem

Apply virtualization and abstraction techniques to sensing resources

new powerful applicationsImage source: http://www.nconnect.com

Page 4: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

The need

Traditional sensors & WSNs

• Application specific, no reusability• Limited capabilities, • Heterogeneity in hardware, OS,

communication protocols.

Challenge

• Allow heterogeneous sensors co-exist• Clean separation of services and infrastructure• Trading of sensor resources among several

service providers• Build innovative, cost-effective applications and

technical solutions

Solution

• Abstraction and virtualization of sensing resources

Page 5: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

The method

Virtual Sensor• Software sensor as opposed to a physical sensor.• Can be either an abstracted sensor in an overlay network, a WSN,

or an aggregated measurement of a group of heterogeneous physical sensors.

VSNs• Collaborative form of WSNs• Subset of sensor nodes of WSNs• Dedicated to a certain task• Can be easily reconfigured and re-purposed

Emerge of Virtual Sensor Nodes & Virtual Sensor Networks.

Page 6: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Introduction of Virtualization / Abstraction layer

Virtual Sensor Networks

Subset of sensor nodes of WSNs for specific applications

Several challenges arise Isolation Manageability Scalability

Relevant Projects

VSNs VITRO FRESnel

Page 7: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Sensor Virtualization

Most common solution: introduction of a virtualization layer

Provides abstraction for the programmers Mate VM one of the first virtualization approaches

sensor

Difficulties to integrate sensors due to heterogeneity issues

Page 8: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Sensor Data Modeling

• Sensor Model Language (SensorML)• Observations & Measurements (O&M)• Sensor Observations Service (SOS) 

OGC Sensor Web Enablement framework (SWE)

• Ontology for sensor and sensor data (CSIRO)• Annotation framework for semantic metadata to

SWE framework

W3C Semantic Sensor Incubator Group

• SUMO• OntoSensor• LENS

Significant Research Work

Page 9: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Participatory Sensing

Resource rich

Support advanced

apps

Equipped with sensors

Communicate with external

sensors

Participatory sensing: deployed mobile devices form interactive, participatory sensor networks for enabling users to gather, analyze and share local knowledge

Applications: public health, urban planning, public transportation

Image source: http://complexitys.com/

Page 10: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Mobile Crowdsensing

Participatory sensing

Opportunistic sensing

Mobile Crowdsensing

Applications Environmental (eg. Common Sense) Infrastructure (eg. MIT’s CarTel) Social (eg. BikeNet and DietSense)

Image source: http://www.vimeo.com Participatory Sensing and Crowd Management in Public Spaces 2011 Lord Mayor's Show

Page 11: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

References1. M.M. Islam, M. M. Hassan, G.W. Lee, & E.N. Huh, “A survey on virtualization of wireless sensor

networks”. Sensors, 12(2), 2175-2207.2. M. Iqbal, D. Yang, T. Obaid, T. J. Ng, H. B. Lim, "A Service-Oriented Application Programming

Interface for Sensor Network Virtualization", in Proc. of 10th International Conference on Information Processing in Sensor Networks, Chicago, USA, April 2011.

3. A. P. Jayasumana, H. Qi and T. H. Illangasekare, "Virtual sensor networks - A resource efficient approach for concurrent applications," in 4th International Conference on Information Technology-New Generations, ITNG 2007, 2007, pp. 111-115.

4. S. Alam, M. M. R. Chowdhury, and J. Noll, "SenaaS:An Event-driven Sensor Virtualization Approach for Internet of Things Cloud", in Proc. of the 1st IEEE International Conference on Networked Embedded Systems for Enterprise Applications, Suzhou, China, November 2010.

5. M. Navarro, M. Antonucci, L. Sarakis, and T. Zahariadis, "VITRO architecture: Bringing Virtualization to WSN world", in Proc. of the 8th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, Valencia, Spain, October 2011

6. C. Reed, M. Botts, J. Davidson, G. Percivall, “OGC® Sensor Web Enablement: Overview and High Level Architecture”, IEEE Autotestcon, pp. 372-380, 2007.

7. H. Neuhaus, and M. Compton, “The Semantic Sensor Network Ontology: A Generic Language to Describe Sensor Assets”, AGILE Workshop Challenges in Geospatial Data Harmonisation, 2009.

8. M. Eid, R. Liscano, A. El-Saddik, “A Universal Ontology for Sensor Networks Data”, IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp. 59–62, 2007.

9. C. Goodwin, and D. J. Russomanno, “An ontology-based sensor network prototype environment”, 5th International Conference on Information Processing in Sensor Networks, 2006.

10. X. Ju, H. Zhang, W. Zeng, M. Sridharan, J. Li, A. Arora, R. Ramnath, and Y. Xin, "LENS: Resource Specification for Wireless Sensor Network Experimentation Infrastructures", in Proc. of the 6th ACM International Workshop on Wireless network testbeds, experimental evaluation and characterization, Las Vegas, USA, September 2011.

11. S. Papavassiliou, C. Papagianni, S. Distefano, G. Merlino and A. Puliafito, "M2M Interactions Paradigm Via Volunteer Computing And Mobile Crowdsensing", in "Machine-To-Machine Communications - Architectures, Technology, Standards, and Applications" book, Taylor and Francis, edited by V. Misic and J. Misic, Ch. 11, March 2014.

12. N. Mitton, S. Papavassiliou, A. Puliafito and K.S. Trivedi, "Combining Cloud and sensors in a smart city environment", in EURASIP Journal on Wireless Communications and Networking Vol. 2012, (doi: 10.1186/1687-1499-2012-247), 2012.

13. A. Puliafito, N. Mitton, S. Papavassiliou, K.S. Trivedi: Editorial: Special Issue on Internet of Things: Convergence of Sensing, Networking, and Web Technologies. EURASIP J. Wireless Comm. and Networking 2012: 212 (2012)

Page 12: S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Mobile Crowdsensing,

Thank you