Abstract
With the increasing interests on received signal strength (RSS) fingerprint-based Wi-Fi localization, the requirement of recording reliable and accurate RSS fingerprints for radio map construction becomes a significant concern. The neighbor matching and Bayesian estimation is recognized as the two most representative algorithms for RSS fingerprint-based indoor Wi-Fi localization. To guarantee the accuracy performance of neighbor matching and Bayesian estimation algorithms, we introduce several method to eliminate RSS sample noise for the sake of improving the distance dependency of Wi-Fi RSS fingerprints.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jin, Y., Soh, W., Wong, W.: Indoor localization with channel impulse response based fingerprint and fonparametric regression. IEEE Trans. Wirel. Commun. 9, 1120–1127 (2010)
Zhou, M., Tian, Z., Xu, K., Yu, X., Wu, H.: Error analysis for RADAR neighbor matching localization in linear logarithmic strength varying Wi-Fi environment. Sci. World J. 15 p. Article ID 647370 (2014)
Casas, A.R., Falco, J., Gracia, H., Artigas, J.I., Roy, A.: Location-based services for elderly and disabled people. Comput. Commun. 31, 1055–1066 (2008)
Hazas, M., Hopper, A.: Broadband ultrasonic location systems for improved indoor positioning. IEEE Trans. Mob. Comput. 5, 536–547 (2006)
Steiner, C., Wittneben, A.: Low complexity location fingerprinting with generalized UWB energy detection receivers. IEEE Trans. Signal Process. 58, 1756–1767 (2010)
Hernandez, A., Badorrey, R., Choliz, J., Alastruey, I.: Accurate indoor wireless location with IR UWB systems a performance evaluation of joint receiver structures and TOA based mechanism. IEEE Trans. Consum. Electron. 54, 381–389 (2008)
Silva, R.D.A., Goncalves, P.A.D.S.: Enhancing the efficiency of active RFID-based indoor location systems. In: IEEE Wireless Communications and Networking Conference, pp. 1–6 (2009)
Aparicio, S., Perez, J., Bernardos, A.M., Casar, J.R.: A fusion method based on Bluetooth and WLAN technologies for indoor location. In: IEEE Multi-Sensor Fusion and Integration for Intelligent Systems Conference, pp. 487–491 (2008)
Pan, J.J., Pan, S.J., Yin, J., Ni, L.M., Yang, Q.: Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans. Pattern Anal. Mach. Intell. 34, 587–600 (2012)
Kaemarungsi, K., Krishnamurthy, P.: Modeling of indoor positioning systems based on location fingerprinting. In: IEEE INFOCOM, pp. 1012–1022 (2004)
Zhou, M., Wong, A.K., Tian, Z., Zhang, V.Y., Yu, X., Luo, X.: Adaptive mobility mapping for people tracking using unlabelled Wi-Fi shotgun reads. IEEE Commun. Lett. 17, 87–90 (2013)
Zhou, M., Tian, Z., Xu, K., Yu, X., Wu, H.: Theoretical entropy assessment of fingerprint-based Wi-Fi localization accuracy. Expert Syst. Appl. 40, 6136–6149 (2013)
Kayton, M.: Global positioning system: signals, measurements, and performance. IEEE Aerosp. Electron. Syst. Mag. 17, 36–37 (2002)
Broumandan, A., Nielsen, J., Lachapelle, G.: Indoor GNSS signal acquisition performance using a synthetic antenna array. IEEE Trans. Aerosp. Electron. Syst. Mag. 47, 1337–1350 (2011)
Chen, C., Zhang, X.: Simulation analysis of positioning performance of BeiDou-2 and integrated BeiDou-2/GPS. In: IEEE Communications and Mobile Computing Conference, vol. 2, pp. 505–509 (2010)
Hu, H., Yuan, C.: Performance analysis of Galileo global position system. In: IEEE Power Electronics and Intelligent Transportation System Conference, pp. 396–399 (2009)
Bahl, P., Padmanabhan, V.N.: RADAR: an in-building RF-based user location and tracking system. In: IEEE INFOCOM, pp. 775–784 (2000)
Youssef, M., Agrawala, A.: The horus location determination system. Wirel. Netw. 14, 357–374 (2008)
Figuera, C., Alvarez, J.L.R., Jimenez, I.M., Curieses, A.G.: Time-space sampling and mobile device calibration for WiFi indoor location systems. IEEE Trans. Mob. Comput. 10, 913–926 (2011)
Kaemarungsi, K., Krishnamurthy, P.: Properties of indoor received signal strength for WLAN location fingerprinting. In: IEEE MOBIQUITOUS, pp. 14–23 (2004)
Alasti, H., Xu, K., Dang, Z.: Efficient experimental path loss exponent measurement for uniformly attenuated indoor radio channels. In: IEEE Southeast Conference, pp. 255–260 (2009)
Cura, T.: A parallel local search approach to solving the uncapacitated warehouse location problem. Comput. Ind. Eng. 59, 1000–1009 (2010)
