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Self-Organizing Logical-Clustering Topology for Managing Distributed Context Information
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. (Immersive Networking)
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 [en]
Internet of Things, Context Information, Clustering, Distributed Computing
National Category
Computer Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-120237OAI: oai:DiVA.org:su-120237DiVA: diva2:851006
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
List of papers
1. On Performance of Logical-Clustering of Flow-Sensors
Open this publication in new window or tab >>On Performance of Logical-Clustering of Flow-Sensors
2013 (English)In: International Journal of Computer Science Issues, ISSN 1694-0784, E-ISSN 1694-0814, Vol. 10, no 5, 1-13 p.Article in journal (Refereed) Published
Abstract [en]

In state-of-the-art Pervasive Computing, it is envisioned that unlimited access to information will be facilitated for anyone and anything. Wireless sensor networks will play a pivotal role in the stated vision. This reflects the phenomena where any situation can be sensed and analyzed anywhere. It makes heterogeneous context ubiquitous. Clustering context is one of the techniques to manage ubiquitous context information efficiently to maximize its potential. Logical-clustering is useful to share real-time context where sensors are physically distributed but logically clustered. This paper investigates the network performance of logical-clustering based on ns-3 simulations. In particular reliability, scalability, and reachability in terms of delay, jitter, and packet loss for the logically clustered network have been investigated. The performance study shows that jitter demonstrates 40 % and 44 % fluctuation for 200 % increase in the node per cluster and 100 % increase in the cluster size respectively. Packet loss exhibits only 18 % increase for 83 % increase in the packet flow-rate.

Keyword
Pervasive Computing, Wireless sensor networks, ubiquitous, context, distributed, logical-clustering, ns-3
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-96364 (URN)
Available from: 2013-11-20 Created: 2013-11-20 Last updated: 2017-12-14Bibliographically approved
2. Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
Open this publication in new window or tab >>Context‐Based Logical Clustering of Flow‐Sensors ‐ Exploiting HyperFlow and Hierarchical DHTs
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.

Keyword
Sensor networks, ubiquitous computing, heterogeneous contexts, logical clustering
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-95584 (URN)10.4156/rnis.vol14.131 (DOI)
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2017-12-04Bibliographically approved
3. Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense
Open this publication in new window or tab >>Enabling Scalable Publish/Subscribe for Logical-Clustering in Crowdsourcing via MediaSense
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
Keyword
crowdsourcing, pervasive computing, context information, logical-clustering, Publish/Subscribe, MediaSense
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-110981 (URN)10.1109/SAI.2014.6918173 (DOI)978-0-9893193-1-7 (ISBN)
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
4. Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing
Open this publication in new window or tab >>Realising Dynamism in MediaSense Publish/Subscribe Model for Logical-Clustering in Crowdsourcing
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
Keyword
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:nbn:se:su:diva-111093 (URN)10.14569/IJARAI.2014.031106 (DOI)
Available from: 2014-12-22 Created: 2014-12-22 Last updated: 2015-10-23Bibliographically approved

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