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Self-Organizing Logical-Clustering Topology for Managing Distributed
Context Information
M.Sc. Hasibur RahmanDepartment of Computer and Systems Sciences,
Stockholm University2015-09-24
2015/09/24/Hasibur Rahman, Licentiate Seminar, DSV
Why manage Context Information? (1)
• Everyday Things get connected• Connected Things > human. Today, the ratio is 1.5:1 [Cisco]• Hundreds of billions by 2020 [Ericsson, Cisco]• Not the scale only but also expanding in its scope• Vast network of Context Information (CI) will remain underutilized if
not properly managed• According to IBM, 90% of CI in IoT is never utilized or acted upon
[IBM 15]
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But how?
• This necessitates proper management of context information
• Clustering efficient management context information
• This thesis proposes a distributed clustering approach namely logical-clustering.
• The idea is to cluster entities (e.g. things in IoT landscape) based on similar context as opposed to physical location-based clustering of entities
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Problem statement
• An architecture that reflects the real world scenario
• A model which enables scalable and dynamic dissemination of context information
• The rapidly changing dynamic environment further mandates to bring about self-organization
• Performance evaluation
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Research question
• How can a self-organizing logical-clustering topology be realized towards efficiently managing a vast network of distributed context information?
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Related work
• e-SENSE [Lombriser et al. 07]• WSN logical neighborhoods [Mottola and Picco 06]• Architectures for connecting entities and provisioning
context information [JW14]• Research until now did not focus on real-time distributed
context information management [Miorandi et al. 12]• A move towards future IoT requires distributed support such
as layering DHT i.e. H-DHT [Zoel et al. 08]• Controlling heterogeneous entities and networks such as in
IoT exposes many challenges that can be resolved via SDN [Pedro and Antonio 14]
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Contributions
• Demonstarted the challenges that require to enable distributed CI clustering
• Four research sub-questions (SQs)– A system architecture [Publication # I]– Distributed PubSub Model [Publication # III & IV]– Self-organization [Publication # V]– Network performance [Publication # II]
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Distributed PubSub Model
• To fulfill the requirements for scalable heterogeneous context information dissemination and logical-sink synchronization
• By extending the DCXP protocol and evaluated on a proven IoT platform namely MediaSense
• High PubSub messages/sec• Fast context-ID matching
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Self-organization
• In line with the vision of autonomic computing and the control loop of IoE, three self-* aspects have been designed and developed
• DCXP was further extended and two new primitive functions were added
• The algorithms now enable each entity to organize itself and further enabled stability and resilience within logical-clustering
• Resulted in fast discovery of entity and high discovery accuracy
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Network Performance
• Mean delay and packet loss ratio were examined• For variant information flow rate, and different cluster size
and entity per cluster• 13% mean delay fluctuated when flow rate and cluster size
are increased by 60% and 200% respectively• PLR increases by 20% for 100% increase in cluster size
each time• Flow rate increases by about 338 % when packet size is
halved and PLR decreases by 15%
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Conclusions• The thesis has grounded the vision of logical-clustering• Several artefacts have been designed, developed, and
evaluated• A fast, scalable and dynamic architecture• Promising results:
– fast subscription matching– high PubSub messages and formulas for PubSub outcome
prediction– fast discovery of entities– high discovery accuracy
• A template for enabling autonomic computing in IoT has been portrayed
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Future work
• Context-based clustering algorithm needs to be designed and developed
• Policies to enable autonomic computing in IoT needs to be explored
• A more concrete and capable sink in IoT such as SDN controller should be incorporated to see a fully functional autonomic IoT
• A naming scheme along with UCI can be explored to ensure unique identification of entity in IoT landscape
• Exploring load-balancing and scheduling algorithms for sinks can result in higher discovery accuracy
• Heterogeneous interoperability of the sinks should also be examined
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References• [online] Big data and analysis, Internet of Things by IBM link:
http://www-01.ibm.com/software/info/internet-of-things/iot-prod/iot-announcement.html
• J. Walters, “Distributed Immersive Participation – Realising Multi-Criteria ContextCentric Relationships on an Internet of Things”, PhD thesis, Department of Computer and Systems Sciences, Stockholm University, November 2014
• C. Lombriser, M. Marin-Perianu, R. Marin-Perianu, D. Roggen, P. Havinga, G. Troster, “Organizing Context Information Processing in Dynamic Wireless Sensor Networks”, Proc. ISSNIP,pp. 67-72,December 2007
• L. Mottola and G. P. Picco: Logical Neighborhoods: A Programming Abstraction for Wireless Sensor Networks, Distributed Computing in Sensor Systems, Volume 4026, 2006, pp 150-168
• H. Gellersen, A. Schmidt, M. Beigl, "Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts", Mobile Networks and Applications, vol. 7, no.5, pp.341-351, 2002
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References (2)
• Pedro Martinez-Julia, Antonio F. Skarmeta, white paper ” Extending the Internet of Things to IPv6 with Software Defined Networking”
• T. Kanter et al.., “MediaSense – an Internet of Things Platform for Scalable and Decentralized Context Sharing and Control,” In: ICDT 2012,, The Seventh International Conference on Digital Telecommunications, pp. 27-32, April 2012
• S. Zoels, Z. Despotovic, W. Kellerer:, “On hierarchical DHT systems- An analytical approach for optimal design”, Computer Communications, Volume 31(3), Page 576-590, 2008
• R.A. Baloch and Noel Crespi. Addressing context dependency using profile context in overlay networks. In Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, pages 1–5. IEEE, January 2010
2015/09/24/Hasibur Rahman, Licentiate Seminar, DSV