Hansen, T.R., Bardram, J.E., Soegaard, M.: Moving out of the lab: deploying pervasive technologies in a hospital. IEEE Pervasive Comput. 5, 24–31 (2006)
Swangmuang, N., Krishnamurthy, P.: An effective location fingerprint model for wireless indoor localization. Pervasive Mob. Comput. 4, 836–850 (2008)
Zhou, M., Xu, Y., Ma, L., Tian, S.: On the statistical errors of radar location sensor networks with built-in Wi-Fi gaussian linear fingerprints. Sensors 12, 3605–3626 (2012)
Zhou, M., Xu, Y., Tang, L.: Multilayer ANN indoor location system with area division in WLAN environment. J. Syst. Eng. Electron. 21, 914–926 (2010)
Ouyang, R.W., Wong, A.K., Lea, C.T., Chiang, M.: Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning. IEEE Trans. Mob. Comput. 11, 1613–1626 (2012)
Fang, S., Lin, T.: A dynamic system approach for radio location fingerprinting in wireless local area networks. IEEE Trans. Commun. 58, 1020–1025 (2010)
Zhao, Y., Zhou, H., Li, M.: Indoor access points location optimization using differential evolution. In: IEEE CSSE, pp. 382–385 (2008)
Xu, Y., Zhou, M., Meng, W., Ma, L.: Optimal KNN positioning algorithm via theoretical accuracy criterion in WLAN indoor environment. In: IEEE GLOBECOM, pp. 1–5 (2010)
Bahl, P., Padmanabhan, V. N.: Enhancements to the RADAR user location and tracking system, Microsoft Corpration, Technical report, MSR-TR-2000-12
Youssef, M., Agrawala, A., Shankar, A.U.: WLAN location determination via clustering and probability distributions. In: IEEE Pervasive Computing and Communications Conference, pp. 143–151 (2003)
Castro, P., Chiu, P., Kremenek, T., Muntz, R.: A probabilistic room location service for wireless networked environments. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 18–34. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45427-6_3
Kurt, D., Milos, M.: Wireless based object tracking based on neural networks. In: IEEE ICIEA, pp. 308–313 (2008)
Outemzabet, S., Nerguizian, C.: Accuracy enhancement of an indoor ANN-based fingerprinting location system using particle filtering and a low-cost sensor. In: IEEE VTC Spring, pp. 2750–2754 (2008)
Ahmad, U., Gavrilov, A., Lee, S., Lee, Y.: Modular multilayer perceptron for WLAN based localization. In: IEEE IJCNN, pp. 3465–3471 (2006)
Fang, S., Lin, T.: Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments. IEEE Trans. Neural Netw. 19, 1973–1978 (2008)
Golden, S.A., Bateman, S.S.: Sensor measurements for Wi-Fi location with emphasis on time-of-arrival ranging. IEEE Trans. Mob. Comput. 6, 1185–1198 (2007)
Schwalowsky, S., Trsek, H., Exel, R., Kero, N.: System integration of an IEEE 802.11 based TDOA localization system. In: IEEE Precision Clock Synchronization for Measurement Control and Communication Conference, pp. 55–60 (2010)
Nasipuri, A., Li, K.: A directionality based location discovery scheme for wireless sensor networks. In: ACM Wireless Sensor Networks and Applications Conference, vol. 6, pp. 1185–1198 (2002)
Emery, M., Denko, M.K.: IEEE 802.11 WLAN based real-time location tracking in indoor and outdoor environments. In: IEEE CCECE, pp. 1062–1065 (2007)
Ahn, H.S., Yu, W.: Wireless localization networks for indoor service robots. In: IEEE/ASME MESA, pp. 65–70 (2008)
Narzullaev, A., Park, Y.W., Jung, H.Y.: Accurate signal strength prediction based positioning for indoor WLAN systems. In: IEEE/ION PLANS, pp. 685–688 (2008)
Widyawan, Klepal, M., Pesch, D.: Influence of predicted and measured fingerprint on the accuracy of RSSI-based indoor location systems. In: IEEE WPNC, pp. 145–151 (2007)
Borrelli, A., Monti, C., Vari, M., Mazzenga, F.: Channel models for IEEE 802.11b indoor system design. In: IEEE ICC, pp. 3701–3705 (2004)
Acknowledgments
The authors wish to thank the reviewers for the careful review and valuable suggestions. This work is supported in part by the National Natural Science Foundation of China (61771083,61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083), University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), and Postgraduate Scientific Research and Innovation Project of Chongqing (CYS17221).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhou, M., Bulgantamir, O., Wang, Y. (2018). Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-00557-3_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00556-6
Online ISBN: 978-3-030-00557-3
eBook Packages: Computer ScienceComputer Science (R0)