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Cloud-Based Context-Aware Spectrum Availability Monitoring and Prediction Using Crowd-Sensing

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Cognitive Radio, Mobile Communications and Wireless Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

The scarcity of radio-frequency bands, due to its fixed allocation, is an emerging problem in wireless communications. Cognitive radio (CR) is a new paradigm which suggests reusing the frequency bands for unlicensed user at the time of licensed users’ inactivity. Therefore, unlicensed users must perform spectrum sensing to find the available spectrum opportunities. Cooperative spectrum sensing (CSS) is a method where unlicensed users individually sense and upload their sensing data to a fusion center. Moreover, crowd-sensing methods could be used by mobile users to provide more sensing data from various locations for the sake of improving the achieved decisions on spectrum status. Providing spectrum data from various sources makes the spectrum monitoring and management more complex. This chapter proposes a novel mechanism that (1) uses cloud computing technology as a well-suited platform for storing, processing such big data, and providing monitoring service in order to be used by, e.g., CR nodes; (2) considers the impact of contextual parameters such as location, time, building complexity around the user, etc. on the spectrum availability decision; and (3) takes the advantages of machine learning techniques to predict the future behavior of spectrum opportunities.

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Acknowledgments

We would thank like to Mobile Telecommunication Company of Iran – Isfahan Branch – for their support that assisted data collection for this research.

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Authors and Affiliations

  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Hussein Shirvani & Behrouz Shahgholi Ghahfarokhi

Authors
  1. Hussein Shirvani
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  2. Behrouz Shahgholi Ghahfarokhi
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Correspondence to Hussein Shirvani .

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Editors and Affiliations

  1. Telecommunications Software and Systems Group, Waterford Institute of Technology, Waterford, Ireland

    Mubashir Husain Rehmani

  2. National Polytechnic Institute of Toulouse, Toulouse, France

    Riadh Dhaou

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Shirvani, H., Shahgholi Ghahfarokhi, B. (2019). Cloud-Based Context-Aware Spectrum Availability Monitoring and Prediction Using Crowd-Sensing. In: Rehmani, M., Dhaou, R. (eds) Cognitive Radio, Mobile Communications and Wireless Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-91002-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-91002-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91001-7

  • Online ISBN: 978-3-319-91002-4

  • eBook Packages: EngineeringEngineering (R0)

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