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Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense
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: Proceedings of 2014 Science and Information Conference, IEEE Computer Society, 2014, 64-71 p.Conference paper, Published paper (Refereed)
Abstract [en]

Crowdsourcing was initially devised as a method for solving problems through soliciting contributions from a large online community. Crowdsourcing is facing new challenges to handle the increase of information in real-time from a vast number of sources in Internet-of-Things (IoT) scenarios. Thus we seek to leverage the power of social web, smart-devices, sensors, etc., fusing these heterogeneous sources into distributed context information in order to enable novel crowdsourcing scenarios. This mandates research in efficient management of heterogeneous and distributed context information through logical-clustering. Logical-clustering can efficiently filter out similar context information obtained from distributed sources based on context similarity. However, the efficiency of logical-clustering is challenged by the distribution of context information in crowdsourcing scenarios. Publish/Subscribe mechanism can counter this challenge. To this end, we propose a scalable publish/subscribe model, MediaSense, which is based on p2p technologies. This paper presents our approach to a scalable logical-clustering concept. The evaluation of our approach applied to MediaSense can achieve a rate of approximately 3530 messages/sec for publish/subscribe events. Moreover, this approach further achieves 99% increase for subscription matching and 163% improvement in memory requirements in comparison with other approaches.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 64-71 p.
Keyword [en]
crowdsourcing, pervasive computing, context information, logical-clustering, Publish/Subscribe, MediaSense
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-110981DOI: 10.1109/SAI.2014.6918173ISBN: 978-0-9893193-1-7 (print)OAI: oai:DiVA.org:su-110981DiVA: diva2:773755
Conference
Science and Information Conference 2014, London, UK, August 27-29, 2014
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2017-12-14Bibliographically 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
2.
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