self-organizing logical-clustering topology for managing distributed context information

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]


http://www.cisco.com/web/solutions/trends/iot/overview.html


Why manage Context Information? (2)

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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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System Model (tiered H-DHT)



Example

Communication


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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


Autonomic computing


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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


Thanks for your kind attention!

Questions?


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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


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