Skip to main content
SpringerLink
Log in

Dynamic Spectrum Access for Machine to Machine Communications: Opportunities, Standards, and Open Issues

  • Living reference work entry
  • First Online:
Handbook of Cognitive Radio

  • 157 Accesses

Abstract

Cognitive radio can be applied to a multitude of domains, one of which is M2M communication. Specifically, M2M communication refers to communication between devices without human intervention. Hence, devices should be able to organize themselves and run the communication protocol autonomously. If cognitive radio is used, tasks such as dynamic spectrum access (DSA), spectrum sensing, and alike present additional challenges compared to traditional network, as all the decision framework should be implemented and automatized in the devices. In this chapter, we focus on DSA techniques for M2M. The main difference from other kinds of communication is relative both to the energy efficiency and to the low protocol overhead, as devices should run for long periods of time and run without human intervention. At first we present related work from literature, categorizing the different tasks devices which want to leverage DSA on M2M have to perform. At the end of the chapter, we present a proof of concept of a general framework, which can be applied to different scenario concerning M2M, encompassing all the spectrum management and measurement tasks M2M devices should generally perform. Finally, we derive open challenges and future research directions concerning this scenario.

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

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. 3GPP. Study on provision of low-cost machine-type communications (MTC) user equipments (UEs) based on LTE. TSG GERAN R1

    Google Scholar 

  2. Altintas O et al (2011) Demonstration of vehicle to vehicle communications over TV white space. In: 2011 IEEE vehicular technology conference (VTC Fall), San Francisco, pp 1–3

    Google Scholar 

  3. Bedogni L, Trotta A, Di Felice M, Bononi L (2013) Machine-to-Machine communication over TV white spaces for smart metering applications. In: 2013 22nd international conference on computer communications and networks (ICCCN), Nassau, pp 1–7

    Google Scholar 

  4. Bedogni L, Achtzehn A, Petrova M, Mähönen P (2014) Smart meters with TV gray spaces connectivity: a feasibility study for two reference network topologies. In: 2014 eleventh annual IEEE international conference on sensing, communication, and networking (SECON), Singapore, pp 537–545

    Google Scholar 

  5. Bedogni L, Trotta A, Di Felice M (2015) On 3-dimensional spectrum sharing for TV white and gray space networks. In: 2015 IEEE 16th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), Boston, pp 1–8

    Google Scholar 

  6. Bedogni L, Franzoso F, Bononi L (2016) A self-adapting algorithm based on atmospheric pressure to localize indoor devices. In: 2016 IEEE global communications conference (GLOBECOM), Washington, DC, pp 1–6

    Google Scholar 

  7. Bedogni L, Trotta A, Di Felice M, Gao Y, Zhang X, Zhang Q, Malabocchia F, Bononi L (2017) Dynamic adaptive video streaming on heterogeneous TVWS and Wi-Fi networks. IEEE/ACM Trans Netw (IEEE TON) 25:3253–3266

    Article  Google Scholar 

  8. Bedogni L, Achtzehn A, Petrova M, Mähönen P, Bononi L (2017) Performance Assessment and Feasibility Analysis of IEEE 802.15.4m Wireless Sensor Networks in TV Grayspaces. ACM Trans Sensor Netw 13(1), Article 8 (January 2017), 27 pages

    Article  Google Scholar 

  9. Bedogni L, Malabocchia F, Di Felice M, Bononi L (2017) Indoor use of gray and white spaces: another look at wireless indoor communication. IEEE Veh Technol Mag 12(1):63–71

    Article  Google Scholar 

  10. Brew M, Darbari F, Crockett LH, Waddell MB, Fitch M, Weiss S, Stewart RW (2011) UHF white space network for rural smart grid communications. In: 2011 IEEE international conference on smart grid communications (SmartGridComm), Brussels, pp 138–142

    Google Scholar 

  11. Chang HB, Chen KC (2011) Cooperative spectrum sharing economy for heterogeneous wireless networks. In: 2011 IEEE GLOBECOM workshops (GC Wkshps), Houston, pp 458–463

    Google Scholar 

  12. Chatziantoniou E, Allen B, Velisavljevic V (2015) Threshold optimization for energy detection-based spectrum sensing over Hyper-Rayleigh fading channels. IEEE Commun Lett 19(6):1077–1080

    Article  Google Scholar 

  13. Condoluci M, Militano L, Orsino A, Alonso-Zarate J, Araniti G (2015) LTE-direct vs. WiFi-direct for machine-type communications over LTE-A systems. In: 2015 IEEE 26th annual international symposium on personal, indoor, and mobile radio communications (PIMRC), Hong Kong, pp 2298–2302

    Google Scholar 

  14. Cui P, Liu H, Rajan D, Camp J (2014) A measurement study of white spaces across diverse population densities. In: 2014 12th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt), number WiNMeE, May 2014. IEEE, pp 30–36

    Google Scholar 

  15. Datta SK, Bonnet C (2014) Smart M2M gateway based architecture for M2M device and endpoint management. In: 2014 IEEE international conference on internet of things (iThings), and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom), Taipei, pp 61–68

    Google Scholar 

  16. Di Felice M, Chowdhury KR, Bononi L (2010) Analyzing the potential of cooperative cognitive radio technology on inter-vehicle communication. In: Wireless days (WD), 2010 IFIP, Venice, pp 1–6

    Google Scholar 

  17. Di Felice M, Bedogni L, Bononi L (2012) DySCO: a dynamic spectrum and contention controlframework for enhanced broadcast communication invehicular networks. In: Proceedings of the 10th ACM international symposium on mobility management and wireless access (MobiWac’12)

    Google Scholar 

  18. Di Felice M, Bedogni L, Bononi L (2013) Group communication on highways: an evaluation study of Geocast protocols and applications. Elsevier’s Ad Hoc Netw J 11: 818–832

    Google Scholar 

  19. Ebrahimzadeh A, Najimi M, Andargoli SMH, Fallahi A (2015) Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing. IEEE Trans Veh Technol 64(4):1565–1577

    Article  Google Scholar 

  20. Fang YX, Xue G (2012) HERA: an optimal relay assignment scheme for cooperative networks, selected areas in communications. IEEE J 30(2):245–253

    Google Scholar 

  21. Federal Communications Commission. FCC online table of frequency allocations. Available online: https://transition.fcc.gov/oet/spectrum/table/fcctable.pdf

  22. Gao Y, Qin Z, Feng Z, Zhang Q, Holland O, Dohler M (2016) Scalable and reliable IoT enabled by dynamic spectrum management for M2M in LTE-A. IEEE Internet Things J 3(6): 1135–1145

    Article  Google Scholar 

  23. Gao L, Duan L, Huang J (2017) Two-sided matching based cooperative spectrum sharing. IEEE Trans Mob Comput 16(2):538–551

    Article  Google Scholar 

  24. Gu Y, Saad W, Bennis M, Debbah M, Han Z (2015) Matching theory for future wireless networks: fundamentals and applications. IEEE Commun Mag 53(5):52–59

    Article  Google Scholar 

  25. Harris J, Beach M, Nix A, Thomas P (2016) Spectrum sharing for M2M applications through Whitetime exploitation in WiFi networks. In: 2016 IEEE wireless communications and networking conference, Doha, pp 1–6

    Google Scholar 

  26. Hsu LK, Chou CT (2013) A dynamic spectrum access (DSA)-based multichannel protocol for large scale machine-to-machine (M2M) networks. In: 2013 9th international wireless communications and mobile computing conference (IWCMC), Sardinia, pp 1217–1222. https://doi.org/10.1109/IWCMC.2013.6583730

  27. ICT-DOST. TV white space deployment in Philippines. http://icto.dost.gov.ph/tv-white-space-deployment-in-ph-the-largest-in-asia/

  28. IEEE 802.15.4m standard. Available online: https://standards.ieee.org/findstds/standard/802.15.4m-2014.html

  29. Ihara Y et al (2013) Distributed autonomous multi-hop vehicle-to-vehicle communications over TV white space. In: 2013 IEEE consumer communications and networking conference (CCNC), Las Vegas, pp 336–344

    Google Scholar 

  30. Islam MH, Koh CL, Oh SW, Qing X, Lai YY, Wang C, Liang YC, Toh BE, Chin F, Tan L, Toh W (2008) Spectrum survey in Singapore: occupancy measurements and analyses. In: Proceedings of the 3rd international conference on cognitive radio oriented wireless networks and communications (CrownCom), May 2008. IEEE

    Google Scholar 

  31. Ji Z, Liu KJR (2007) Cognitive radios for dynamic spectrum access – dynamic spectrum sharing: a game theoretical overview. IEEE Commun Mag 45(5):88–94

    Article  Google Scholar 

  32. Kerttula J, Jäntti R (2011) DVB-T receiver performance measurements under secondary system interference. In: Proceedings of COCORA 2011

    Google Scholar 

  33. Laya A, Wang K, Widaa AA, Alonso-Zarate J, Markendahl J, Alonso L (2014) Device-to-device communications and small cells: enabling spectrum reuse for dense networks. IEEE Wirel Commun 21(4):98–105

    Article  Google Scholar 

  34. Lee J, Bagheri B, Kao H-A (2015) A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf Lett 3:18–23. ISSN:2213–8463

    Article  Google Scholar 

  35. Li TL, Chou CT, Hsu LK (2015) Proportional sharing in distributed dynamic spectrum access-based networks. IEEE Trans Mob Comput 14(1):155–168

    Google Scholar 

  36. Liang YC, Chen KC, Li GY, Mahonen P (2011) Cognitive radio networking and communications: an overview. IEEE Trans Veh Technol 60(7):3386–3407. https://doi.org/10.1109/TVT.2011.2158673

    Article  Google Scholar 

  37. Lim JH, Kim W, Naito K, Yun JH, Cabric D, Gerla M (2014) Interplay between TVWS and DSRC: optimal strategy for safety message dissemination in VANET. IEEE J Sel Areas Commun 32(11):2117–2133

    Article  Google Scholar 

  38. London ZOO TVWS animal monitoring. http://www.dailymail.co.uk/sciencetech/article-2788044/using-tv-whitespaces-save-endangered-animals-gaps-digital-frequencies-monitor-creatures-remote-areas.html

  39. Marcu I, Marghescu I (2010) Evaluation of spectrum occupancy in an urban environment in a cognitive radio context. Adv Telecommun 3(3):172–181

    Google Scholar 

  40. Masonta MMT, Johnson DL, Mzyece M (2011) The white space opportunity in Southern Africa: measurements with Meraka cognitive radio platform. In: Proceedings of AFRICOMM. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 92. Springer, pp 64–73

    Google Scholar 

  41. McHenry MA, Tenhula PA, McCloskey D, Roberson DA, Hood CS (2006) Chicago spectrum occupancy measurements & analysis and a long-term studies proposal. In: Proceedings of the first international workshop on technology and policy for accessing spectrum (TAPAS)

    Google Scholar 

  42. Montori F, Contigiani R, Bedogni L (2017) Is WiFi suitable for energy efficient IoT deployments? In: A performance studyon proceedings of the 3rd IEEE internal forum on research and technologies for society and industry, technologies for smarter societies (IEEE RTSI), 11–13 Sept, Modena

    Google Scholar 

  43. Niyato D, Hossain E (2008) Competitive spectrum sharing in cognitive radio networks: a dynamic game approach. IEEE Trans Wirel Commun 7(7):2651–2660

    Article  Google Scholar 

  44. Pérez-Romero J, Noguet D, López-Benítez M, Casadevall F (2011) Towards more-efficient spectrum usage: spectrum-sensing and cognitive-radio techniques. in URSI Radio Science Bulletin, 2011(336):59–74, March 2011

    Google Scholar 

  45. Qin Z, Gao Y, Plumbley MD, Parini CG, Cuthbert LG (2014) Efficient compressive spectrum sensing algorithm for M2M devices. In: 2014 IEEE global conference on signal and information processing (GlobalSIP), Atlanta, pp 1170–1174

    Google Scholar 

  46. Reed JH (2010) Enabling rural Virginia’s smart grid through white space communications, Virginia Tech, Technical report

    Google Scholar 

  47. Selen Y, Tullberg H, Kronander J (2008) Sensor Selection for Cooperative Spectrum Sensing. 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, Chicago, pp. 1–11

    Google Scholar 

  48. Southwell R, Chen X, Huang J (2014) Quality of service games for spectrum sharing. IEEE J Sel Areas Commun 32(3):589–600

    Article  Google Scholar 

  49. Sum CS, Harada H, Kojima F, Lan Z, Funada R (2011) Smart utility networks in tv white space. IEEE Commun Mag 49(7):132–139

    Article  Google Scholar 

  50. Taher TM, Bacchus RB, Zdunek KJ, Roberson DA (2011) Long-term spectral occupancy findings in Chicago. In: Proceedings of IEEE international symposium on dynamic spectrum access networks (DySPAN), pp 100–107, May 2011

    Google Scholar 

  51. Tracking and monitoring of animals with combined wireless technology and geofencing, US Patent Application US20130340305

    Google Scholar 

  52. Umar R, Sheikh AUH, Deriche M (2014) Unveiling the hidden assumptions of energy detector based spectrum sensing for cognitive radios. IEEE Commun Surv Tutor 16(2):713–728. Second Quarter 2014

    Article  Google Scholar 

  53. Varghese A, Tandur D (2014) Wireless requirements and challenges in industry 4.0. In: 2014 international conference on contemporary computing and informatics (IC3I), Mysore, pp 634–638

    Google Scholar 

  54. Wang J, Liu A, Yan T et al. (2018) Peer-to-Peer Netw Appl. 11:(679)

    Google Scholar 

  55. Wellens M, Wu J, Mähönen P (2007) Evaluation of spectrum occupancy in indoor and outdoor scenario in the context of cognitive radio. In: 2007 2nd international conference on cognitive radio oriented wireless networks and communications (CROWNCOM), Aug 2007. IEEE, pp 420–427

    Google Scholar 

  56. Yin S, Chen D, Zhang Q, Liu M, Li S (2012) Mining spectrum usage data: a large-scale spectrum measurement study. IEEE Trans Mob Comput 11(6):1033–1046

    Google Scholar 

  57. Ying X, Zhang J, Yan L, Zhang G, Chen M, Chandra R (2013) Exploring indoor white spaces in metropolises. In: Proceedings of the 19th annual international conference on Mobile computing & networking – MobiCom’13, New York. ACM Press, p 255

    Google Scholar 

  58. Yuan Ma, Gao Y, Parini CG (2015) Sub-Nyquist rate wideband spectrum sensing over TV white space for M2M communications. In: 2015 IEEE 16th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), Boston, pp 1–6

    Google Scholar 

  59. Zhang Y, Yu R, Nekovee M, Liu Y, Xie S, Gjessing S (2012) Cognitive machine-to-machine communications: visions and potentials for the smart grid. IEEE Netw 26(3):6–13

    Article  Google Scholar 

  60. Zhou H et al (2015) Enabling efficient and wide-coverage vehicular content distribution over TV white spaces. In: 2015 international conference on wireless communications & signal processing (WCSP), Nanjing, pp 1–6

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, 40126, Mura Anteo Zamboni 7, Bologna, Italy

    Luca Bedogni, Marco Di Felice & Luciano Bononi

Authors
  1. Luca Bedogni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Di Felice
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luciano Bononi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Di Felice .

Editor information

Editors and Affiliations

  1. Electrical Engg Bldg, Rm 325, The University of New South Wales, Sydney, New South Wales, Australia

    Wei Zhang

Section Editor information

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, 327 Mile End Road, E1 4NS, London, UK

    Yue Gao

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Bedogni, L., Di Felice, M., Bononi, L. (2018). Dynamic Spectrum Access for Machine to Machine Communications: Opportunities, Standards, and Open Issues. In: Zhang, W. (eds) Handbook of Cognitive Radio . Springer, Singapore. https://doi.org/10.1007/978-981-10-1389-8_57-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1389-8_57-1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1389-8

  • Online ISBN: 978-981-10-1389-8

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation