Advertisement

Peer-to-Peer Networking and Applications

, Volume 9, Issue 4, pp 795–808 | Cite as

The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network

  • Cheriet Mohammed El AmineEmail author
  • Ouslim Mohamed
  • Belaidi Boualam
Article

Abstract

In this paper, we present the implementation of a new indoor localization system. We studied the behavior of the Received Signal Strength Indication (RSSI) for different configurations depending on the initial energy level of the sensors used. The choice of the best XBee configuration for each sensor is obtained after studying the standard deviation of the RSSI. Thus, we performed an indoor localization application using three algorithms based on the RSSI fingerprinting. Several experiments were conducted on an established test bed made of a certain number of XBee wireless sensors. The obtained results are considered very encouraging as they are suitable to locate a person, inside a building with a precision of 80 cm and an efficiency of 90 %.

Keywords

Indoor localization RSSI fingerprinting Wireless sensor network XBee 

References

  1. 1.
    Mahmoud A, Feham M, Labraoui N (2013) “Wireless sensor networks localization algorithms: a comprehensive survey”. Int J Comput Net Commun (IJCNC) 5(6):45–64Google Scholar
  2. 2.
    Al-Kuwari S, Wolthusen SD (2010) “A Survey of Forensic Localization and Tracking Mechanisms in Short-Range and Cellular Networks”. Social Informatics and Telecommunications Engineering Vol 31, pp 19–32, Springer Berlin HeidelbergGoogle Scholar
  3. 3.
    Gezici S (2008) A survey on wireless position estimation. Wirel Pers Commun 44(3):263–282CrossRefGoogle Scholar
  4. 4.
    Cheriet A. Ouslim M, Aizi K (2013) “Localization in a Wireless Sensor Network based on RSSI and a decision tree”.PRZEGLĄD ELEKTROTECHNICZNY, R. 89 NR 12/2013, pp 121–125Google Scholar
  5. 5.
    Tsai S, Lau S and Huang P (2012) “WSN-based Real-Time Indoor Location System at the Taipei World Trade Center: Implementation, Deployment, Measurement, and Experience”.Sensors, 2012 IEEE, pp 1 – 4, Taipei 28–31Google Scholar
  6. 6.
    Coca E, Popa V (2013) “Antenna radiation pattern influence on the localization accuracy in wireless sensor networks”. Ad Elec Comput Eng 13(2):43–46CrossRefGoogle Scholar
  7. 7.
    Luoa X, O’Briena WJ, Julienb CL (2011) “Comparative evaluation of received signal-strength index (RSSI) based indoor localization techniques for construction jobsites”. Adv Eng Inform 25(2):355–363CrossRefGoogle Scholar
  8. 8.
    Chih-Ning H, Chia-Tai C, (2011) “ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI”. The 2nd International Conference on Ambient Systems, Networks and Technologies (ANT),Procedia Computer Science, Vol 5, pp 58–65Google Scholar
  9. 9.
    Chen-Yang C, (2014)“Indoor localization algorithm using clustering on signal and coordination pattern”.Annals of Operations Research,Vol 216, Issue 1, pp 83–99, Springer USGoogle Scholar
  10. 10.
    Wesselsa A, Wangb X, Laurb R, Langa W (2010) “Dynamic indoor localization using multilateration with RSSI in wireless sensor networks for transport logistics”. Eurosensor XXIV Conf Proc Eng 5:220–223Google Scholar
  11. 11.
    Dong Q and Dargi W, (2012) “Evaluation of the Reliability of RSSI for Indoor Localization”. International Conference on Wireless Communications in Unusual and Confined Areas (ICWCUCA), pp 1–6, Clermont Ferrand, 28–30Google Scholar
  12. 12.
    Adewumi, Omotayo G., Karim Djouani, and Anish M. Kurien. (2013) “RSSI based indoor and outdoor distance estimation for localization in WSN.” Industrial Technology (ICIT), 2013 I.E. International Conference on. IEEEGoogle Scholar
  13. 13.
    Dalce, Rejane. (2013) Méthodes de localisation par le signal de communication dans les réseaux de capteurs sans fil en intérieur. Diss. Toulouse, INSAGoogle Scholar
  14. 14.
    Shen, Xingfa, et al. (2005) “Connectivity and RSSI based localization scheme for wireless sensor networks.” Advances in intelligent computing. Springer Berlin Heidelberg, 578–587Google Scholar
  15. 15.
    Yang, Ruohan, and Hao Zhang (2014) “RSSI-Based Fingerprint Positioning System for Indoor Wireless Network.” Intelligent Computing in Smart Grid and Electrical Vehicles. Springer Berlin Heidelberg, 313–319Google Scholar
  16. 16.
