Skip to main content
Springer Nature Link
Log in

A Bio-Inspired Approach to WiFi-Based Indoor Localization

  • Conference paper
  • First Online:
Artificial Life and Evolutionary Computation (WIVACE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 900))

Included in the following conference series:

  • 418 Accesses

Abstract

In this paper, the problem of indoor localization is investigated using a bio-inspired approach. The proposed approach relies on the use of WiFi networks, which nowadays can be considered a commodity available in all indoor environments. WiFi signals are used to obtain estimates of the distance between a smart device, whose position needs to be estimated, and the fixed access points of the network, which are assumed to be in known positions. Once a given number of range estimates from each available access point has been acquired, proper range averages are performed to feed the proposed localization algorithm. According to the proposed approach, localization is formulated in terms of an optimization problem, which is solved using an algorithm inspired from particle swarm optimization. Such an algorithm has been integrated in an add-on module of JADE, which is intended to execute on the smart device whose position needs to be estimated, and which was tested in relevant indoor scenarios. Experimental results in tested scenarios are shown in the last part of the paper in order to evaluate the performance of the proposed bio-inspired localization algorithm.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Banzi, M., Caire, G., Gotta, D.: WADE: a software platform to develop mission critical, applications exploiting agents and workflows. In: Proceedings of the International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2008), pp. 29–36. IFAAMAS (2008)

    Google Scholar 

  2. Bellifemine, F., Bergenti, F., Caire, G., Poggi, A.: Jade—a Java agent development framework. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) Multi-Agent Programming. MSASSO, vol. 15, pp. 125–147. Springer, Boston, MA (2005). https://doi.org/10.1007/0-387-26350-0_5

    Chapter  Google Scholar 

  3. Bergenti, F., Caire, G., Gotta, D.: Interactive workflows with WADE. In: Proceedings of the IEEE International Conference on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE 2012), pp. 10–15. IEEE (2012)

    Google Scholar 

  4. Bergenti, F., Caire, G., Gotta, D.: An overview of the AMUSE social gaming platform. In: Proceedings of the Workshop From Objects to Agents (WOA 2013), CEUR Workshop Proceedings, vol. 1099. RWTH Aachen (2013)

    Google Scholar 

  5. Bergenti, F., Caire, G., Gotta, D.: Agents on the move: JADE for Android devices. In: Proceedings of the Workshop From Objects to Agents (WOA 2014), CEUR Workshop Proceedings, vol. 1260. RWTH Aachen (2014)

    Google Scholar 

  6. Bergenti, F., Caire, G., Gotta, D.: Large-scale network and service management with WANTS. In: Industrial Agents: Emerging Applications of Software Agents in Industry. pp. 231–246. Elsevier (2015)

    Google Scholar 

  7. Bergenti, F., Franchi, E., Poggi, A.: Agent-based social networks for enterprise collaboration. In: Proceedings of the IEEE International Conference on Enabling Technologies: Infrastructures for Collaborative Enterprises, WETICE 2011, pp. 25–28. IEEE (2011)

    Google Scholar 

  8. Bergenti, F., Monica, S.: Location-aware social gaming with AMUSE. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M.J. (eds.) PAAMS 2016. LNCS (LNAI), vol. 9662, pp. 36–47. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39324-7_4

    Chapter  Google Scholar 

  9. Bulusu, N., Heidemann, J., Estrin, D.: GPS-less low cost outdoor localization for very small devices. IEEE Pers. Commun. 7(5), 28–34 (2000)

    Article  Google Scholar 

  10. Farid, Z., Nordin, R., Ismail, M.: Recent advances in wireless indoor localization techniques and system. J. Comput. Netw. Commun. 2013, 12 (2013)

    Google Scholar 

  11. Ho, K.C., Lu, X., Kovavisaruch, L.: Source localization using TDOA and FDOA measurements in the presence of receiver location errors: analysis and solution. IEEE Transact. Sign. Proces. 55(2), 684–696 (2007)

