6,634
Views
9
CrossRef citations to date
0
Altmetric
Articles

A survey of deep learning approaches for WiFi-based indoor positioning

ORCID Icon, ORCID Icon & ORCID Icon
Pages 163-216 | Received 11 Jun 2021, Accepted 25 Aug 2021, Published online: 20 Sep 2021

References

  • Abbas, M., Elhamshary, M., Rizk, H., Torki, M., & Youssef, M. (2019). WiDeep: WiFi-based accurate and robust indoor localization system using deep learning. In 2019 IEEE International Conference on Pervasive Computing and Communications (PERCOM) (pp. 1–10), IEEE.
  • Adege, A. B., Lin, H. P., G. B. Tarekegn, & Jeng, S. S. (2018a). Applying deep neural network (DNN) for robust indoor localization in multi-building environment. Applied Sciences, 8(7), 1062. https://doi.org/https://doi.org/10.3390/app8071062
  • Adege, A. B., Lin, H. P., Tarekegn, G. B., Munaye, Y. Y., & Yen, L. (2018b). An indoor and outdoor positioning using a hybrid of support vector machine and deep neural network algorithms. The Journal of Sensors, 2018(1), 1–12. https://doi.org/https://doi.org/10.1155/2018/1253752
  • Adege, A. B., Yayeh, Y., Berie, G., Lin, H. P., Yen, L., & Li, Y. R. (2018). Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms. In 2018 27th Wireless and Optical Communication Conference (WOCC) (pp. 1–2), IEEE.
  • Adege, A. B., Yen, L., Lin, H. P., Yayeh, Y., Li, Y. R., Jeng, S. S., & Berie, G. (2018). Applying deep neural network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm. In 2018 IEEE International Conference on Applied System Invention (ICASI) (pp. 814–817), IEEE.
  • Aikawa, S., Yamamoto, S., & Morimoto, M. (2018). WLAN finger print localization using deep learning. In 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP) (pp. 541–542), IEEE.
  • Alitaleshi, A., Jazayeriy, H., & Kazemitabar, S. J. (2020). WiFi fingerprinting based floor detection with hierarchical extreme learning machine. In 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) (pp. 113–117), IEEE.
  • Anzum, N., Afroze, S. F., & Rahman, A. (2018). Zone-based indoor localization using neural networks: A view from a real testbed. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–7), IEEE.
  • Bai, J., Sun, Y., Meng, W., & Li, C. (2021). Wi-Fi fingerprint-based indoor mobile user localization using deep learning. Wireless Communications and Mobile Computing, 2021(1), 1–12. https://doi.org/https://doi.org/10.1155/2021/6660990
  • Bai, S., Yan, M., Wan, Q., He, L., Wang, X., & Li, J. (2019). DL-RNN: An accurate indoor localization method via double RNNs. IEEE Sensors Journal, 20(1), 286–295. https://doi.org/https://doi.org/10.1109/JSEN.7361
  • Bai, Y. B., Gu, T., & Hu, A. (2016). Integrating Wi-Fi and magnetic field for fingerprinting based indoor positioning system. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–6), IEEE.
  • Basiouny, Y., Arafa, M., & Sarhan, A. M. (2017). Enhancing Wi-Fi fingerprinting for indoor positioning system using single multiplicative neuron and PCA algorithm. In 2017 12th International Conference on Computer Engineering and Systems (ICCES) (pp. 295–305), IEEE.
  • Basri, C., & El Khadimi, A. (2016). Survey on indoor localization system and recent advances of WIFI fingerprinting technique. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 253–259), IEEE.
  • Battiti, R., Le, N. T., & Villani, A. (2002). Location-Aware Computing: A Neural Network Model For Determining Location In Wireless LANs. In 2002 IEEE Int. Semicond. Conf., IEEE.
