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A Big Data architecture for spectrum monitoring in cognitive radio applications

Abstract

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next-generation wireless networks. A crucial requirement for future cognitive radio networks is the wideband spectrum sensing, which allows detecting spectral opportunities across a wide frequency range. On the other hand, the Internet of Things concept has revolutionized the usage of sensors and of the relevant data. Connecting sensors to cloud computing infrastructure enables the so-called paradigm of Sensing as a Service (S2aaS). In this paper, we present an S2aaS architecture to offer the Spectrum Sensing as a Service (S3aaS), by exploiting the flexibility of software-defined radio. We believe that S3aaS is a crucial step to simplify the implementation of spectrum sensing in cognitive radio. We illustrate the system components for the S3aaS, highlighting the system design choices, especially for the management and processing of the large amount of data coming from the spectrum sensors. We analyze the connectivity requirements between the sensors and the processing platform, and evaluate the trade-offs between required bandwidth and target service delay. Finally, we show the implementation of a proof-of-concept prototype, used for assessing the effectiveness of the whole system in operation with respect to a legacy processing architecture.

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Notes

  1. SDR can be considered among the enabling technologies that allow dynamic reconfiguration and quick adaptation to the offered communication opportunities, since physical layer (PHY) processing is carried out by general purpose processors in software, and they can be reconfigured by software in real time and continuously [28].

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Funding

This work is financially supported by CLOUD and HYDRA, two research projects funded by the University of Perugia.

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Correspondence to Mauro Femminella.

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Baruffa, G., Femminella, M., Pergolesi, M. et al. A Big Data architecture for spectrum monitoring in cognitive radio applications. Ann. Telecommun. 73, 451–461 (2018). https://doi.org/10.1007/s12243-018-0642-7

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  • DOI: https://doi.org/10.1007/s12243-018-0642-7

Keywords

  • Spectrum sensing
  • Big Data
  • NoSQL
  • MapReduce
  • Performance evaluation