    Article  MathSciNet  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, ICNN 1995, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  13. Monica, S., Bergenti, F.: Location-aware JADE agents in indoor scenarios. In: Proceedings of the Workshop from Objects to Agents (WOA 2015), CEUR Workshop Proceedings, vol. 1382, pp. 103–108. RWTH Aachen (2015)

    Google Scholar 

  14. Monica, S., Bergenti, F.: A Comparison of accurate indoor localization of static targets via WiFi and UWB ranging. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. AISC, vol. 473, pp. 111–123. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40159-1_9

    Chapter  Google Scholar 

  15. Monica, S., Bergenti, F.: An experimental evaluation of agent-based indoor localization. In: The Science and Information Computing Conference. pp. 638–646. IEEE (2017)

    Google Scholar 

  16. Monica, S., Bergenti, F.: Experimental Evaluation of agent-based localization of smart appliances. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds.) EUMAS/AT -2016. LNCS (LNAI), vol. 10207, pp. 293–304. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59294-7_24

    Chapter  Google Scholar 

  17. Monica, S., Bergenti, F.: Indoor localization of JADE agents without a dedicated infrastructure. In: Berndt, J.O., Petta, P., Unland, R. (eds.) MATES 2017. LNCS (LNAI), vol. 10413, pp. 256–271. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64798-2_16

    Chapter  Google Scholar 

  18. Monica, S., Bergenti, F.: An optimization-based algorithm for indoor localization of JADE agents. In: Proceedings of Workshop From Objects to Agents (WOA 2017), CEUR Workshop Proceedings, vol. 1867, pp. 65–70. RWTH Aachen (2017)

    Google Scholar 

  19. Monica, S., Ferrari, G.: Swarm intelligent approaches to auto-localization of nodes in static UWB networks. Appl. Soft Comput. 25, 426–434 (2014)

    Article  Google Scholar 

  20. Monica, S., Ferrari, G.: A swarm-based approach to real-time 3D indoor localization: experimental performance analysis. Appl. Soft Comput. 43, 489–497 (2016)

    Article  Google Scholar 

  21. Patwari, N., Ash, J.N., Kyperountas, S., Hero, A.O., Moses, R.L., Correal, N.S.: Locating the nodes. IEEE Signal Process. Mag. 22(4), 54–69 (2005)

    Article  Google Scholar 

  22. Poggi, A., Bergenti, F.: Developing smart emergency applications with multi-agent systems. Int. J. E-Health Med. Commun. 1(4), 1–13 (2010)

    Article  Google Scholar 

  23. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. J. 1(1), 33–57 (2007)

    Article  Google Scholar 

  24. Sahinoglu, Z., Gezici, S., Guvenc, I.: Ultra-wideband Positioning Systems: Theoretical Limits. Ranging Algorithms and Protocols. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  25. Shen, G., Zetik, R., Thomä, R.S.: Performance comparison of TOA and TDOA based location estimation algorithms in LOS environment. In: Proceedings of the Workshop on Positioning, Navigation and Communication, WPNC 2008, pp. 71–78. IEEE (2008)

    Google Scholar 

  26. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, ICEC 1999, pp. 69–73. IEEE (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze 53/A, 43124, Parma, Italy

    Federico Bergenti & Stefania Monica

Authors
  1. Federico Bergenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefania Monica
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefania Monica .

Editor information

Editors and Affiliations

  1. University of Parma, Parma, Italy

    Stefano Cagnoni

  2. University of Parma, Parma, Italy

    Monica Mordonini

  3. Università degli Studi eCampus, Novedrate, Italy

    Riccardo Pecori

  4. University of Bologna, Cesena, Italy

    Andrea Roli

  5. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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

Bergenti, F., Monica, S. (2019). A Bio-Inspired Approach to WiFi-Based Indoor Localization. In: Cagnoni, S., Mordonini, M., Pecori, R., Roli, A., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2018. Communications in Computer and Information Science, vol 900. Springer, Cham. https://doi.org/10.1007/978-3-030-21733-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21733-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21732-7

  • Online ISBN: 978-3-030-21733-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics