Download
1 / 12
120 likes | 260 Views
Mobile Crowdsensing , Social and Big Data as Innovation Enablers for Future Internet Cloud-based Architectures and Services. Resource Abstraction & Virtualization. S. Kafetzoglou , C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory
E N D
Mobile Crowdsensing, Social and Big Data as Innovation Enablers for Future Internet Cloud-based Architectures and Services Resource Abstraction & Virtualization S. Kafetzoglou, C. Papagianni skafetzo@netmode.ntua.gr Network Management & Optimal Design Laboratory National Technical University of Athens Athens - March 18, 2014
Resource Virtualization Virtualization is mainly applied and refers to network, storage and computing resources
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 applications Image source: http://www.nconnect.com
The method Emerge of Virtual Sensor Nodes & Virtual Sensor Networks. • 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
Virtual Sensor Networks • Subset of sensor nodes of WSNs for specific applications • Several challenges arise • Isolation • Manageability • Scalability Introduction of Virtualization / Abstraction layer • Relevant Projects • VSNs • VITRO • FRESnel
Sensor Virtualization • Most common solution: introduction of a virtualization layer • Provides abstraction for • the programmers • Mate VM one of the first • virtualization approaches Difficulties to integrate sensors due to heterogeneity issues sensor
Participatory Sensing Support advanced apps Resource rich Communicate with external sensors Equipped with 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/
Mobile Crowdsensing • Applications • Environmental (eg. Common Sense) • Infrastructure (eg. MIT’s CarTel) • Social (eg. BikeNet and DietSense) Mobile Crowdsensing Opportunistic sensing Participatory sensing Image source: http://www.vimeo.com Participatory Sensing and Crowd Management in Public Spaces 2011 Lord Mayor's Show
References • 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. • 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. • 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. • 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. • 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 • C. Reed, M. Botts, J. Davidson, G. Percivall, “OGC® Sensor Web Enablement: Overview and High Level Architecture”, IEEE Autotestcon, pp. 372-380, 2007. • 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. • 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. • C. Goodwin, and D. J. Russomanno, “An ontology-based sensor network prototype environment”, 5th International Conference on Information Processing in Sensor Networks, 2006. • 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. • 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. • 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. • 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)
