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Towards 802.11g Signal Strength Estimation in an Industrial Environment: A Practical Study

  • Dalton Cézane Gomes ValadaresEmail author
  • Joseana Macêdo Fechine Régis de Araújo
  • Ângelo Perkusich
  • Marco Aurélio Spohn
  • Elmar Uwe Kurt Melcher
  • Natália Porfírio Albuquerque
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)

Abstract

With Industry 4.0 and Industrial Internet of Things (IIoT), new communication protocols are emerging or being updated. These protocols demand technological updates at industries, mainly related to their network infrastructures, and generally leading to large expenditures. Given that the 802.11g standard is a largely used communication protocol, mostly in residential and commercial applications, many industries also adopt such standard mainly due to its low deployment and maintenance cost. In this scenario, there is a need to evaluate signal quality to better design the network infrastructure in order to obtain good communication coverage. In this work, we present a practical study about the 802.11g signal strength in a thermoelectric power plant. We have compared our measured values with the estimated ones through the Log-Distance Path Loss model. We concluded that it is possible to use this model in an industrial environment to estimate signal strength with a low error by choosing the right propagation (path loss) exponent.

Notes

Acknowledgement

The authors thank Borborema Energética S.A. and Maracanaú Geradora de Energia S.A., sponsors of the ANEEL GASIS R&D project, in which this research was inserted, as well as CNPq, for having financed some months of the master’s research of the main author.

References

  1. Ali, A.H., Razak, M.R.A., Hidayab, M., Azman, S.A., Jasmin, M.Z.M., Zainol, M.A.: Investigation of indoor WiFi radio signal propagation. In: 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA), pp. 117–119 (2010).  https://doi.org/10.1109/ISIEA.2010.5679486
  2. Chebil, J., Lwas, A., Islam, M.: Comparison between measured and predicted path loss for mobile communication in Malaysia. World Appl. Sci. J. (Math. Appl. Eng.) 21, 123–128 (2013).  https://doi.org/10.5829/idosi.wasj.2013.21.mae.99936CrossRefGoogle Scholar
  3. Cheffena, M., Mohamed, M.: Empirical path loss models for wireless sensor network deployment in snowy environments. IEEE Antennas Wirel. Propag. Lett. 16, 2877–2880 (2017).  https://doi.org/10.1109/LAWP.2017.2751079CrossRefGoogle Scholar
  4. Community L: iwconfig(8) - linux man page (2018). https://linux.die.net/man/8/iwconfig
  5. Damsaz, M., Guo, D., Peil, J., Stark, W., Moayeri, N., Candell, R.: Channel modeling and performance of Zigbee radios in an industrial environment. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1–10 (2017).  https://doi.org/10.1109/WFCS.2017.7991975
  6. Faria, D.B.: Modeling signal attenuation in IEEE 802.11 wireless LANs (2005)Google Scholar
  7. Fernández, J., Quispe, M., Kemper, G., Samaniego, J., Díaz, D.: An improvement of the log-distance path loss model for digital television in Lima. In: XXX Simpósio Brasileiro de Telecomunições (2012)Google Scholar
  8. Intelbras: WOG 212 CPE 2.4 GHz 12 DBI (2018). http://en.intelbras.com.br/business/outdoor-radios/cpe/wog-212
  9. Japertas, S., Orzekauskas, E., Slanys, R.: Research of IEEE 802.11 standard signal propagation features in multi partition indoors. In: 2012 Second International Conference on Digital Information Processing and Communications (ICDIPC), pp. 1–4 (2012).  https://doi.org/10.1109/ICDIPC.2012.6257267
  10. Karaagac, A., Haxhibeqiri, J., Joseph, W., Moerman, I., Hoebeke, J.: Wireless industrial communication for connected shuttle systems in warehouses. In: 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS), pp. 1–4 (2017).  https://doi.org/10.1109/WFCS.2017.7991971
  11. Lkhagvatseren, T., Hruska, F.: Path loss aspects of a wireless communication system for sensors. Int. J. Comput. Commun. 5 (2011)Google Scholar
  12. Ndzi, D., Arif, M., Shakaff, A., Ahmad, M., Harun, A., Kamarudin, L., Zakaria, A., Ramli, M., Razalli, M.: Signal propagation analysis for low data rate wireless sensor network applications in sport grounds and on roads. Prog. Electromagn. Res. 125, 1–19 (2012).  https://doi.org/10.2528/PIER11111406CrossRefGoogle Scholar
  13. Rath, H.K., Timmadasari, S., Panigrahi, B., Simha, A.: Realistic indoor path loss modeling for regular WiFi operations in india. In: 2017 Twenty-third National Conference on Communications (NCC), pp. 1–6 (2017).  https://doi.org/10.1109/NCC.2017.8077107
  14. Rubio, L., Fernández, H., Rodrigo-Peñarrocha, V.M., Reig, J.: Path loss characterization for vehicular-to-infrastructure communications at 700 MHz and 5.9 GHz in urban environments. In: 2015 IEEE International Symposium on Antennas and Propagation USNC/URSI National Radio Science Meeting, pp. 93–94 (2015).  https://doi.org/10.1109/APS.2015.7304432
  15. Srinivasa, S., Haenggi, M.: Path loss exponent estimation in large wireless networks. In: 2009 Information Theory and Applications Workshop, pp. 124–129 (2009).  https://doi.org/10.1109/ITA.2009.5044933
  16. Valadares, D.C.G., da Silva, M.S.L., Brito, A.M.E., Salvador, E.M.: Achieving data dissemination with security using FIWARE and Intel software guard extensions (SGX). In: IEEE Symposium on Computers and Communications (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dalton Cézane Gomes Valadares
    • 1
    • 2
    Email author
  • Joseana Macêdo Fechine Régis de Araújo
    • 2
  • Ângelo Perkusich
    • 2
  • Marco Aurélio Spohn
    • 3
  • Elmar Uwe Kurt Melcher
    • 2
  • Natália Porfírio Albuquerque
    • 4
  1. 1.Federal Institute of Pernambuco (IFPE)CaruaruBrazil
  2. 2.Federal University of Campina Grande (UFCG)Campina GrandeBrazil
  3. 3.Federal University of Fronteira SulChapecóBrazil
  4. 4.Borborema Termoelétrica LTDACampina GrandeBrazil

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