Wireless Networks

, Volume 25, Issue 3, pp 1385–1402 | Cite as

Indoor navigation systems based on data mining techniques in internet of things: a survey

  • Mahbubeh Sattarian
  • Javad RezazadehEmail author
  • Reza Farahbakhsh
  • Alireza Bagheri


Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.


IoT Indoor navigation system Indoor positioning Data mining Machine learning 


  1. 1.
    Thibaud, M., Chi, H., Zhou, W., & Piramuthu, S. (2018). Internet of things (iot) in high-risk environment, health and safety (ehs) industries: A comprehensive review, Decision Support Systems.Google Scholar
  2. 2.
    Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., Jidhesh, R., & Vadivel, T. (2017). Intelligent face recognition and navigation system using neural learning for smart security in internet of things, Cluster Computing, pp. 1–12.Google Scholar
  3. 3.
    Shu, Y., Shin, K. G., He, T., & Chen, J. (2015). Last-mile navigation using smartphones, In Proceedings of the 21st annual international conference on mobile computing and networking, ser. MobiCom ’15. New York, NY, USA: ACM, 2015, pp. 512–524. [Online]. Available:
  4. 4.
    Xiao, Z., Wen, H., Markham, A., & Trigoni, N. (2014). Lightweight map matching for indoor localisation using conditional random fields, In Proceedings of the 13th international symposium on information processing in sensor networks, ser. IPSN ’14. Piscataway, NJ, USA: IEEE Press, 2014, pp. 131–142. [Online]. Available:
  5. 5.
    Farahzadi, A., Shams, P., Rezazadeh, J., & Farahbakhsh, R. (2017). Middleware technologies for cloud of things-a survey.Digital Communications and Networks, Elsevier.Google Scholar
  6. 6.
    Wlodarczak, P., Ally, M., & Soar, J. (2017). Data mining in iot: Data analysis for a new paradigm on the internet, In Proceedings of the international conference on web intelligence (pp. 1100–1103). ACM, 2017.Google Scholar
  7. 7.
    He, S., Chan, S. H. G., Yu, L., & Liu, N. (April 2015). Fusing noisy fingerprints with distance bounds for indoor localization, In 2015 IEEE conference on computer communications (INFOCOM) (pp. 2506–2514) April 2015.Google Scholar
  8. 8.
    REzazadeh, J., Moradi, M., Ismail, A. S., & Dutkiewicz, E. (2014). Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks. IEEE Sensors Journal, 14, 3052–3064.Google Scholar
  9. 9.
    Fleury, A., Vacher, M., & Noury, N. (2010). Svm-based multimodal classification of activities of daily living in health smart homes: Sensors, algorithms, and first experimental result s. IEEE Transactions on Information Technology in Biomedicine, 14(2), 274–283.Google Scholar
  10. 10.
    Win, M. Z., Conti, A., Mazuelas, S., Shen, Y., Gifford, W. M., Dardari, D., & Chiani, M. (2011). Network localization and navigation via cooperation. IEEE Communications Magazine, vol. 49, no. 5.Google Scholar
  11. 11.
    Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27–34. Scholar
  12. 12.
    Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. New York: Elsevier.zbMATHGoogle Scholar
  13. 13.
    Shearer, C. (2000). The crisp-dm model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13–22.Google Scholar
  14. 14.
    Kumar, S., Qadeer, M. A., & Gupta, A. (2009). Location based services using android (lbsoid). In 2009 IEEE international conference on internet multimedia services architecture and applications (IMSAA) (pp. 1–5) IEEE, 2009.Google Scholar
  15. 15.
    Summers, I. R., & Chanter, C. M. (2002). A broadband tactile array on the fingertip. The Journal of the Acoustical Society of America, 112(5), 2118–2126.Google Scholar
  16. 16.
    Beder, C., & Klepal, M. (2012). Fingerprinting based localisation revisited: A rigorous approach for comparing rssi measurements coping with missed access points and differing antenna attenuations. In 2012 international conference on indoor positioning and indoor navigation (IPIN) (pp. 1–7) IEEE, 2012.Google Scholar
  17. 17.
    Kay, S. M. (1993). Fundamentals of statistical signal processing. Englewood Cliffs: Prentice-Hall.zbMATHGoogle Scholar
  18. 18.
    Shen, Y., Mazuelas, S., & Win, M. Z. (2012). Network navigation: Theory and interpretation. IEEE Journal on Selected Areas in Communications, 30(9), 1823–1834.Google Scholar
  19. 19.
    Paull, L., Saeedi, S., Seto, M., & Li, H. (2014). Auv navigation and localization: A review. IEEE Journal of Oceanic Engineering, 39(1), 131–149.Google Scholar
  20. 20.
    Zheng, Y., Shen, G., Li, L., Zhao, C., Li, M., & Zhao, F. (2017). Travi-navi: Self-deployable indoor navigation system. IEEE/ACM Transactions on Networking, 25(5), 2655–2669.Google Scholar
  21. 21.
    Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Message-efficient localization in mobile wireless sensor networks. Journal of Communication and Computer (JCC), 9(3), 340–344.Google Scholar
  22. 22.
    Yang, Z., Wu, C., Zhou, Z., Zhang, X., Wang, X., & Liu, Y. (2015). Mobility increases localizability: A survey on wireless indoor localization using inertial sensors. ACM Computing Surveys (CSUR), 47(3), 54.Google Scholar
  23. 23.
    Sanchez, J., & Saenz, M. (2008). Orientation and mobility in external spaces for blind apprentices using mobile devices. Mag Ann Metrop Univ, 8, 47–66.Google Scholar
  24. 24.
    Hossain, A. M., & Soh, W.-S. (2015). A survey of calibration-free indoor positioning systems. Computer Communications, 66, 1–13.Google Scholar
  25. 25.
    Sonnenblick, Y. (1998). An indoor navigation system for blind individuals, In Proceedings of the 13th annual conference on technology and persons with disabilities (pp. 215–224).Google Scholar
  26. 26.
    Faragher, R., & Harle, R. (2015). Location fingerprinting with bluetooth low energy beacons. IEEE Journal on Selected Areas in Communications, 33(11), 2418–2428.Google Scholar
  27. 27.
    He, S., Lin, W., & Chan, S.-H. (2016). Indoor localization and automatic fingerprint update with altered ap signals. IEEE Transactions on Mobile Computing, 16, 1897–1910.Google Scholar
  28. 28.
    Halder, S., & Ghosal, A. (2016). A survey on mobile anchor assisted localization techniques in wireless sensor networks. Wireless Networks, 22(7), 2317–2336.Google Scholar
  29. 29.
    Kang, J.-H., Kwon, Y.-M., & Park, K.-J. (2017). Cooperative spatial retreat for resilient drone networks. Sensors, 17(5), 1018.Google Scholar
  30. 30.
    Sukkarieh, S., Nebot, E. M., & Durrant-Whyte, H. F. (2012). The gps aiding of ins. In Field and service robotics. Springer, 2012, 267.Google Scholar
  31. 31.
    Dardari, D., Closas, P., & Djurić, P. M. (2015). Indoor tracking: Theory, methods, and technologies. IEEE Transactions on Vehicular Technology, 64(4), 1263–1278.Google Scholar
  32. 32.
    Kessel, M., & Werner, M. (2011). Smartpos: Accurate and precise indoor positioning on mobile phones. In Proceedings of the first international conference on mobile services, resources, and users, MOBILITY 2011, pp. 158–163.Google Scholar
  33. 33.
    Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics Part C (Applications and Reviews), 37(6), 1067–1080.Google Scholar
  34. 34.
    Durrant-Whyte, H. (2001). A critical review of the state-of-the-art in autonomous land vehicle systems and technology. Albuquerque (NM) and Livermore (CA), USA: SandiaNationalLaboratories, vol. 41.Google Scholar
  35. 35.
    Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Mobile wireless sensor networks overview. International Journal of Computer Communications and Networks, 2(1), 17–22.Google Scholar
  36. 36.
    Rezazadeh, J., Moradi, M., Ismail, A. S., & Dutkiewicz, E. (2015). Impact of static trajectories on localization in wireless sensor networks. Wireless Networks, 21(3), 809–827.Google Scholar
  37. 37.
    Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Fundamental metrics for wireless sensor networks localization. International Journal of Electrical and Computer Engineering (IJECE), 2(4), 452–455.Google Scholar
  38. 38.
    Wang, J., Ghosh, R. K., & Das, S. K. (2010). A survey on sensor localization. Journal of Control Theory and Applications, 8(1), 2–11.zbMATHGoogle Scholar
  39. 39.
    Alrajeh, N. A., Bashir, M., & Shams, B. (2013). Localization techniques in wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, 1–9.Google Scholar
  40. 40.
    Rezazadeh, J., Moradi, M., & Ismail, A. (2011). Efficient localization via middle-node cooperation in wireless sensor networks. In 2011 international conference on electrical, control and computer engineering (INECCE), 2011, pp. 410–415.Google Scholar
  41. 41.
    Moradi, M., Rezazadeh, J., & Ismail, A. S. (2012). A reverse localization scheme for underwater acoustic sensor networks. Sensors, 12, 4352–4380.Google Scholar
  42. 42.
    Chen, Z., Zhu, Q., & Soh, Y. C. (2016). Smartphone inertial sensor-based indoor localization and tracking with ibeacon corrections. IEEE Transactions on Industrial Informatics, 12(4), 1540–1549.Google Scholar
  43. 43.
    Nguyen, K. A. & Luo, Z. (2017). On assessing the positioning accuracy of google tango in challenging indoor environments. In 2017 international conference on indoor positioning and indoor navigation (IPIN) (pp. 1–8). IEEE, 2017.Google Scholar
  44. 44.
    Neges, M., Koch, C., König, M., & Abramovici, M. (2017). Combining visual natural markers and imu for improved ar based indoor navigation. Advanced Engineering Informatics, 31, 18–31.Google Scholar
  45. 45.
    Ryu, J. H., Gankhuyag, G., & Chong, K. T. (2016). Navigation system heading and position accuracy improvement through gps and ins data fusion. Journal of Sensors, 9, 1–9.Google Scholar
  46. 46.
    Hu, P., Wang, S., Zhang, R., Liu, X., & Xu, B. (2017). Fast heading-rotation-based high-accuracy misalignment angle estimation method for ins and gnss. Measurement, 102, 208–213.Google Scholar
  47. 47.
    Yue, Z., Lian, B., & Tang, C. (2017). The gps/ins integrated navigation method based on adaptive ssr-sckf cubature kalman filter. In China satellite navigation conference (pp. 395–405). Springer, 2017.Google Scholar
  48. 48.
    Zhao, J., Pei, F. -j., & Liu, H. -y., (2016). Self-alignment for strapdown ins using real-time adaptive filter. In 2016 IEEE Chinese guidance, navigation and control conference (CGNCC) (pp. 2022–2026). IEEE, 2016.Google Scholar
  49. 49.
    Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27–34.Google Scholar
  50. 50.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.Google Scholar
  51. 51.
    Team, R. C. (2000). R language definition. Vienna, Austria: R foundation for statistical computing.Google Scholar
  52. 52.
    Graczyk, M., Lasota, T., & Trawiński, B. (2009). Comparative analysis of premises valuation models using keel, rapidminer, and weka. In International conference on computational collective intelligence (pp. 800–812). Springer, 2009.Google Scholar
  53. 53.
    Yu, Q., & Shen, Y. (2016). Research of information security risk prediction based on grey theory and anp. In Advanced information management, communicates, electronic and automation control conference (IMCEC) (pp. 107–113). IEEE, 2016.Google Scholar
  54. 54.
    Luo, J., & Bridges, S. M. (2000). Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection. International Journal of Intelligent Systems, 15(8), 687–703.zbMATHGoogle Scholar
  55. 55.
    Guyot, A., Hubert-Moy, L., & Lorho, T. (2018). Detecting neolithic burial mounds from lidar-derived elevation data using a multi-scale approach and machine learning techniques. Remote Sensing, 10(2), 225.Google Scholar
  56. 56.
    Llamas, J., Lerones, P. M., Medina, R., Zalama, E., & Gómez-García-Bermejo, J. (2017). Classification of architectural heritage images using deep learning techniques. Applied Sciences, 7(10), 992.Google Scholar
  57. 57.
    Wang, X., Gao, L., Mao, S., & Pandey, S. (2017). Csi-based fingerprinting for indoor localization: A deep learning approach. IEEE Transactions on Vehicular Technology, 66(1), 763–776.Google Scholar
  58. 58.
    Kumar, A. K. T. R., Schäufele, B., Becker, D., Sawade, O., & Radusch, I. (2016). Indoor localization of vehicles using deep learning, In 2016 IEEE 17th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM) (pp. 1–6). IEEE, 2016.Google Scholar
  59. 59.
    Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.Google Scholar
  60. 60.
    Siddiqa, A., Hashem, I. A. T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., et al. (2016). A survey of big data management: Taxonomy and state-of-the-art. Journal of Network and Computer Applications, 71, 151–166.Google Scholar
  61. 61.
    Gui, L., Yang, M., Fang, P., & Yang, S. (2017). Rss-based indoor localization using multiplicative distance-correction factor. IET Wireless Sensor Systems, 7, 98–104.Google Scholar
  62. 62.
    Yassin, A., Nasser, Y., Awad, M., Al-Dubai, A., Liu, R., Yuen, C., et al. (2016). Recent advances in indoor localization: A survey on theoretical approaches and applications. IEEE Communications Surveys & Tutorials., 19, 1327–1346.Google Scholar
  63. 63.
    Zhu, C., Leung, V. C., Shu, L., & Ngai, E. C.-H. (2015). Green internet of things for smart world. IEEE Access, 3, 2151–2162.Google Scholar
  64. 64.
    Zafari, F., Papapanagiotou, I., Devetsikiotis, M., & Hacker, T. J. (2017). Enhancing the accuracy of ibeacons for indoor proximity-based services. In IEEE ICC.Google Scholar
  65. 65.
    Chen, L. -K., & Hong, Y. (2016). A vision-based indoor positioning method with high accuracy and efficiency based on self-optimized-ordered visual vocabulary. In 2016 IEEE/ION position, location and navigation symposium (PLANS) (pp. 48–56). IEEE, 2016.Google Scholar
  66. 66.
    Wang, H., Zhao, Z., Hu, J., Qu, Z., & Feng, H. (2016). Study on improvement of fingerprint matching algorithm in wireless lan based indoor positioning system. In 2016 17th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD) (pp. 275–280). IEEE, 2016.Google Scholar
  67. 67.
    Yun, S., Yao, Z., Wang, T., & Lu, M. (2016). High accuracy and fast acquisition algorithm for pseudolites-based indoor positioning systems. In 2016 fourth international conference on ubiquitous positioning, indoor navigation and location based services (UPINLBS) (pp. 51–60). IEEE, 2016.Google Scholar
  68. 68.
    Pérez, S., Laperrière, V., Borderon, M., Padilla, C., Maignant, G., & Oliveau, S. (2016). Evolution of research in health geographics through the international journal of health geographics (2002–2015). International Journal of Health Geographics, 15(1), 3.Google Scholar
  69. 69.
    Khalajmehrabadi, A., Gatsis, N., & Akopian, D. (2016). Modern wlan fingerprinting indoor positioning methods and deployment challenges. arXiv preprint arXiv:1610.05424.
  70. 70.
    George, J., Kumar, V., & Kumar, S. (2015). Data warehouse design considerations for a healthcare business intelligence system. In World congress on engineering Google Scholar
  71. 71.
    Lee, C. K., Ho, W., Ho, G. T., & Lau, H. C. (2011). Design and development of logistics workflow systems for demand management with rfid. Expert Systems With Applications, 38(5), 5428–5437.Google Scholar
  72. 72.
    Peng, Y., Kou, G., Wang, G., Wu, W., & Shi, Y. (2011). Ensemble of software defect predictors: An ahp-based evaluation method. International Journal of Information Technology & Decision Making, 10(01), 187–206.Google Scholar
  73. 73.
    García-Osorio, C., de Haro-García, A., & García-Pedrajas, N. (2010). Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts. Artificial Intelligence, 174(5), 410–441.MathSciNetGoogle Scholar
  74. 74.
    Mishra, D. P., Samantaray, S. R., & Joos, G. (2016). A combined wavelet and data-mining based intelligent protection scheme for microgrid. IEEE Transactions on Smart Grid, 7(5), 2295–2304.Google Scholar
  75. 75.
    Gu, Y., Song, Q., Ma, M., Li, Y., & Zhou, Z. (2016). Using ibeacons for trajectory initialization and calibration in foot-mounted inertial pedestrian positioning systems. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–7). IEEE, 2016.Google Scholar
  76. 76.
    Kazienko, P., Musial, K., & Kajdanowicz, T. (2011). Multidimensional social network in the social recommender system. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(4), 746–759.Google Scholar
  77. 77.
    Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. Science, 344(6191), 1492–1496.Google Scholar
  78. 78.
    He, S., & Chan, S.-H. G. (2016). Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials, 18(1), 466–490.Google Scholar
  79. 79.
    Vossen, G., Dillon, S., Schomm, F., & Stahl, F. (2017). A classification framework for beacon applications. Open Journal of Internet of Things (OJIOT), 3(1), 1–11.Google Scholar
  80. 80.
    Kushki, A., Plataniotis, K. N., & Venetsanopoulos, A. N. (2010). Intelligent dynamic radio tracking in indoor wireless local area networks. IEEE Transactions on Mobile Computing, 9(3), 405–419.Google Scholar
  81. 81.
    Han, D., Jung, S., Lee, M., & Yoon, G. (2014). Building a practical wi-fi-based indoor navigation system. IEEE Pervasive Computing, 13(2), 72–79.Google Scholar
  82. 82.
    Awang, A., & Agarwal, S. (2015). Data aggregation using dynamic selection of aggregation points based on rssi for wireless sensor networks. Wireless Personal Communications, 80(2), 611–633.Google Scholar
  83. 83.
    Park, J., Ryu, J., & Yang, S. -B. Activedbc: Learning knowledge-based information propagation in mobile social networks. Wireless Networks pp. 1–13.Google Scholar
  84. 84.
    Di Taranto, R., Muppirisetty, S., Raulefs, R., Slock, D., Svensson, T., & Wymeersch, H. (2014). Location-aware communications for 5g networks: How location information can improve scalability, latency, and robustness of 5g. IEEE Signal Processing Magazine, 31(6), 102–112.Google Scholar
  85. 85.
    Oh, J. -H., Back, M. -K., Oh, G. -T., & Lee, K. -C. (2016). A study on dds-based ble profile adaptor for solving ble data heterogeneity in internet of things. In International conference on computer science and its applications (pp. 619–624) Springer, 2016.Google Scholar
  86. 86.
    Shi, D., Zurada, J., & Guan, J. (2014). A neuro-fuzzy approach to bad debt recovery in healthcare, In System Sciences (HICSS), 2014 47th Hawaii International Conference on IEEE, 2014, pp. 2888–2897.Google Scholar
  87. 87.
    Tsai, C.-W., Lai, C.-F., Chiang, M.-C., Yang, L. T., et al. (2014). Data mining for internet of things: A survey. IEEE Communications Surveys and Tutorials, 16(1), 77–97.Google Scholar
  88. 88.
    Różewski, P., & Małachowski, B. (2009). Competence management in knowledge-based organisation: case study based on higher education organisation, In International conference on knowledge science, engineering and management (pp. 358–369). Springer, 2009 .Google Scholar
  89. 89.
    Shokri, R., Theodorakopoulos, G., Troncoso, C., Hubaux, J. -P. & Le Boudec, J. -Y. (2012). Protecting location privacy: Optimal strategy against localization attacks. In Proceedings of the 2012 ACM conference on computer and communications security (pp. 617–627). ACM, 2012.Google Scholar
  90. 90.
    Cucoranu, I. C., Parwani, A. V., West, A. J., Romero-Lauro, G., Nauman, K., Carter, A. B., et al. (2013). Privacy and security of patient data in the pathology laboratory. Journal of Pathology Informatics, 4(1), 4.Google Scholar
  91. 91.
    Junior, N. F., Silva, A., Guelfi, A., & Kofuji, S. T. (2017). Iot6sec: Reliability model for internet of things security focused on anomalous measurements identification with energy analysis. Wireless Networks pp. 1–24.Google Scholar
  92. 92.
    Zhang, W., Liu, K., Zhang, W., Zhang, Y., & Gu, J. (2016). Deep neural networks for wireless localization in indoor and outdoor environments. Neurocomputing, 194, 279–287.Google Scholar
  93. 93.
    Kopetz, H. (2011). Internet of things. In Real-time systems (pp. 307–323). Springer, 2011.Google Scholar
  94. 94.
    Pei, L., Zhang, M., Zou, D., Chen, R., & Chen, Y. (2016). A survey of crowd sensing opportunistic signals for indoor localization. Mobile Information Systems, 2016, 1–16.Google Scholar
  95. 95.
    Liao, J. -K., Chiang, K. -W., Tsai, G. -J., & Chang, H. -W. (2016). A low complexity map-aided fuzzy decision tree for pedestrian indoor/outdoor navigation using smartphone. In 2016 international conference on indoor positioning and indoor navigation (IPIN) (pp. 1–8). IEEE, 2016.Google Scholar
  96. 96.
    Arslan, O., Guralnik, D. P., & Koditschek, D. E. (2016). Coordinated robot navigation via hierarchical clustering. IEEE Transactions on Robotics, 32(2), 352–371.Google Scholar
  97. 97.
    Alhajri, M., Alsindi, N., Ali, N., & Shubair, R. (2016). Classification of indoor environments based on spatial correlation of rf channel fingerprints. In 2016 IEEE international symposium on antennas and propagation (APSURSI) (pp. 1447–1448). IEEE, 2016.Google Scholar
  98. 98.
    Ahmadi, H., Viani, F., Polo, A., & Bouallegue, R. (2016). An accurate ensemble-based wireless localization strategy for wireless sensor networks, In 2016 24th international conference on software, telecommunications and computer networks (SoftCOM) (pp. 1–5). IEEE, 2016.Google Scholar
  99. 99.
    Xin, H., Zhi, X., Jianxin, X., & Limin, X. (2017). Research on pedestrian navigation system aided by indoor geomagnetic matching. In 2017 29th Chinese control and decision conference (CCDC) (pp. 1946–1951). IEEE, 2017.Google Scholar
  100. 100.
    Tabrizi, S. S., & Cavus, N. (2016). A hybrid knn-svm model for iranian license plate recognition. Procedia Computer Science, 102, 588–594.Google Scholar
  101. 101.
    Gonzalez, E. J., Luo, C., Shrivastava, A., Palem, K., Moon, Y., Noh, S., et al. (2017). Location detection for navigation using imus with a map through coarse-grained machine learning, In 2017 design, automation & test in Europe conference & exhibition (DATE) (pp. 500–505). IEEE, 2017.Google Scholar
  102. 102.
    Chen, R. and (2017). A study of gps/ins integrated navigation with artificial neural network and k-means aided kalman filter. HKU Theses Online (HKUTO).Google Scholar
  103. 103.
    Xiao, Z., Zhan, S., Xiang, Z., Wang, D., & Chen, W. (2016). A gpr-pso incremental regression framework on gps/ins integration for vehicle localization under urban environment. In 2016 IEEE 27th annual international symposium on personal, indoor, and mobile radio communications (PIMRC) (pp. 1–6). IEEE, 2016.Google Scholar
  104. 104.
    Wang, W., & Ku, W.-S. (2017). Dynamic indoor navigation with bayesian filters. SIGSPATIAL Special, 8(3), 9–10.Google Scholar
  105. 105.
    Haq, M. A. U., Kamboh, H. M. A., Akram, U., Sohail, A., & Iram, H. (2016). Indoor localization using improved multinomial naïve bayes technique. In International afro-European conference for industrial advancement (pp. 321–329) .Springer, 2016.Google Scholar
  106. 106.
    Wu, Z., Xu, Q., Li, J., Fu, C., Xuan, Q., & Xiang, Y. (2017). Passive indoor localization based on csi and naive bayes classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49, 1–12.Google Scholar
  107. 107.
    Perera, L. P., & Mo, B. (2016). Data compression of ship performance and navigation information under deep learning. In Proceedings of the 35th international conference on ocean, offshore and arctic engineering (OMAE 2016), Busan, Korea, (OMAE2016-54093).Google Scholar
  108. 108.
    Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017). Target-driven visual navigation in indoor scenes using deep reinforcement learning. In 2017 IEEE international conference on robotics and automation (ICRA)(pp. 3357–3364). IEEE, 2017.Google Scholar
  109. 109.
    Pratiba, D., & Shobha, G. (2014). A survey of resource sharing cloud using data mining, In 2014 fifth international conference on signal and image processing (ICSIP) (pp. 323–327). IEEE, 2014.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Tehran North BranchIslamic Azad UniversityTehranIran
  2. 2.University of Technology SydneyUltimoAustralia
  3. 3.Institut Mines-Telecom, Telecom Sud-ParisEvryFrance
  4. 4.Amirkabir University of TechnologyTehranIran

Personalised recommendations