Skip to main content
SpringerLink
Log in

An Adaptive Algorithm for Geofencing

  • Conference paper
  • First Online:
Information Technology for Management: Emerging Research and Applications (AITM 2018, ISM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 346))

Abstract

Location based services play a key role in creating fully automated and adaptive systems that support Supply Chain Management and complex inter-modal logistics. IoT technology allows companies to part from statistical analysis in favour of proactive management by leveraging data collected in real time from the goods and processes that sustain their business. This paper describes a real world implementation of proactive location-based services suitable for application scenarios with strong time constraints, such as real-time systems, called Proactive Fast and Low Resource Geofencing Algorithm within a centralized, thin-client IoT system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Feng, F., Pang, Y., Lodewijks, G.: Towards context-aware supervision for logistics asset management: concept design and system implementation. In: Ziemba, E. (ed.) AITM/ISM -2016. LNBIP, vol. 277, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53076-5_1

    Chapter  Google Scholar 

  2. Sachin, W., Rahate, D.M.S.: Geo-fencing infrastructure: location based service. Int. Res. J. Eng. Technol. 3, 1095–1098 (2016)

    Google Scholar 

  3. Rouse, M.: Geo-fencing. http://whatis.techtarget.com/definition/geofencing. Accessed 2016

  4. Allen, G.: Internet of things, industrial internet of things, industry 4.0 - it’s all connected! (no pun intended). https://redshift.autodesk.com/industrial-internet-of-things-iot-terms/. Accessed 2015

  5. Garzon, S.R., Deva, B.: Infrastructure-assisted geofencing: proactive location-based services with thin mobile clients and smart servers. In: 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 61–70, March 2015. https://doi.org/10.1109/MobileCloud.2015.31

  6. Carchiolo, V., Modica, P.W., Loria, M.P., Toja, M., Malgeri, M.: A geofencing algorithm fit for supply chain management. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018, Poznań, Poland, 9–12 September 2018, pp. 737–746 (2018). https://doi.org/10.15439/2018F238

  7. Ray, S., Brown, A.D., Koudas, N., Blanco, R., Goel, A.K.: Parallel in-memory trajectory-based spatiotemporal topological join. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 361–370, October 2015. https://doi.org/10.1109/BigData.2015.7363777

  8. Lin, K., Chen, Y., Qiu, M., Zeng, M., Huang, W.: SLGC: a fast point-in-area algorithm based on scan-line algorithm and grid compression. In: 2016 11th International Conference on Computer Science Education (ICCSE), pp. 352–356, August 2016. https://doi.org/10.1109/ICCSE.2016.7581606

  9. Tang, S., Yu, Y., Zimmermann, R., Obana, S.: Efficient geo-fencing via hybrid hashing: a combination of bucket selection and in-bucket binary search. ACM Trans. Spat. Algorithms Syst. 1(2), 5:1–5:22 (2015). https://doi.org/10.1145/2774219

    Article  Google Scholar 

  10. Allen, G.: Harnessing the power of location based services. http://blogs.dcvelocity.com/supply_chain_innovation/2016/03/harnessing-the-power-of-location-based-services.html. Accessed 2016

  11. Rao, B., Minakakis, L.: Evolution of mobile location-based services. Commun. ACM 46(12), 61–65 (2003). https://doi.org/10.1145/953460.953490

    Article  Google Scholar 

  12. IATA: Guidance on the expanded use of passenger portable electronic devices (PEDs) (2014)

    Google Scholar 

  13. Rein, A., Ülar, M.: Location based services-new challenges for planning and public administration? Futures 37(6), 547–561 (2005). https://doi.org/10.1016/j.futures.2004.10.012

    Article  Google Scholar 

  14. Carchiolo, V., Loria, M.P., Malgeri, M., Toja, M.: An efficient real-time architecture for collecting IoT data. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1157–1166, September 2017. https://doi.org/10.15439/2017F381

  15. ICAO: DOC 9674/AN 946 - WGS84 Manual (2002)

    Google Scholar 

  16. Butler, H., Daly, M., Doyle, A., Gillies, S., Hagen, S., Schaub, T.: The GeoJSON format. RFC 7946, RFC Editor, August 2016. https://tools.ietf.org/html/rfc7946

  17. Erwig, M., Schneider, M.: Developments in spatio-temporal query languages. In: Proceedings of Tenth International Workshop on Database and Expert Systems Applications. DEXA 1999, pp. 441–449 (1999). https://doi.org/10.1109/DEXA.1999.795206

  18. Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-object representations. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds.) SSD 1999. LNCS, vol. 1651, pp. 111–131. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48482-5_9

    Chapter  Google Scholar 

  19. TAS Foundation: ab - Apache HTTP server benchmarking tool. https://httpd.apache.org/docs/2.4/programs/ab.html. Accessed 2018

  20. U.S. Census Bureau: Tiger/line shapefiles and tiger/line files (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dip. Matematica e Informatica, Universitá di Catania, Catania, Italy

    Vincenza Carchiolo

  2. See Your Box, 2 Cormont Road, London, SE5 9RA, England, UK

    Mark Phillip Loria, Paolo Walter Modica & Marco Toja

  3. Dip. Ingegneria Elettrica Elettronica e Informatica, Universitá di Catania, Catania, Italy

    Michele Malgeri

Authors
  1. Vincenza Carchiolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Phillip Loria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Malgeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paolo Walter Modica
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marco Toja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vincenza Carchiolo .

Editor information

Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Carchiolo, V., Loria, M.P., Malgeri, M., Modica, P.W., Toja, M. (2019). An Adaptive Algorithm for Geofencing. In: Ziemba, E. (eds) Information Technology for Management: Emerging Research and Applications. AITM ISM 2018 2018. Lecture Notes in Business Information Processing, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-15154-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-15154-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15153-9

  • Online ISBN: 978-3-030-15154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation