Skip to main content
SpringerLink
Log in

A Big Data architecture for spectrum monitoring in cognitive radio applications

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

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].

References

  1. Akyildiz IF, Lee WY, Vuran MC, Mohanty S (2008) A survey on spectrum management in cognitive radio networks. IEEE Commun Mag 46(4):40–48

    Article  Google Scholar 

  2. Wang B, Liu KJR (2011) Advances in cognitive radio networks: a survey. IEEE J Sel Top Sign Proces 5 (1):5–23

    Article  Google Scholar 

  3. Yucek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutor 11(1):116–130

    Article  Google Scholar 

  4. Flores AB et al (2013) IEEE 802.11af: a standard for TV white space spectrum sharing. IEEE Commun Mag 51(10):92–100

    Article  Google Scholar 

  5. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  6. Perera C, et al. (2014) Context aware computing for the Internet of Things: a survey. IEEE Commun Surv Tutor 16(1):414–454

    Article  Google Scholar 

  7. Miorandi D et al (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10 (7):1497–1516

    Article  Google Scholar 

  8. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Sensing as a service model for smart cities supported by Internet of Things. Trans Emerg Telecommun Technol 25(1):81–93

    Article  Google Scholar 

  9. De Mauro A, Greco M, Grimaldi M (2016) A formal definition of Big Data based on its essential features. Library Review

  10. Zaslavsky A, Perera C, Georgakopoulos D (2013) Sensing as a service and big data. arXiv:1301.0159

  11. Mell P, Grance T (2011) The NIST definition of cloud computing

  12. Sheng X, Tang J, Xiao X, Xue G (2013) Sensing as a service: challenges, solutions and future directions. IEEE Sens J 13(10):3733–3741

    Article  Google Scholar 

  13. Zaslavsky A et al (2012) Sensing-as-a-Service and Big Data. In: Proceedings of the international conference on advances in cloud computing (ACC), Bangalore

  14. Ghasemi A, Sousa ES (2008) Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs. IEEE Commun Mag 46(4):32–39

    Article  Google Scholar 

  15. Apache: Flink. https://flink.apache.org. Accessed: 11 Apr 2017

  16. Carbone P, Katsifodimos A, Ewen S, Markl V, Haridi S, Tzoumas K (2015) Apache Flink: stream and batch processing in a single engine. Bull IEEE Comput Soc Tech Comm Data Eng 38(4):28–38

    Google Scholar 

  17. MongoDB: Mongodb. https://www.mongodb.com. Accessed: 11 Apr 2017

  18. Győrödi C, Győrödi R, Pecherle G, Olah A (2015) A comparative study: MongoDB vs. MySQL. In: 13th international conference on engineering of modern electric systems (EMES). IEEE, pp 1–6

  19. Apache: Kafka. http://kafka.apache.org. Accessed: 11 Apr 2017

  20. Ranjan R (2014) Streaming big data processing in datacenter clouds. IEEE Cloud Comput 1(1):78–83

    Article  Google Scholar 

  21. Blefari-Melazzi N, Sorte DD, Femminella M, Reali G (2007) Autonomic control and personalization of a wireless access network. Comput Netw 51(10):2645–2676

    Article  MATH  Google Scholar 

  22. Baruffa G, Femminella M, Pergolesi M, Reali G (2016) A cloud computing architecture for spectrum sensing as a service. In: Cloudification of the Internet of Things (CIoT), pp 1–5

  23. Sun H, Nallanathan A, Wang CX, Chen Y (2013) Wideband spectrum sensing for cognitive radio networks: a survey. IEEE Wirel Commun 20(2):74–81

    Article  Google Scholar 

  24. Li Z, Yu FR, Huang M (2010) A distributed consensus-based cooperative spectrum-sensing scheme in cognitive radios. IEEE Trans Veh Technol 59(1):383–393

    Article  Google Scholar 

  25. Kotobi K et al (2015) Data-throughput enhancement using data mining-informed cognitive radio. Electronics 4(2):221

    Article  Google Scholar 

  26. Zhang T et al (2015) A wireless spectrum analyzer in your pocket. In: Proceedings of HotMobile ’15. HotMobile ’15. ACM, New York, pp 69–74

  27. Chakraborty A, Das SR (2016) Designing a cloud-based infrastructure for spectrum sensing: a case study for indoor spaces. In: IEEE DCOSS 2016. Washington DC, pp 17–24

  28. Ulversoy T (2010) Software defined radio: challenges and opportunities. IEEE Commun Surv Tutor 12 (4):531–550

    Article  Google Scholar 

  29. Open IoT Consortium: open IoT. http://openiot.eu. Accessed: 11 Apr 2017

  30. Amazon: Amazon AWS. http://aws.amazon.com/. Accessed: 11 Apr 2017

  31. Google: Google Cloud. https://cloud.google.com/compute. Accessed: 11 Apr 2017

  32. Openstack: Openstack. https://www.openstack.org/. Accessed: 15 Dec 2017

  33. Apache: Avro. http://avro.apache.org/. Accessed: 11 Apr 2017

  34. Maeda K (2012) Performance evaluation of object serialization libraries in XML, JSON and binary formats. In: 2012 second international conference on digital information and communication technology and its applications (DICTAP). IEEE, pp 177–182

  35. Popa L et al (2012) Faircloud: sharing the network in cloud computing. In: ACM SIGCOMM 2012. ACM, pp 187–198

  36. Ousterhout K et al (2015) Making sense of performance in data analytics frameworks. In: USENIX NSDI’15. Oakland

  37. Chakraborty A, Gupta U, Das SR (2016) Benchmarking resource usage for spectrum sensing on commodity mobile devices. In: ACM HotWireless, New York

  38. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51 (1):107–113

    Article  Google Scholar 

  39. The Java Tutorials. Oracle: using prepared statements. http://docs.oracle.com/javase/tutorial/jdbc/basics/prepared.html. Accessed: 12 Apr 2017

  40. MongoDB: MongoDB connector for Hadoop. https://github.com/mongodb/mongo-hadoop. Accessed: 11 Apr 2017

Download references

Funding

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

Author information

Authors and Affiliations

  1. Department of Engineering, University of Perugia, via G. Duranti 93, 06125, Perugia, Italy

    Giuseppe Baruffa, Mauro Femminella, Matteo Pergolesi & Gianluca Reali

Authors
  1. Giuseppe Baruffa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauro Femminella
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matteo Pergolesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gianluca Reali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mauro Femminella.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation