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Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
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.
2013 (English)In: RNIS: Research Notes in Information and Service Sciences, ISSN 2287-1934, Vol. 14, 721-728 p.Article in journal (Refereed) Published
Abstract [en]

In the state-of-the-art sensor networks are becoming an integral part of ubiquitous computing. Context information is ubiquitous due to the deployment of sensors in Internet infrastructure and availability to services. This corresponds to the phenomena where any situation can be sensed and analyzed anywhere. Services can access heterogeneous context information anywhere through the distributed acquisition and dissemination of sensor data assembled from physical objects. A novel idea of clustering sensors based on context similarity is presented in this paper. The sensors are physically distributed but logically clustered based on similar context. This will enable resources (data, services) to be shared. The network is a two-tier hierarchical distributed hash tables (DHTs) system based on the HyperFlow platform. The approach provides topological sensor networks with scalability, robustness, mobility, heterogeneity support, adaptability to different contexts, etc. A performance study demonstrates feasibility and scalability, adaptability, heterogeneity, and robustness of the proposed approach.

Place, publisher, year, edition, pages
2013. Vol. 14, 721-728 p.
Keyword [en]
Sensor networks, ubiquitous computing, heterogeneous contexts, logical clustering
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-95584DOI: 10.4156/rnis.vol14.131OAI: oai:DiVA.org:su-95584DiVA: diva2:660892
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2017-12-04Bibliographically 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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