  • Belay, A., Yen, L., Renu, S., Lin, H. P., & Jeng, S. S. (2017). Indoor localization at 5 GHz using dynamic machine learning approach (DMLA). In 2017 International Conference on Applied System Innovation (ICASI) (pp. 1763–1766), IEEE.
  • BelMannoubi, S., & Touati, H. (2019). Deep neural networks for indoor localization using WiFi fingerprints. In International Conference on Mobile, Secure, and Programmable Networking (pp. 247–258), Springer.
  • Belmonte-Hernández, A., Hernández-Peñaloza, G., D. M. Gutiérrez, & Alvarez, F. (2019). SWiBluX: Multi-sensor deep learning fingerprint for precise real-time indoor tracking. IEEE Sensors Journal, 19(9), 3473–3486. https://doi.org/https://doi.org/10.1109/JSEN.7361
  • Bernas, M., & Płaczek, B. (2015). Fully connected neural networks ensemble with signal strength clustering for indoor localization in wireless sensor networks. International Journal of Distributed Sensor Networks, 11(12), 403242. https://doi.org/https://doi.org/10.1155/2015/403242
  • Berruet, B., Baala, O., Caminada, A., & Guillet, V. (2018). DelFin: A deep learning based CSI fingerprinting indoor localization in IoT context. In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–8), IEEE.
  • Borenovic, M., Neskovic, A., Budimir, D., & Zezelj, L.. (2008). Utilizing artificial neural networks for WLAN positioning. In 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (pp. 1–5), IEEE.
  • Borenović, M. N., & Nešković, A. M. (2009). Positioning in WLAN environment by use of artificial neural networks and space partitioning. Annals of Telecommunications-Annales des Télécommunications, 64(9), 665–676. https://doi.org/https://doi.org/10.1007/s12243-009-0115-0
  • Cai, C., Deng, L., Zheng, M., & Li, S. (2018). PILC: Passive indoor localization based on convolutional neural networks. In 2018 Ubiquitous Positioning, Indoor Navigation and Location-based Services (UPINLBS) (pp. 1–6), IEEE.
  • Campos, R. S., Lovisolo, L., & de Campos, M. L. R. (2014). Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity. Expert Systems with Applications, 41(14), 6211–6223. https://doi.org/https://doi.org/10.1016/j.eswa.2014.04.011
  • Careem, A. A., Ali, W. H., & Jasim, M. H. (2020). Wirelessly Indoor Positioning System based on RSS Signal. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 238–243), IEEE.
  • Cheerla, S., & Ratnam, D. V. (2018). RSS based Wi-Fi positioning method using multi layer neural networks. In 2018 Conference on Signal Processing and Communication Engineering Systems (SPACES) (pp. 58–61), IEEE.
  • Chen, H., Zhang, Y., Li, W., Tao, X., & Zhang, P. (2017). ConFi: Convolutional neural networks based indoor Wi-Fi localization using channel state information. IEEE Access, 5(1), 18066–18074. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2749516.
  • Chen, Z., Zou, H., Yang, J., Jiang, H., & Xie, L. (2019). WiFi fingerprinting indoor localization using local feature-based deep LSTM. IEEE Systems Journal, 14(2), 3001–3010. https://doi.org/https://doi.org/10.1109/JSYST.4267003
  • Chidlovskii, B., & Antsfeld, L. (2019). Semi-supervised variational autoencoder for WiFi indoor localization. In 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–8), IEEE.
  • Chollet, F. (2018). Deep learning with python. (Vol. 361). Manning.
  • Dai, H., Ying, W h., & Xu, J. (2016). Multi-layer neural network for received signal strength-based indoor localisation. IET Communications, 10(6), 717–723. https://doi.org/https://doi.org/10.1049/cmu2.v10.6
  • Dai, P., Yang, Y., Wang, M., & Yan, R. (2019). Combination of DNN and improved KNN for indoor location fingerprinting. Wireless Communications and Mobile Computing, 2019(1), 1–9. https://doi.org/https://doi.org/10.1155/2019/4283857.
