IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i9p232-d631077.html
   My bibliography  Save this article

Spectrum Demand Forecasting for IoT Services

Author

Listed:
  • Daniel Jaramillo-Ramirez

    (Electronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia
    These authors contributed equally to this work.)

  • Manuel Perez

    (Electronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia
    These authors contributed equally to this work.)

Abstract

The evolution of IoT has come with the challenge of connecting not only a massive number of devices, but also providing an always wider variety of services. In the next few years, a big increase in the number of connected devices is expected, together with an important increase in the amount of traffic generated. Never before have wireless communications permeated so deeply in all industries and economic sectors. Therefore, it is crucial to correctly forecast the spectrum needs, which bands should be used for which services, and the economic potential of its utilization. This paper proposes a methodology for spectrum forecasting consisting of two phases: a market study and a spectrum forecasting model. The market study determines the main drivers of the IoT industry for any country: services, technologies, frequency bands, and the number of devices that will require IoT connectivity. The forecasting model takes the market study as the input and calculates the spectrum demand in 5 steps: Defining scenarios for spectrum contention, calculating the offered traffic load, calculating a capacity for some QoS requirements, finding the spectrum required, and adjusting according to key spectral efficiency determinants. This methodology is applied for Colombia’s IoT spectrum forecast. We provide a complete step-by-step implementation in fourteen independent spectrum contention scenarios, calculating offered traffic, required capacity, and spectrum for cellular licensed bands and non-cellular unlicensed bands in a 10-year period. Detailed results are presented specifying coverage area requirements per economic sector, frequency band, and service. The need for higher teledensity and higher spectral efficiency turns out to be a determining factor for spectrum savings.

Suggested Citation

  • Daniel Jaramillo-Ramirez & Manuel Perez, 2021. "Spectrum Demand Forecasting for IoT Services," Future Internet, MDPI, vol. 13(9), pages 1-24, September.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:232-:d:631077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/9/232/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/13/9/232/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Juan M. Gallego & Luis H. Gutiérrez & Sang H. Lee, 2015. "A firm-level analysis of ICT adoption in an emerging economy: evidence from the Colombian manufacturing industries," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 24(1), pages 191-221.
    2. Alan Cobham, 1954. "Priority Assignment in Waiting Line Problems," Operations Research, INFORMS, vol. 2(1), pages 70-76, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Divya Velayudhan Nair & Achyutha Krishnamoorthy & Agassi Melikov & Sevinj Aliyeva, 2021. "MMAP/(PH,PH)/1 Queue with Priority Loss through Feedback," Mathematics, MDPI, vol. 9(15), pages 1-26, July.
    2. Thomas Kittsteiner & Benny Moldovanu, 2005. "Priority Auctions and Queue Disciplines That Depend on Processing Time," Management Science, INFORMS, vol. 51(2), pages 236-248, February.
    3. Andrei Sleptchenko & M. Eric Johnson, 2015. "Maintaining Secure and Reliable Distributed Control Systems," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 103-117, February.
    4. Delera, Michele & Pietrobelli, Carlo & Calza, Elisa & Lavopa, Alejandro, 2022. "Does value chain participation facilitate the adoption of Industry 4.0 technologies in developing countries?," World Development, Elsevier, vol. 152(C).
    5. Huang, Jiashun & Li, Weiping & Guo, Lijia & Hall, Jim W., 2022. "Information and communications technology infrastructure and firm growth: An empirical study of China's cities," Telecommunications Policy, Elsevier, vol. 46(3).
    6. Aboal D. & Tacsir E., 2015. "Innovation and productivity in services and manufacturing : The role of ICT investment," MERIT Working Papers 2015-012, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Orozco, Luis Antonio & Sanabria, John Alirio & Sosa, Juan Camilo & Aristizabal, Jeimy & López, Liliana, 2022. "How do IT investments interact with other resources to improve innovation?," Journal of Business Research, Elsevier, vol. 144(C), pages 358-365.
    8. Adel Ben Khalifa, 2018. "Government Supports and Technology Adoption: Evidence from Tunisia," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(4), pages 41-70, December.
    9. Josef Zuk & David Kirszenblat, 2024. "Explicit results for the distributions of queue lengths for a non-preemptive two-level priority queue," Annals of Operations Research, Springer, vol. 341(2), pages 1223-1246, October.
    10. Mor Harchol-Balter & Takayuki Osogami & Alan Scheller-Wolf & Adam Wierman, 2005. "Multi-Server Queueing Systems with Multiple Priority Classes," Queueing Systems: Theory and Applications, Springer, vol. 51(3), pages 331-360, December.
    11. Hideaki Takagi, 2016. "Waiting time in the M/M/ $$ m $$ m LCFS nonpreemptive priority queue with impatient customers," Annals of Operations Research, Springer, vol. 247(1), pages 257-289, December.
    12. Bakry, Walid & Nghiem, Xuan-Hoa & Farouk, Sherine & Vo, Xuan Vinh, 2023. "Does it hurt or help? Revisiting the effects of ICT on economic growth and energy consumption: A nonlinear panel ARDL approach," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 597-617.
    13. Tob-Ogu, Abiye & Kumar, Niraj & Cullen, John, 2018. "ICT adoption in road freight transport in Nigeria – A case study of the petroleum downstream sector," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 240-252.
    14. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    15. G. M. Koole & B. F. Nielsen & T. B. Nielsen, 2012. "First in Line Waiting Times as a Tool for Analysing Queueing Systems," Operations Research, INFORMS, vol. 60(5), pages 1258-1266, October.
    16. Ariel Herbert FAMBEU, 2016. "Déterminants De L’Adoption Des Tic Dans Un Pays En Développement : Une Analyse Économétrique Sur Les Entreprises Industrielles Au Cameroun," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 43, pages 159-186.
    17. van Vianen, L.A. & Gabor, A.F. & van Ommeren, J.C.W., 2014. "A simple derivation of the waiting time distributions in a non-preemptive M/M/c queue with priorities," ERIM Report Series Research in Management ERS-2014-016-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    18. Nilay Tan{i}k Argon & Serhan Ziya, 2009. "Priority Assignment Under Imperfect Information on Customer Type Identities," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 674-693, June.
    19. BEN KHALIFA, Adel, 2017. "Territoire Appropriant Et Economie De La Connaissance Basee Sur Les Tic : Une Approche Eclectique [Appropriating Territory and ICT-Based Knowledge economy: An Eclectic Approach]," MPRA Paper 77535, University Library of Munich, Germany.
    20. Haviv, Moshe & van der Wal, Jan, 2008. "Mean sojourn times for phase-type discriminatory processor sharing systems," European Journal of Operational Research, Elsevier, vol. 189(2), pages 375-386, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:232-:d:631077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.