Redundancy Reduction in Wireless Sensor Networks via Centrality Metrics

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.

DOI: 10.1109/ICDMW.2015.53

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Cite this paper

@article{Mocanu2015RedundancyRI, title={Redundancy Reduction in Wireless Sensor Networks via Centrality Metrics}, author={Decebal Constantin Mocanu and Maria Torres Vega and Antonio Liotta}, journal={2015 IEEE International Conference on Data Mining Workshop (ICDMW)}, year={2015}, pages={501-507} }