  • Dang, X., Tang, X., Hao, Z., & Ren, J. (2020). Discrete Hopfield neural network based indoor Wi-Fi localization using CSI. EURASIP Journal on Wireless Communications and Networking, 2020(1), 1–16.https://doi.org/https://doi.org/10.1186/s13638-020-01692-7
  • De Vita, F., & Bruneo, D. (2018). A deep learning approach for indoor user localization in smart environments. In 2018 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 89–96), IEEE.
  • Ding, X., Li, H., Li, F., & Wu, J. (2008). A novel infrastructure WLAN locating method based on neural network. In Proceedings of the 4th Asian Conference on Internet Engineering (pp. 47–55), Bangkok.
  • Dinh-Van, N., Nashashibi, F., Thanh-Huong, N., & Castelli, E. (2017). Indoor Intelligent Vehicle localization using WiFi received signal strength indicator. In 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) (pp. 33–36), IEEE.
  • Dou, F., Lu, J., Wang, Z., Xiao, X., Bi, J., & Huang, C. H. (2018). Top-down indoor localization with Wi-fi fingerprints using deep Q-network. In 2018 IEEE 15th International Conference on Mobile ad hoc and Sensor Systems (MASS) (pp. 166–174), IEEE.
  • Elbakly, R., Aly, H., & Youssef, M. (2018). TrueStory: Accurate and robust RF-based floor estimation for challenging indoor environments. IEEE Sensors Journal, 18(24), 10115–10124. https://doi.org/https://doi.org/10.1109/JSEN.2018.2872827
  • Elbakly, R., & Youssef, M. (2020). The StoryTeller: Scalable building-and ap-independent deep learning-based floor prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1–20. https://doi.org/https://doi.org/10.1145/3380979
  • Elbes, M., Almaita, E., Alrawashdeh, T., Kanan, T., AlZu'bi, S., & Hawashin, B.. (2019). An indoor localization approach based on deep learning for indoor location-based services. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 437–441), IEEE.
  • Fahed, D., & Liu, R. (2013). Wi-Fi-based localization in dynamic indoor environment using a dynamic neural network. International Journal of Machine Learning and Computing, 3(1), 127. https://doi.org/https://doi.org/10.7763/IJMLC.2013.V3.286
  • Fang, S. H., & Lin, T. N. (2008). Indoor location system based on discriminant-adaptive neural network in IEEE 802.11 environments. IEEE Transactions on Neural Networks, 19(11), 1973–1978. https://doi.org/https://doi.org/10.1109/TNN.2008.2005494
  • Farid, Z., Nordin, R., Ismail, M., & Abdullah, N. F. (2016). Hybrid indoor-based WLAN-WSN localization scheme for improving accuracy based on artificial neural network. Mobile Information Systems, 2016(1), 6923931. https://doi.org/https://doi.org/10.1155/2016/6923931.
  • Félix, G., Siller, M., & Alvarez, E. N. (2016). A fingerprinting indoor localization algorithm based deep learning. In 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 1006–1011), IEEE.
  • Gan, X., Yu, B., Huang, L., & Li, Y. (2017). Deep learning for weights training and indoor positioning using multi-sensor fingerprint. In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–7), IEEE.
  • Gu, Y., Chen, Y., Liu, J., & Jiang, X. (2015). Semi-supervised deep extreme learning machine for Wi-Fi based localization. Neurocomputing, 166(C), 282–293. https://doi.org/https://doi.org/10.1016/j.neucom.2015.04.011.
  • Guney, S., Erdogan, A., Aktas, M., & Ergun, M. (2020). Wi-Fi based indoor positioning system with using deep neural network. In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP) (pp. 225–228), IEEE.
  • Haider, A., Wei, Y., Liu, S., & Hwang, S. H. (2019). Pre-and post-processing algorithms with deep learning classifier for Wi-Fi fingerprint-based indoor positioning. Electronics, 8(2), 195. https://doi.org/https://doi.org/10.3390/electronics8020195
  • He, S., & Chan, S. H. G. (2015). Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials, 18(1), 466–490. https://doi.org/https://doi.org/10.1109/COMST.2015.2464084
  • He, S., Tan, J., & Chan, S. H.G. (2016). Towards area classification for large-scale fingerprint-based system. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 232–243), ACM.
  • Hinton, G. E. (2009). Deep belief networks. Scholarpedia, 4(5), 5947. https://doi.org/https://doi.org/10.4249/scholarpedia.5947
  • Hoang, M. T., Yuen, B., Dong, X., Lu, T., Westendorp, R., & Reddy, K. (2019). Recurrent neural networks for accurate RSSI indoor localization. IEEE Internet of Things Journal, 6(6), 10639–10651. https://doi.org/https://doi.org/10.1109/JIoT.6488907
  • Hsieh, C. H., Chen, J. Y., & Nien, B. H. (2019). Deep learning-based indoor localization using received signal strength and channel state information. IEEE Access, 7(1), 33256–33267. https://doi.org/https://doi.org/10.1109/ACCESS.2019.2903487.
  • Hsieh, H. Y., Prakosa, S. W., & Leu, J. S.. (2018). Towards the implementation of recurrent neural network schemes for WiFi fingerprint-based indoor positioning. In 2018 IEEE 88th Vehicular technology Conference (VTC-Fall) (pp. 1–5), IEEE.
  • Hsu, C. S., Chen, Y. S., Juang, T. Y., & Wu, Y. T. (2019). An adaptive Wi-Fi indoor localisation scheme using deep learning. International Journal of Ad Hoc and Ubiquitous Computing, 30(4), 265–274. https://doi.org/https://doi.org/10.1504/IJAHUC.2019.098880
  • Hu, X., Chu, L., Pei, J., Liu, W., & Bian, J. (2021). Model complexity of deep learning: A survey. Preprint arXiv:2103.05127.
  • Ibrahim, M., Torki, M., & ElNainay, M. (2018). CNN based indoor localization using RSS time-series. In 2018 IEEE Symposium on Computers and Communications (ISCC) (pp. 01044–01049), IEEE.
  • Jang, J. W., & Hong, S. N. (2018). Indoor localization with WiFi fingerprinting using convolutional neural network. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 753–758), IEEE.
  • Jiang, X., Chen, Y., Liu, J., Gu, Y., & Hu, L. (2018). FSELM: Fusion semi-supervised extreme learning machine for indoor localization with Wi-Fi and Bluetooth fingerprints. Soft Computing, 22(11), 3621–3635. https://doi.org/https://doi.org/10.1007/s00500-018-3171-4
  • Joseph, R., & Sasi, S. B. (2018). Indoor positioning using WiFi fingerprint. In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) (pp. 1–3), IEEE.
  • JunLin, G., Xin, Z., HuaDeng, W., & Lan, Y. (2020). WiFi fingerprint positioning method based on fusion of autoencoder and stacking mode. In 2020 International Conference on Culture-Oriented Science & Technology (ICCST) (pp. 356–361), IEEE.
  • Khassanov, Y., Nurpeiissov, M., Sarkytbayev, A., Kuzdeuov, A., & Varol, H. A. (2021). Finer-level sequential WiFi-based indoor localization. In 2021 IEEE/SICE International Symposium on System Integration (SII) (pp. 163–169), IEEE.
  • Khatab, Z. E., Gazestani, A. H., Ghorashi, S. A., & Ghavami, M. (2021). A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine. Signal Processing, 181(1), 107915. https://doi.org/https://doi.org/10.1016/j.sigpro.2020.107915.
