Redundancy reduction in wireless sensor networks via centrality metrics

D.C. Mocanu, M. Torres Vega, A. Liotta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
267 Downloads (Pure)

Abstract

The advances in wireless communications, together with the need of sensing and controlling various nature or human made systems in a large number of points (e.g. smart traffic control, environmental monitoring), lead to the emergence of Wireless Sensor Networks (WSN) as a powerful tool to fulfill the above requirements. Due to the large amount of wireless devices needed and cost constraints, such networks are usually made by low-cost devices with limited energy and computational capabilities, these further on being subject to easy communication or hardware fails. At the same time, the deployment of such devices in harsh environments (e.g. in the ocean) may lead to uncontrollable redundant topologies which have to be often refined during the exploitation phase of these networks in an automated manner. In the scope of these arguments, in this paper, we take advantage of the latest theoretical advances in complex networks and we propose an automated solution to refine the topology of WSNs by using centrality metrics to detect the redundant nodes and links in a network, and further on to shut down them safely. Our solution may work in both ways, centralized or decentralized, by choosing a centralized or a decentralized centrality metric, this choice being driven by the application goal. The experiments performed on a wide variety of network topologies with different sizes (e.g. number of nodes and links), using different centrality metrics, validate our approach and recommend it as a solution for the automatic control of WSNs topologies during the exploitation phase of such networks to optimize, for instance, their life time.
Original languageEnglish
Title of host publicationProceedings 15th IEEE International Conference on Data Mining Workshop : 14-17 November 2015, Atlantic City, New Jersey
EditorsP. Cui, C. Aggarwal, Z.-H. Zhou, A. Tuzhilin, H. Xiong, X. Wu
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages501-507
ISBN (Print)978-1-4673-8492-6
DOIs
Publication statusPublished - 2015
Event5th IEEE ICDM International Workshop on Data Mining in Networks (DaMNet 2015), November 14, 2015, Atlantic City, NJ, USA - Atlantic City, NJ, United States
Duration: 14 Nov 201514 Nov 2015
http://damnet.reading.ac.uk/DaMNet2015/

Workshop

Workshop5th IEEE ICDM International Workshop on Data Mining in Networks (DaMNet 2015), November 14, 2015, Atlantic City, NJ, USA
Abbreviated titleDaMNet 2015
CountryUnited States
CityAtlantic City, NJ
Period14/11/1514/11/15
OtherWorkshop held at the 15th IEEE International Conference on Data Mining (ICDM 2015)
Internet address

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