Skip to main content

Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection

  • 1673 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 319)

Abstract

The scientific contribution of this paper is a Big Data-centric study, conducted using Google Trends, that involved analysis of the global, country-level, and state-level search trends related to indoor localization by mining relevant Google Search data from 2015–2020. There are three novel findings of this study. First, the current global search interest in indoor localization is higher than the average, median, and mode values of search interests (since 2015), and it is projected to keep increasing in the near future. Second, Singapore predominantly leads all other countries in terms of user interests in indoor localization. It is followed by Canada and United States, which are followed by the other countries. Third, the state-level analysis for the United States shows that Massachusetts leads all other states in terms of user interests in indoor localization. It is followed by New Jersey and Michigan, which are followed by the other states.

Keywords

  • Google trends
  • Indoor localization
  • User interests
  • Big data
  • Indoor location detection
  • Google search
  • Search interest
  • Search trends

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-85540-6_73
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-85540-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

References

  1. Langlois, C., Tiku, S., Pasricha, S.: Indoor localization with smartphones: harnessing the sensor suite in your pocket. IEEE Consum. Electron. Mag. 6(4), 70–80 (2017)

    CrossRef  Google Scholar 

  2. Gorecky, D., Schmitt, M., Loskyll, M., Zuhlke, D.: Human-machine-interaction in the industry 4.0 era. In: 2014 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289–294. IEEE (2014)

    Google Scholar 

  3. Thakur, N., Han, C.Y.: Multimodal approaches for indoor localization for ambient assisted living in smart homes. Information (Basel). 12(3), 114 (2021)

    CrossRef  Google Scholar 

  4. Dardari, D., Closas, P., Djuric, P.M.: Indoor tracking: theory, methods, and technologies. IEEE Trans Veh Technol. 64(4), 1263–1278 (2015)

    CrossRef  Google Scholar 

  5. Google Search Statistics [Internet]. Internetlivestats.com. https://www.internetlivestats.com/google-search-statistics/. Accessed 21 Mar 2021

  6. Preis, T., Moat, H.S., Stanley, H.E., Bishop, S.R.: Quantifying the advantage of looking forward. Sci. Rep. 2(1), 350 (2012)

    CrossRef  Google Scholar 

  7. Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using Google Trends. Sci Rep. 3(1), 1684 (2013)

    CrossRef  Google Scholar 

  8. Mavragani, A., Ochoa, G., Tsagarakis, K.P.: Assessing the methods, tools, and statistical approaches in Google Trends research: systematic review. J. Med. Internet Res. 20(11), e270 (2018)

    Google Scholar 

  9. Chen, Y., Xie, J.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manage. Sci. 54(3), 477–491 (2008)

    CrossRef  Google Scholar 

  10. Google Trends [Internet]. Google.com. https://trends.google.com/trends/. Accessed 22 Nov 2020

  11. Mellon, J.: Where and when can we use Google Trends to measure issue salience? PS Polit Sci Polit. 46(02), 280–290 (2013)

    CrossRef  Google Scholar 

  12. Hu, J., Liu, D., Yan, Z., Liu, H.: Experimental analysis on weight K-nearest neighbor indoor fingerprint positioning. IEEE Internet Things J. 6(1), 891–897 (2019)

    CrossRef  Google Scholar 

  13. Qin, F., Zuo, T., Wang, X.: CCpos: WiFi fingerprint indoor positioning system based on CDAE-CNN. Sensors (Basel). 21(4), 1114 (2021)

    CrossRef  Google Scholar 

  14. Ullah Khan, I., et al.: An improved hybrid indoor positioning system based on surface tessellation artificial neural network. Meas. Control. 53(9–10), 1968–1977 (2020)

    Google Scholar 

  15. Labinghisa, B.A., Lee, D.M.: Neural network-based indoor localization system with enhanced virtual access points. J. Supercomput. 77(1), 638–651 (2020). https://doi.org/10.1007/s11227-020-03272-4

    CrossRef  Google Scholar 

  16. Zhang, L., Zhao, C., Wang, Y., Dai, L.: Fingerprint-based indoor localization using weighted K-nearest neighbor and weighted signal intensity. In: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture, New York, NY, USA. ACM (2020)

    Google Scholar 

  17. Gao, J., Li, X., Ding, Y., Su, Q., Liu, Z.: WiFi-based indoor positioning by random forest and adjusted cosine similarity. In: 2020 Chinese Control and Decision Conference (CCDC), pp. 1426–1431. IEEE (2020)

    Google Scholar 

  18. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: rapid prototyping for complex data mining tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2006. New York, New York, USA. ACM Press (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nirmalya Thakur .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Thakur, N., Han, C.Y. (2022). Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection. In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_73

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85540-6_73

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85539-0

  • Online ISBN: 978-3-030-85540-6

  • eBook Packages: EngineeringEngineering (R0)