Redundancy Reduction in Wireless Sensor Networks via Centrality Metrics

Decebal Constantin Mocanu, Maria Torres Vega, Antonio Liotta

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

9 Citations (Scopus)


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, ICDMW 2015
EditorsXindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
Place of PublicationPiscataway, NJ
Number of pages7
ISBN (Electronic)978-1-4673-8493-3
ISBN (Print)978-1-4673-8492-6
Publication statusPublished - 29 Jan 2016
Externally publishedYes
Event15th IEEE International Conference on Data Mining Workshops, ICDMW 2015 - Atlantic City, United States
Duration: 14 Nov 201517 Nov 2015
Conference number: 15

Publication series

NameProceedings - IEEE International Conference on Data Mining Workshop (ICDMW)
ISSN (Electronic)2375-9259


Conference15th IEEE International Conference on Data Mining Workshops, ICDMW 2015
Abbreviated titleICDMW
Country/TerritoryUnited States
CityAtlantic City


  • Centrality metrics
  • Complex networks
  • Redundancy reduction
  • Wireless Sensor Networks (WSN)


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