    Machaj J, Brida P (2011) “Performance comparison of similarity measurements for database correlation localization method.” intelligent information and database systems. Springer, Berlin, pp 452–461Google Scholar
  17. 17.
    Shin M, Hyunjin J, Inwhee J (2012) “An indoor localization Pre-processing with optimal channel selection considering channel interference.” convergence and hybrid information technology. Springer, Berlin, pp 78–85Google Scholar
  18. 18.
    Anzai D and Hara S, (2009) “An RSSI-Based MAP Localization Method with Channel Parameters Estimation in Wireless Sensor Networks”.IEEE 69th Vehicular Technology Conference, 2009. VTC Spring 2009, pp 1–5, Barcelona, 26–29Google Scholar
  19. 19.
    Liu W. Qiang B, Ng, B. Liu, Y. Liang Guan, Y. HaoLeow, J. Huang (2012) “Radio map position inference algorithm for indoor positioning systems”. 18th IEEE International Conference on Networks (ICON), 2012, pp 161 – 166, Singapore, 12–14Google Scholar
  20. 20.
    Xie L, Wang Y and Xue X. (2010) “A New Indoor Localization Method Based on Inversion Propagation Model”. 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 2010, pp 1 – 4, Chengdu, 23–25Google Scholar
  21. 21.
    Alhmiedat T, Samara G, Abu Salem AO (2013) “An indoor fingerprinting localization approach for ZigBee wireless sensor networks”. Eur J Sci Res 105(2):190–202Google Scholar
  22. 22.
    Yaqian Xu, et al. (2013) “DCCLA: Automatic Indoor Localization Using Unsupervised Wi-Fi Fingerprinting.” Modeling and Using Context. Springer Berlin Heidelberg, 73–86Google Scholar
  23. 23.
    Nerguizian, Chahé, Charles Despins, and Sofiene Affes. (2004) “Indoor geolocation with received signal strength fingerprinting technique and neural networks.” Telecommunications and Networking-ICT 2004. Springer Berlin Heidelberg, 866–875Google Scholar
  24. 24.
    Zhu, Julie Yixuan, et al. (2014) “Spatio-temporal (ST) Similarity Model for Constructing WIFI-based RSSI Fingerprinting Map for Indoor Localization.” International Conference on Indoor Positioning and Indoor Navigation. Vol. 27Google Scholar
  25. 25.
    Quan, M, Eduardo N, and Benjamin P (2010) “Wi-Fi Localization Using RSSI Fingerprinting”Google Scholar
  26. 26.
    Krause, E. F. (1986) “Taxicab Geometry: An Adventure in Non-Euclidean Geometry. Mineola”Google Scholar
  27. 27.
    Bolliger, P, et al. (2009) “Improving location fingerprinting through motion detection and asynchronous interval labeling.” Location and Context Awareness. Springer Berlin Heidelberg. 37–51.Google Scholar
  28. 28.
    Honkavirta V, et al. (2009) “A comparative survey of WLAN location fingerprinting methods.” Positioning, Navigation and Communication, 2009. WPNC 2009. 6th Workshop on. IEEEGoogle Scholar
  29. 29.
    Chehri A, Hussein M and Wisam F (2012) “Indoor Cooperative Positioning Based on Fingerprinting and Support Vector Machines.” Mobile and Ubiquitous Systems: Computing, Networking, and Services. Springer Berlin Heidelberg, 114–124Google Scholar
  30. 30.
    Product Manual v1.xAx - 802.15.4 Protocol, IEEE® 802.15.4 OEM RF Modules by MaxStream.Google Scholar
  31. 31.
    Scholl PM, Kohlbrecher S, Sachidananda V, Van Laerhoven K. (2012) “Fast Indoor Radio-Map Building for RSSI-based Localization Systems”. Ninth International Conference on Networked Sensing Systems (INSS), 2012, pp 1–2, Antwerp, 11–14Google Scholar
  32. 32.
    Gogolak L, Pletl S, Kukolj D (2013) “Neural network-based indoor localization in WSN environments”. Acta Pol Hung 10(6):221–234Google Scholar
  33. 33.
    Schmid J, adeke TG, Wilhelm S, Klaus D. uller-Glaser M. (2011) “On the Fusion of Inertial Data for Signal Strength Localization”. 8th Workshop on Positioning Navigation and Communication (WPNC), 2011, pp 7 – 12, Dresden, 7–8Google Scholar
  34. 34.
    Cheriet A and Ouslim M.“A localization and an identification system of personnel in areas at risk using a wireless sensor network”. International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013, pp 127 – 131, Konya, 9–11Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Cheriet Mohammed El Amine
    • 1
    Email author
  • Ouslim Mohamed
    • 1
  • Belaidi Boualam
    • 2
  1. 1.LMSE laboratoryUniversity of science and technology USTOMBOranAlgeria
  2. 2.Electronics departmentUniversity of science and technology USTOMBOranAlgeria

Personalised recommendations