Advertisement

Developing WLAN-Based Intelligent Positioning System for Presence Detection with Limited Sensors

  • Ivan Nikitin
  • Vitaly Romanov
  • Giancarlo Succi
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

WiFi-Based Positioning Systems (WBPS) play a key role in indoor navigation, but further development of these systems continues to this day. WBPS have been applied to different tasks including mobility tracking and behavior analysis. Mobility tracking allows detecting a user in the environment even if one does not use positioning services. Tracking enables sensing the human presence in different environments, including occupancy detections in smart homes, geofencing, enhanced security and many other scenarios. One of the basic performance criteria of a positioning system is its precision. The general rule states that precision grows with the increase of the number of reference signals used for positioning. However, it is unclear how much information is required to estimate the location of a person reliably. This chapter overviews the current research in the area of Received Signal Strength Indicator (RSSI) based positioning and evaluates a positioning system for localizing a person in an indoor environment, taking into account the number of Access Points (APs) available for estimating the location. We conduct performance analysis of an indoor positioning system based on measurements from a real walk. Additionally, we conduct a simulation, where we analyze the impact of the noise on the positioning quality.

Keywords

Indoor localization WiFi Mobility tracking Markov model Positioning system WIFI-based positioning system Simulation GPS 

References

  1. 1.
    Vo QD, De P (2016) A survey of fingerprint-based outdoor localization. IEEE Commun Surv Tutor 18(1):491–506CrossRefGoogle Scholar
  2. 2.
    Li H et al (2014) Achieving privacy preservation in WiFi fingerprint-based localizationGoogle Scholar
  3. 3.
    Youssef MA, Agrawala A (2007) Analysis of the optimal strategy for WLAN location determination systems. Int J Model Simul 27(1):53–59CrossRefGoogle Scholar
  4. 4.
    Figuera C et al. (2009) Nonparametric model comparison and uncertainty evaluation for signal strength indoor location. IEEE Trans Mob Comput 8(9):1250–1264CrossRefGoogle Scholar
  5. 5.
    Pourhomayoun M, Fowler M (2012) Improving WLAN-based indoor mobile positioning using sparsity. In: Conference record—asilomar conference on signals, systems and computers, pp 1393–1396Google Scholar
  6. 6.
    Sen S et al. (2013) Avoiding multipath to revive inbuilding WiFi localization. In: Proceeding of the 11th annual international conference on mobile systems, applications, and services—MobiSys’13Google Scholar
  7. 7.
    Dardari D et al. (2015) Indoor tracking: theory, methods, and technologies. IEEE Trans Veh Technol 64(4):1263–1278Google Scholar
  8. 8.
    Sapiezynski P et al. (2016) Inferring person-to-person proximity using WiFi signalsGoogle Scholar
  9. 9.
    Chilipirea C et al. (2016) Presumably simple: monitoring crowds using WiFi, pp 220–225Google Scholar
  10. 10.
    Bellavista P et al. (2017) Human dynamics of mobile crowd sensing experimental datasets. In: IEEE international conference on communications, pp 0–5Google Scholar
  11. 11.
    Li Z et al. (2017) Discovering mass activities using anomalies in individual mobility motifs. In: Proceedings—13th IEEE international conference on ubiquitous intelligence and computing, 13th IEEE international conference on advanced and trusted computing, 16th IEEE international conference on scalable computing and communications, IEEE international, pp 321–326Google Scholar
  12. 12.
    Traunmueller M et al. (n.d.) Digital traces: modeling urban mobility using WiFi probe dataGoogle Scholar
  13. 13.
    Basalamah A (2016) Crowd mobility analysis using WiFi sniffers. Int J Adv Comput Sci Appl 7:374–378Google Scholar
  14. 14.
    Scheuner J et al. (2016) Probr—a generic and passive WiFi tracking system. In: Proceedings—conference on local computer networks, LCN, pp 495–502Google Scholar
  15. 15.