  • Kim, K. S. (2018). Hybrid building/floor classification and location coordinates regression using a single-input and multi-output deep neural network for large-scale indoor localization based on Wi-Fi fingerprinting. In 2018 Sixth International Symposium on Computing and Networking Workshops (CANDARW) (pp. 196–201), IEEE.
  • Kim, K. S., Lee, S., & Huang, K. (2018). A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting. Big Data Analytics, 3(1), 1–17. https://doi.org/https://doi.org/10.1186/s41044-018-0031-2
  • Kim, K. S., Wang, R., Zhong, Z., Tan, Z., Song, H., Cha, J., & Lee, S. (2018). Large-scale location-aware services in access: Hierarchical building/floor classification and location estimation using Wi-Fi fingerprinting based on deep neural networks. Fiber and Integrated Optics, 37(5), 277–289. https://doi.org/https://doi.org/10.1080/01468030.2018.1467515
  • Koike-Akino, T., Wang, P., Pajovic, M., Sun, H., & Orlik, P. V. (2020). Fingerprinting-based indoor localization with commercial MMWave WiFi: A deep learning approach. IEEE Access, 8(1), 84879–84892. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Kozma, R., Alippi, C., Choe, Y., & Morabito, F. C. (2018). Artificial intelligence in the age of neural networks and brain computing. Academic Press.
  • Laoudias, C., Kemppi, P., & Panayiotou, C. G. (2009). Localization using radial basis function networks and signal strength fingerprints in WLAN. In Globecom 2009–2009 IEEE Global Telecommunications Conference (pp. 1–6). IEEE.
  • Le, D. V., Meratnia, N., & Havinga, P. J. (2018). Unsupervised deep feature learning to reduce the collection of fingerprints for indoor localization using deep belief networks. In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–7), IEEE.
  • Lembo, S., Horsmanheimo, S., Somersalo, M., Laukkanen, M., Tuomimäki, L., & Huilla, S. (2019). Enhancing WiFi RSS fingerprint positioning accuracy: Lobe-forming in radiation pattern enabled by an air-gap. In 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–8), IEEE.
  • Li, H., Zeng, X., Li, Y., Zhou, S., & Wang, J. (2019). Convolutional neural networks based indoor Wi-Fi localization with a novel kind of CSI images. China Communications, 16(9), 250–260. https://doi.org/https://doi.org/10.1109/CC.6245522
  • Li, J., Li, Y., & Ji, X. (2016). A novel method of Wi-Fi indoor localization based on channel state information. In 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP) (pp. 1–5), IEEE.
  • Li, N., Chen, J., Yuan, Y., Tian, X., Han, Y., & Xia, M. (2016). A Wi-Fi indoor localization strategy using particle swarm optimization based artificial neural networks. International Journal of Distributed Sensor Networks, 12(3), 4583147. https://doi.org/https://doi.org/10.1155/2016/4583147
  • Li, T., Wang, H., Shao, Y., & Niu, Q. (2018). Channel state information–based multi-level fingerprinting for indoor localization with deep learning. International Journal of Distributed Sensor Networks, 14(10), 1550147718806719. https://doi.org/https://doi.org/10.1177/1550147718806719
  • Li, Y., Gao, Z., He, Z., Zhuang, Y., Radi, A., Chen, R., & El-Sheimy, N. (2019). Wireless fingerprinting uncertainty prediction based on machine learning. Sensors, 19(2), 324. https://doi.org/https://doi.org/10.3390/s19020324
  • Lian, L., Xia, S., Zhang, S., Wu, Q., & Jing, C. (2019). Improved Indoor positioning algorithm using KPCA and ELM. In 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1–5), IEEE.
  • Lin, W. Y., Huang, C. C., Duc, N. T., & Manh, H. N. (2018). Wi-Fi indoor localization based on multi-task deep learning. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (pp. 1–5), IEEE.
