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Efficient Clustering Schemes Towards Information Collection

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Cognitive Radio-Oriented Wireless Networks (CrownCom 2020)

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Abstract

One of the challenges in cooperative spectrum sensing is to optimize the energy consumption of the network. Delivery of all measurements from all the nodes to the fusion centre is not the best solution from the perspective of energy-efficiency. Clustering of nodes with similar channel conditions may reduce the amount of transmitted data, and in consequence reduce the amount of consumed energy. In this paper we investigate the performance of selected algorithms known in the domain of artificial intelligence, applied to perform reliable yet energy-aware spectrum sensing.

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Acknowledgement

The presented work has been funded by the National Science Centre in Poland within the CERTAIN project (no. 2017/27/L/ST7/03166) of the DAINA programme.

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Authors and Affiliations

  1. Institute of Radiocommunications, Poznan University of Technology, Poznan, Poland

    Krzysztof Cichoń & Adrian Kliks

Authors
  1. Krzysztof Cichoń
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  2. Adrian Kliks
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Correspondence to Krzysztof Cichoń .

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Editors and Affiliations

  1. Department of Mobile Systems and Analytics (MOSAIC), Simula Metropolitan Center for Digital Engineering (SimulaMet), Oslo, Norway

    Giuseppe Caso

  2. DIET Department, Sapienza University of Rome, Rome, Italy

    Luca De Nardis

  3. Faculty of Electrical Engineering and Information Technology, Ss. Cyril and Methodius University, Skopje, North Macedonia

    Liljana Gavrilovska

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Cichoń, K., Kliks, A. (2021). Efficient Clustering Schemes Towards Information Collection. In: Caso, G., De Nardis, L., Gavrilovska, L. (eds) Cognitive Radio-Oriented Wireless Networks. CrownCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 374. Springer, Cham. https://doi.org/10.1007/978-3-030-73423-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-73423-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73422-0

  • Online ISBN: 978-3-030-73423-7

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