    Vanderhulst G et al. (2015) Detecting human encounters from WiFi radio signals. In: Proceedings of the 14th international conference on mobile and ubiquitous multimedia—MUM’15, pp 97–108Google Scholar
  16. 16.
    Ma W et al. (2015) Detecting pedestrians behavior in building based on Wi-Fi signals. Proceedings of 2015 IEEE international conference on smart city, SmartCity 2015, Held jointly with 8th IEEE international conference on social computing and networking, SocialCom 2015, 5th IEEE international conference on sustainable computing and communication, pp 1–8Google Scholar
  17. 17.
    Acer UG et al. (2016) Capturing personal and crowd behavior with Wi-Fi analytics. In: Proceedings of the 3rd international on workshop on physical analytics—WPA’16, pp 43–48Google Scholar
  18. 18.
    Wu FJ, Solmaz G (2017) We hear your activities through Wi-Fi signals. In: 2016 IEEE 3rd world forum on internet of things, WF-IoT 2016, pp 251–256Google Scholar
  19. 19.
    Seifeldin M et al (2013) Nuzzer: a large-scale device-free passive localization system for wireless environments. IEEE Trans Mob Comput 12:1321–1334CrossRefGoogle Scholar
  20. 20.
    Zhou Z et al. (2013) Towards omnidirectional passive human detectionGoogle Scholar
  21. 21.
    Wang Y et al. (2014) E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: Proceedings of the 20th annual international conference on Mobile computing and networking—MobiCom’14, pp 617–628Google Scholar
  22. 22.
    Wu C et al. (2015) Non-invasive detection of moving and stationary human with WiFi. IEEE J Sel Areas CommunGoogle Scholar
  23. 23.
    Abdelnasser H et al. (2015) WiGest: {A} Ubiquitous WiFi-based gesture recognition system. CoRR, Volume abs/1501.0, pp 1472–1480Google Scholar
  24. 24.
    Mrindoko NR, Minga LM (2016) A comparison review of indoor positioning techniques. Int J Comput (IJC) 21:42–49Google Scholar
  25. 25.
    Liu H et al. (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C (Appl Rev) 37(6):1067–1080CrossRefGoogle Scholar
  26. 26.
    Deak G et al. (2012) A survey of active and passive indoor localisation systems. Comput Commun 35(16):1939–1954CrossRefGoogle Scholar
  27. 27.
    Xiao J et al. (2016) A survey on wireless indoor localization from the device perspective. ACM Comput Surv 49(2):25CrossRefGoogle Scholar
  28. 28.
    Pirzada N et al (2013) Comparative analysis of active and passive indoor localization systems. Elsevier B.V, New YorkCrossRefGoogle Scholar
  29. 29.
    Hossain AKMM, Soh WS (2015) A survey of calibration-free indoor positioning systems. Comput Commun 66:1–3CrossRefGoogle Scholar
  30. 30.
    Farid Z et al (2013) Recent advances in wireless indoor localization techniques and system. J Comput Netw Commun 2013:1–12CrossRefGoogle Scholar
  31. 31.
    Makki A et al. (2015) Survey of WiFi positioning using time-based techniques. Comput Netw 88:218–233CrossRefGoogle Scholar
  32. 32.
    Honkavirta V et al. (2009) A comparative survey of WLAN location fingerprinting methods, pp 243–251Google Scholar
  33. 33.
    He S, Chan SHG (2016) Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutor 18(1):466–490CrossRefGoogle Scholar
  34. 34.
    Yiu S et al. (2017) Wireless RSSI fingerprinting localization. Sig Process 131:235–244CrossRefGoogle Scholar
  35. 35.
    Saxena M et al. (2008) Experimental analysis of RSSI-based location estimation in wireless sensor networks. In: 2008 3rd international conference on communication systems software and middleware and workshops (COMSWARE’08), pp 503–510Google Scholar
  36. 36.
    Mariakakis A et al. (2014) SAIL: single access point-based indoor localization In: MobiSys’14Google Scholar
  37. 37.
