Abstract
Indoor location attracts a lot of attention because people spend their maximum time indoors. As well as the loss of GPS signal power through walls, which requires the integration of other approaches to locate passengers indoors. With the technological growth of telecommunications systems such as 5G and the development of the Internet of Things technique, indoor location is becoming an applicable reality, in order to improve the services offered indoors. In addition, ensuring security in public environments such as airports, train stations, shopping malls, supermarkets… In this paper, we present results acquired during the bibliography phase, before studying more than 78 approaches dominant in the literature. The majority of indoor positioning techniques only use accuracy as a criterion that determines the quality of the approach, while system performance is strongly related to other criteria, such as energy consumption, stability, cost, response time, measurement heterogeneity, environmental change, and others. For this reason, we evaluate the proposed techniques of different categories of indoor positioning systems on these criteria. This document is structured around the following points:
-
We expose the different techniques of indoor positioning
-
We present the different criteria for indoor locations
-
We evaluate indoor positioning techniques
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdelnasser H, Mohamed R, Elgohary A, Alzantot MF, Wang H, Sen S, Choudhury RR, Youssef M (2015) Semantic-slam: Using environment landmarks for unsupervised indoor localization. IEEE Trans Mob Comput 15(7):1770–1782
Alavi B, Pahlavan K (2006) Modeling of the toa-based distance measurement error using uwb indoor radio measurements. IEEE Commun Lett 10(4):275–277
Botsinis P, Alanis D, Feng S, Babar Z, Nguyen HV, Chandra D, Ng SX, Zhang R, Hanzo L (2017) Quantum-assisted indoor localization for uplink mm-wave and downlink visible light communication systems. IEEE Access 5:23327–23351
Dong J, Noreikis M, Xiao Y, A. Yl¨a-J¨a¨aski. Vinav (2018) A vision based indoor navigation system for smartphones. IEEE Transactions on Mobile Computing, 18(6):1461–1475
Guan W, Chen S, Wen S, Tan Z, Song H, Hou W (2020) High-accuracy robot indoor localization scheme based on robot operating system using visible light positioning. IEEE Photonics J 12(2):1–16
Guidi F, Decarli N, D. Dardari, F. Mani, Errico RD (2018) Millimeter-wave beamsteering for passive rfid tag localization. IEEE Journal of Radio Frequency Identification, 2(1):9–14
Hehn M, Sippel E, Carlowitz C, Vossiek M (2018) High-accuracy localization and calibration for 5-dof indoor magnetic positioning systems. IEEE Trans Instrum Meas 68(10):4135–4145
Ji Y, Hejselbæk J, Fan W, Pedersen GF (2018) A map-free indoor localization method using ultrawideband large-scale array systems. IEEE Antennas Wirel Propag Lett 17(9):1682–1686
Kang W, Han Y (2014) Smartpdr: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sens J 15(5):2906–2916
Li H, Qian Z, Tian C, Wang X (2020) Tiloc: Improving the robustness and accuracy for fingerprint-based indoor localization. IEEE Internet Things J 7(4):3053–3066
Li P, Yang X, Yin Y, Gao S, Niu Q (2020) Smartphone-based indoor localization with integrated fingerprint signal. IEEE Access 8:33178–33187
Liu M, Wang H, Yang Y, Zhang Y, Ma L, Wang N (2018) Rfid 3-d indoor localization for tag and tag-free target based on interference. IEEE Trans Instrum Meas 68(10):3718–3732
Luo C, Hong H, Chan MC, Li J, Zhang X, Ming Z (2017) Mpiloc: Self-calibrating multi-floor indoor localization exploiting participatory sensing. IEEE Trans Mob Comput 17(1):141–154
Palacios J, Bielsa G, Casari P, Widmer J (2019) Single-and multiple access point indoor localization for millimeter-wave networks. IEEE Trans Wireless Commun 18(3):1927–1942
Rezazadeh J, Subramanian R, Sandrasegaran K, Kong X, Moradi M, Khodamoradi F (2018) Novel ibeacon placement for indoor positioning in iot. IEEE Sens J 18(24):10240–10247
G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang. Walkiemarkie: Indoor pathway mapping made easy. In Presented as part of the 10th fUSENIXg Symposium on Networked Systems Design and Implementation (fNSDIg 13), pages 85–98, 2013.
Y. Shu, P. Cou´e, Y. Huang, J. Zhang, P. Cheng, and J. Chen. G-loc: Indoor localization leveraging gradient-based fingerprint map. In 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 129–130. IEEE, 2014.
Y. Shu, Y. Huang, J. Zhang, P. Cou´e, P. Cheng, J. Chen, and K. G. Shin. Gradient-based fingerprinting for indoor localization and tracking. IEEE Transactions on Industrial Electronics, 63(4):2424–2433, 2015.
F. Vedadi and S. Valaee. Automatic visual fingerprinting for indoor image-based localization applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017.
Wang C, Luo J, Zheng Y (2018) Optimal target tracking based on dynamic fingerprint in indoor wireless network. IEEE Access 6:77226–77239
Wang Y, Zhao H (2018) Improved smartphone-based indoor pedestrian dead reckoning assisted by visible light positioning. IEEE Sens J 19(8):2902–2908
Yassin A, Nasser Y, Al-Dubai AY, Awad M (2018) Mosaic: Simultaneous localization and environment mapping using mmwave without a-priori knowledge. IEEE Access 6:68932–68947
Yin Z, Jiang X, Yang Z, Zhao N, Chen Y (2017) Wub-ip: A highprecision uwb positioning scheme for indoor multiuser applications. IEEE Syst J 13(1):279–288
Zhang C, Subbu KP, Luo J, Wu J (2014) Groping: Geomagnetism and crowdsensing powered indoor navigation. IEEE Trans Mob Comput 14(2):387–400
Zhao W, Xu L, Qi B, Hu J, Wang T, Runge T (2020) Vivid: Augmenting vision-based indoor navigation system with edge computing. IEEE Access 8:42909–42923
Zhao Y, Xu J, Wu J, Hao J, Qian H (2019) Enhancing camera-based multimodal indoor localization with device-free movement measurement using wifi. IEEE Internet Things J 7(2):1024–1038
Zhou B, Liu A, Lau V (2019) Successive localization and beamforming in 5g mmwave mimo communication systems. IEEE Trans Signal Process 67(6):1620–1635
Zhou X, Chen L, Yan J, Chen R (2020) Accurate doa estimation with adjacent angle power difference for indoor localization. IEEE Access 8:44702–44713
Zou H, Jin M, Jiang H, Xie L, Spanos CJ (2017) Winips: Wifi-based non-intrusive indoor positioning system with online radio map construction and adaptation. IEEE Trans Wireless Commun 16(12):8118–8130
Zuo J, Liu S, Xia H, Qiao Y (2018) Multi-phase fingerprint map based on interpolation for indoor localization using ibeacons. IEEE Sens J 18(8):3351–3359
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ibnatta, Y., Khaldoun, M., Sadik, M. (2022). Exposure and Evaluation of Different Indoor Localization Systems. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_64
Download citation
DOI: https://doi.org/10.1007/978-981-16-1781-2_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1780-5
Online ISBN: 978-981-16-1781-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)