  • Liu, C., Wang, C., & Luo, J. (2020). Large-scale deep learning framework on FPGA for fingerprint-based indoor localization. IEEE Access, 8(1), 65609–65617. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Liu, J., Liu, N., Pan, Z., & You, X. (2018). AutLoc: Deep autoencoder for indoor localization with RSS fingerprinting. In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1–6), IEEE.
  • Liu, M., Chen, R., Li, D., Chen, Y., Guo, G., Cao, Z., & Pan, Y. (2017). Scene recognition for indoor localization using a multi-sensor fusion approach. Sensors, 17(12), 2847. https://doi.org/https://doi.org/10.3390/s17122847
  • Liu, W., Chen, H., Deng, Z., Zheng, X., Fu, X., & Cheng, Q. (2020). LC-DNN: Local connection based deep neural network for indoor localization with CSI. IEEE Access, 8(1), 108720–108730. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Liu, Y., Sinha, R. S., Liu, S. Z., & Hwang, S. H. (2020). Side-information-aided preprocessing scheme for deep-learning classifier in fingerprint-based indoor positioning. Electronics, 9(6), 982. https://doi.org/https://doi.org/10.3390/electronics9060982
  • Liu, Z., Dai, B., Wan, X., & Li, X. (2019). Hybrid wireless fingerprint indoor localization method based on a convolutional neural network. Sensors, 19(20), 4597. https://doi.org/https://doi.org/10.3390/s19204597
  • Lu, X., Long, Y., Zou, H., Yu, C., & Xie, L. (2014). Robust extreme learning machine for regression problems with its application to WiFi based indoor positioning system. In 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6), IEEE.
  • Lukito, Y., & Chrismanto, A. R. (2017). Recurrent neural networks model for WiFi-based indoor positioning system. In 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS) (pp. 121–125), IEEE.
  • Ma, Z., Wu, B., & Poslad, S. (2019). A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition. International Journal of Distributed Sensor Networks, 15(4), 1550147719837916. https://doi.org/https://doi.org/10.1177/1550147719837916
  • Mehmood, H., Tripathi, N. K., & Tipdecho, T. (2010). Indoor positioning system using artificial neural network. Journal of Computer Science, 6(10), 1219. https://doi.org/https://doi.org/10.3844/jcssp.2010.1219.1225
  • Mok, E., & Cheung, B. K. (2013). An improved neural network training algorithm for Wi-Fi fingerprinting positioning. ISPRS International Journal of Geo-information, 2(3), 854–868. https://doi.org/https://doi.org/10.3390/ijgi2030854
  • Nabati, M., Navidan, H., Shahbazian, R., Ghorashi, S. A., & Windridge, D. (2020). Using synthetic data to enhance the accuracy of fingerprint-based localization: A deep learning approach. IEEE Sensors Letters, 4(4), 1–4. https://doi.org/https://doi.org/10.1109/LSENS.7782634
  • Nguyen, D. V., De Charette, R., Nashashibi, F., Dao, T. K., & Castelli, E. (2018). WiFi fingerprinting localization for intelligent vehicles in car park. In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–6), IEEE.
  • Nguyen, D. V., Recalde, M. E. V., & Nashashibi, F. (2016). Low speed vehicle localization using WiFi fingerprinting. In 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1–5), IEEE.
  • Nguyen, K. A., Luo, Z., Li, G., & Watkins, C. (2021). A review of smartphones-based indoor positioning: Challenges and applications. IET Cyber-Systems and Robotics, 3(1), 1–30. https://doi.org/https://doi.org/10.1049/csy.v3.1
  • Nowicki, M., & Wietrzykowski, J. (2017). Low-effort place recognition with WiFi fingerprints using deep learning. In International Conference Automation (pp. 575–584), Springer.
  • Ohta, M., Sasaki, J., Takahashi, S., & Yamashita, K. (2015). WiFi positioning system without AP locations for indoor evacuation guidance. In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (pp. 483–484), IEEE.