    Xiao Z et al. (2015) Robust indoor positioning with lifelong learning. IEEE J Sel Areas CommunGoogle Scholar
  38. 38.
    Wu C et al. (2013) DorFin: WiFi fingerprint-based localization revisited. arXiv preprint arXiv:1308.6663
  39. 39.
    Wu C et al. (2013) Smartphones based crowdsourcing for indoor localizationGoogle Scholar
  40. 40.
    Liu H et al. (2012) Push the limit of WiFi based localization for smartphones. In: Proceedings of the 18th annual international conference on mobile computing and networking (Mobicom’12), p 305Google Scholar
  41. 41.
    Jing H et al. (2016) Wi-Fi fingerprinting based on collaborative confidence level training. Pervasive Mob Comput 30:32–44CrossRefGoogle Scholar
  42. 42.
    Zàruba GV et al. (2007) Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wirel Netw 13(2):221–235CrossRefGoogle Scholar
  43. 43.
    Ni Y et al. (2016) An indoor pedestrian positioning method using HMM with a fuzzy pattern recognition algorithm in a WLAN fingerprint system. Sensors (Switzerland) 16(9):1447CrossRefGoogle Scholar
  44. 44.
    He S et al. (2015) Fusing noisy fingerprints with distance bounds for indoor localizationGoogle Scholar
  45. 45.
    Xie Y et al. (2015) Precise power delay profiling with commodity WiFi. In: Proceedings of the 21st annual internationalGoogle Scholar
  46. 46.
    Chapre Y et al. (2014) CSI-MIMO: indoor Wi-Fi fingerprinting system. In: Proceedings—conference on local computer networks, LCN, pp 202–209Google Scholar
  47. 47.
    Jiang ZP et al. (2014) Communicating is crowdsourcing: Wi-Fi indoor localization with CSI-based speed estimation. J Comput Sci Technol 29(4):589–604CrossRefGoogle Scholar
  48. 48.
    Kumar CP et al. (2014) Single access point based indoor localization technique for augmented reality gaming for children. s.l., s.nGoogle Scholar
  49. 49.
    Yang Z et al (2013) From RSSI to CSI: indoor localization via channel response. ACM Comput Surv (CSUR) 46:25CrossRefGoogle Scholar
  50. 50.
    Wu C et al. (2012) WILL: wireless indoor localization without site surveyGoogle Scholar
  51. 51.
    Jin Y et al. (2010) Indoor localization with channel impulse response based fingerprint and nonparametric regression. IEEE Trans Wirel Commun 9(3):1120–1127CrossRefGoogle Scholar
  52. 52.
    Zhou Z et al. (2014) LiFi: Line-Of-Sight identification with WiFi. s.l., s.nGoogle Scholar
  53. 53.
    Phillips C et al (2013) A survey of wireless path loss prediction and coverage mapping methods. IEEE Commun Surv Tutor 15:255–270CrossRefGoogle Scholar
  54. 54.
    Bose A, Chuan HF (2007) A practical path loss model for indoor WiFi positioning enhancement. s.l., s.nGoogle Scholar
  55. 55.
    Ji Y et al. (2006) ARIADNE: a dynamic indoor signal map construction and localization system. In: Proceedings of the 4th international conference on mobile systems, applications and services—MobiSys 2006, p 151Google Scholar
  56. 56.
    Kraxberger S et al. (2010) WLAN location determination without active client collaboration. In: Proceedings of the 2010 international conference on wireless communications and mobile computing, IWCMC 10, pp 1188–1192Google Scholar
  57. 57.
    Ji Z et al (2001) Efficient ray-tracing methods for propagation prediction for indoor wireless communications. IEEE Antennas Propag Mag 43:41–49CrossRefGoogle Scholar
  58. 58.
    El-Kafrawy K et al. (2010) Propagation modeling for accurate indoor WLAN RSS-based localization. In: IEEE vehicular technology conference, pp 1–5Google Scholar
  59. 59.
    Cocheril Y, Vauzelle R (2007) A new ray-tracing based wave propagation model including rough surfaces scattering. Prog Electromagn Res 75:357–381CrossRefGoogle Scholar
  60. 60.
