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Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: International Journal of Advanced Research in Artificial Intelligence, ISSN 2165-4050, Vol. 3, no 11Article in journal (Refereed) Published
Abstract [en]

The upsurge of social networks, mobile devices, Internet or Web-enabled services have enabled unprecedented level of human participation in pervasive computing which is coined as crowdsourcing. The pervasiveness of computing devices leads to a fast varying computing where it is imperative to have a model for catering the dynamic environment. The challenge of efficiently distributing context information in logical-clustering in crowdsourcing scenarios can be countered by the scalable MediaSense PubSub model. MeidaSense is a proven scalable PubSub model for static environment. However, the scalability of MediaSense as PubSub model is further challenged by its viability to adjust to the dynamic nature of crowdsourcing. Crowdsourcing does not only involve fast varying pervasive devices but also dynamic distributed and heterogeneous context information. In light of this, the paper extends the current MediaSense PubSub model which can handle dynamic logical-clustering in crowdsourcing. The results suggest that the extended MediaSense is viable for catering the dynamism nature of crowdsourcing, moreover, it is possible to predict the near-optimal subscription matching time and predict the time it takes to update (insert or delete) context-IDs along with existing published context-IDs. Furthermore, it is possible to foretell the memory usage in MediaSense PubSub model.

Place, publisher, year, edition, pages
Bradford: The Science and Information (SAI) Organization , 2014. Vol. 3, no 11
Keyword [en]
Internet, crowdsourcing; pervasive computing; context information; dynamism, context-ID; logical-clustering; Publish/Subscribe; MediaSense
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-111093DOI: 10.14569/IJARAI.2014.031106OAI: oai:DiVA.org:su-111093DiVA: diva2:774230
Available from: 2014-12-22 Created: 2014-12-22 Last updated: 2015-10-23Bibliographically approved
In thesis
1. Self-Organizing Logical-Clustering Topology for Managing Distributed Context Information
Open this publication in new window or tab >>Self-Organizing Logical-Clustering Topology for Managing Distributed Context Information
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Internet of Things (IoT) is on the verge of experiencing a paradigm shift, the focus of which is the integration of people, services, context information, and things in the Connected Society, thus enabling Internet of Everything (IoE). Hundreds of billions of things will be connected to IoT/IoE by 2020. This massive immersion of things paves the way for sensing and analysing anything, anytime and anywhere. This everywhere computing coupled with Internet or web-enabled services have allowed access to a vast amount of distributed context information from heterogeneous sources. This enormous amount of context information will remain under-utilized if not properly managed. Therefore, this thesis proposes a new approach of logical-clustering as opposed to physical clustering aimed at enabling efficient context information management.

However, applying this new approach requires many research challenges to be met. By adhering to a design science research method, this thesis addresses these challenges and proposes solutions to them. The thesis first outlines the architecture for realizing logical-clustering topology for which a two-tier hierarchical-distributed hash table (DHT) based system architecture and a Software Defined Networking (SDN)-like approach are utilized whereby the clustering identifications are managed on the top-level overlay (as context storage) and heterogeneous context information sources are controlled via the bottom level. The feasibility of the architecture has been proven with an ns-3 simulation tool. The next challenge is to enable scalable clustering identification dissemination, for which a distributed Publish/Subscribe (PubSub) model is developed. The massive number of immersed nodes further necessitates a dynamic self-organized system. The thesis concludes by proposing new algorithms with regard to autonomic management of IoT to bring about the self-organization. These algorithms enable to structure the logical-clustering topology in an organized way with minimal intervention from outside sources and further ensure that it evolves correctly. A distributed IoT context information-sharing platform, MediaSense, is employed and extended to prove the feasibility of the dynamic PubSub model and the correctness of self-organized algorithms and to utilize them as context storage. Promising results have provided a high number of PubSub messages per second and fast subscription matching. Self-organization further enabled logical-clustering to evolve correctly and provided results on a par with the existing MediaSense for entity joining and high discovery rates for non-concurrent entity joining.

The increase in context information requires its proper management. Being able to cluster (i.e. filter) heterogeneous context information based on context similarity can help to avoid under-utilization of resources. This thesis presents an accumulation of work which can be comprehended as a step towards realizing the vision of logical-clustering topology.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 101 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; No. 15-013
Keyword
Internet of Things, Context Information, Clustering, Distributed Computing
National Category
Computer Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-120237 (URN)
Presentation
2015-09-24, L30, Borgarfjordsgatan 12 (Nod Building), Campus Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, Grant Agreement No 318452
Available from: 2015-10-23 Created: 2015-09-03 Last updated: 2015-10-23Bibliographically approved

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