    Kusaka M (2015) Efficient ray tracing algorithm with the avoidance of duplicate image generation, pp 152–157Google Scholar
  61. 61.
    Viol N et al. (2012) Hidden Markov model-based 3D path-matching using raytracing-generated Wi-Fi models. In: 2012 international conference on indoor positioning and indoor navigation, IPIN 2012—conference proceedings, pp 13–15Google Scholar
  62. 62.
    Kausar ASMZ et al. (2013) Efficient radio propagation prediction algorithm including rough surface scattering with improved time complexity. Prog Electromagn Res 53:127–145Google Scholar
  63. 63.
    Klepal M (2003) Novel approach to indoor electromagnetic wave propagation modelling. Czech Technical University in PragueGoogle Scholar
  64. 64.
    Pahlavan K (1998) Site-specific wideband and narrowband modeling of indoor radio channel using ray-tracing, vol 6, pp 65–68Google Scholar
  65. 65.
    Schmitz A, Kobbelt L (2011) Efficient and accurate urban outdoor radio wave propagation. In: Proceedings—2011 international conference on electromagnetics in advanced applications, ICEAA’11, pp 323–326Google Scholar
  66. 66.
    Schmitz A et al. (2011) Efficient rasterization for outdoor radio wave propagation. IEEE Trans Vis Comput Graph 17:159–170CrossRefGoogle Scholar
  67. 67.
    Salem M et al (2011) Validation of three-dimensional ray-tracing algorithm for indoor wireless propagations. ISRN Commun Netw 2011:1–5CrossRefGoogle Scholar
  68. 68.
    Rothe D (2012) Indoor Localization of mobile devices based on Wi-Fi signals using raytracing supportedGoogle Scholar
  69. 69.
    Khodayari S et al (2010) A RSS-based fingerprinting method for positioning based on historical data. In: 2010 international symposium on performance evaluation of computer and telecommunication systems, pp 306–310Google Scholar
  70. 70.
    Gu Z et al. (2016) Reducing fingerprint collection for indoor localization. s.l., s.nGoogle Scholar
  71. 71.
    Jiang Y et al. (2012) ARIEL: automatic Wi-Fi based room fingerprinting for indoor localization. In: Proceedings of the 2012 ACM conference on ubiquitous computing, pp 441–450Google Scholar
  72. 72.
    Jiang Y et al. (2013) Hallway based automatic indoor floorplan construction using room fingerprints. In: Proceedings of international conference on ubiquitous computingGoogle Scholar
  73. 73.
    Goswami A et al. (2011) WiGEM: a learning-based approach for indoor localization. CoNEXTGoogle Scholar
  74. 74.
    Ferris B D et al. (2007) WiFi-SLAM using Gaussian process latent variable models. Science 2480–2485Google Scholar
  75. 75.
    Wang B et al. (2015) Indoor positioning via subarea fingerprinting and surface fitting with received signal strength. Pervasive Mob Comput 23:43–58CrossRefGoogle Scholar
  76. 76.
    Kaji K, Kawaguchi N (2012) Design and implementation of WiFi indoor localization based on Gaussian mixture model and particle filter. In: 2012 international conference on indoor positioning and indoor navigationGoogle Scholar
  77. 77.
    Xiao Z et al. (2014) Lightweight map matching for indoor localisation using conditional random fields. s.l., s.n., pp 131–142Google Scholar
  78. 78.
    Chintalapudi K et al. (2010) Indoor localization without the pain. In: 16th Annual international conference on mobile computing and networking—(MobiCom’10), p 173Google Scholar
  79. 79.
    Yang Z et al. (2012) Locating in fingerprint space: wireless indoor localization with little human intervention. In: Proceedings of the annual international conference on mobile computing and networking, pp 269–280Google Scholar
  80. 80.
    Wang H et al. (2012) No need to war-drive: unsupervised indoor localization. In: Proceedings of the 10th international conference on mobile systems, applications, and services (MobiSys’12), pp 197–210Google Scholar
  81. 81.
    Shen G et al. (2013) Walkie-Markie: indoor pathway mapping made easy. In: Proceedings of the 10th USENIX conference on networked systems design and implementation, pp 85–98Google Scholar
  82. 82.
    Xiong H, Tao D (2017) A Diversified generative latent variable model for WiFi-SLAM. In: Proceedings of the 31th Conference on Artificial Intelligence (AAAI 2017), pp 3841–3847Google Scholar
  83. 83.
    Herranz F et al (2016) WiFi SLAM algorithms: an experimental comparison. Robotica 34:837–858CrossRefGoogle Scholar
  84. 84.
    Faragher RM et al. (2012) Opportunistic radio SLAM for indoor navigation using smartphone sensors. In: Record—IEEE PLANS, position location and navigation symposium, pp 120–128Google Scholar
  85. 85.
    Bruno L, Robertson P (2011) WiSLAM: improving FootSLAM with WiFi. s.l., s.nGoogle Scholar
  86. 86.
    Huang J et al. (2011) Efficient, generalized indoor WiFi GraphSLAM. s.l., s.nGoogle Scholar
  87. 87.
    Kivimäki T et al. (2014) A review on device-free passive indoor positioning methods. Int J Smart Home 8(1):71–94CrossRefGoogle Scholar
  88. 88.
    Zhao Y, Patwari N (2011) Noise reduction for variance-based device-free localization and tracking. s.l., s.n., pp. 179–187Google Scholar
  89. 89.
    Wilson J, Patwari N (2011) See-through walls: motion tracking using variance-based radio tomography networks. IEEE Trans Mob Comput 10:612–621CrossRefGoogle Scholar
  90. 90.
    Wang J et al (2013) Robust device-free wireless localization based on differential RSS measurements. IEEE Trans Industr Electron 60:5943–5952CrossRefGoogle Scholar
  91. 91.
    Nannuru S et al (2013) Radio-frequency tomography for passive indoor multitarget tracking. IEEE Trans Mob Comput 12:2322–2333CrossRefGoogle Scholar
  92. 92.
    Guo Y et al. (2015) An exponential-rayleigh model for RSS-based device-free localization and tracking. IEEE Trans Mob ComputGoogle Scholar
  93. 93.
    Hong J, Ohtsuki T (2015) Signal eigenvector-based device-free passive localization using array sensor. IEEE Trans Veh Technol 64:1354–1363CrossRefGoogle Scholar
  94. 94.
    Xu C et al (2016) The case for efficient and robust RF-based device-free localization. IEEE Trans Mob Comput 15:2362–2375CrossRefGoogle Scholar
  95. 95.
    Lui G et al. (2011) Differences in RSSI readings made by different Wi-Fi chipsets: a limitation of WLAN localization, pp 53–57Google Scholar
  96. 96.
    Jun J et al. (2013) Social-Loc: improving indoor localization with social sensing. s.l., s.n., p 14Google Scholar
  97. 97.
    Geng X et al. (2013) Hybrid radio-map for noise tolerant wireless indoor localizationGoogle Scholar
  98. 98.
    Kaemarungsi K, Krishnamurthy P (2012) Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive Mob Comput 8:292–316CrossRefGoogle Scholar
  99. 99.
    Farshad A et al. (2013) A microscopic look at WiFi fingerprinting for indoor mobile phone localization in diverse environments. s.l., s.nGoogle Scholar
  100. 100.
    Wen Y et al. (2015) Fundamental limits of RSS fingerprinting based indoor localization. s.l., s.nGoogle Scholar
  101. 101.
    Jiang P et al. (2015) Indoor mobile localization based on Wi-Fi fingerprint’s important access point. Int J Distrib Sens NetwGoogle Scholar
  102. 102.
    Valenzuela R (1993) A ray tracing approach to predicting indoor wireless transmission. In: IEEE 43rd vehicular technology conference, pp 214–218Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Innopolis UniversityInnopolisRussia

Personalised recommendations