  • Own, C. M., Hou, J., & Tao, W. (2019). Signal fuse learning method with dual bands WiFi signal measurements in indoor positioning. IEEE Access, 7(1), 131805–131817. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Park, C. U., Shin, H. G., & Choi, Y. H. (2018). A parallel artificial neural network learning scheme based on radio wave fingerprint for indoor localization. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 794–797), IEEE.
  • Qi, G., Jin, Y., & Yan, J. (2018). RSSI-based floor localization using principal component analysis and ensemble extreme learning machine technique. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (pp. 1–5), IEEE.
  • Qian, W., Lauri, F., & Gechter, F. (2019). Convolutional mixture density recurrent neural network for predicting user location with WiFi fingerprints. Preprint arXiv:1911.09344.
  • Rizk, H., Yamaguchi, H., Youssef, M., & Higashino, T. (2020). Gain without pain: Enabling fingerprinting-based indoor localization using tracking scanners. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems (pp. 550–559), ACM.
  • Roy, P., & Chowdhury, C. (2021). Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration. Multimedia Tools and Applications, 80(1), 1–25. https://doi.org/https://doi.org/10.1007/s11042-020-10456-w
  • Shao, W., Luo, H., Zhao, F., Ma, Y., Zhao, Z., & Crivello, A. (2018). Indoor positioning based on fingerprint-image and deep learning. IEEE Access, 6(1), 74699–74712. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Shao, W., Luo, H., Zhao, F., Wang, C., Crivello, A., & Tunio, M. Z. (2018). DePos: Accurate orientation-free indoor positioning with deep convolutional neural networks. In 2018 Ubiquitous Positioning, Indoor Navigation and Location-based Services (UPINLBS) (pp. 1–7), IEEE.
  • Shao, Y., Li, L., & Guo, X. (2019). A semi-supervised deep learning approach towards localization of crowdsourced data. In Proceedings of the ACM Turing Celebration Conference-China (pp. 1–5), ACM.
  • Sinha, R. S., & Hwang, S. H. (2019). Comparison of CNN applications for RSSI-based fingerprint indoor localization. Electronics, 8(9), 989. https://doi.org/https://doi.org/10.3390/electronics8090989
  • Song, X., Fan, X., He, X., Xiang, C., Ye, Q., Huang, X., Fang, G., Chen, L. L., Qin, J., & Wang, Z. (2019). CNNLoc: Deep-learning based indoor localization with WiFi fingerprinting. In 2019 IEEE Smartworld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 589–595), IEEE.
  • Song, X., Fan, X., Xiang, C., Ye, Q., Liu, L., Wang, Z., He, X., Yang, N., & Fang, G. (2019). A novel convolutional neural network based indoor localization framework with WiFi fingerprinting. IEEE Access, 7(1), 110698–110709. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Soro, B., & Lee, C. (2018). Performance comparison of indoor fingerprinting techniques based on artificial neural network. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 0056–0061), IEEE.
  • Soro, B., & Lee, C. (2019). A wavelet scattering feature extraction approach for deep neural network based indoor fingerprinting localization. Sensors, 19(8), 1790. https://doi.org/https://doi.org/10.3390/s19081790
  • Ssekidde, P., Steven Eyobu, O., Han, D. S., & Oyana, T. J. (2021). Augmented CWT features for deep learning-based indoor localization using WiFi RSSI data. Applied Sciences, 11(4), 1806. https://doi.org/https://doi.org/10.3390/app11041806
  • Stella, M., Russo, M., & Begusic, D. (2007). Location determination in indoor environment based on RSS fingerprinting and artificial neural network. In 2007 9th International conference on Telecommunications (pp. 301–306), IEEE.
  • Sun, H., Zhu, X., Liu, Y., & Liu, W. (2020). WiFi based fingerprinting positioning based on Seq2seq model. Sensors, 20(13), 3767. https://doi.org/https://doi.org/10.3390/s20133767
  • Torres-Sospedra, J., Montoliu, R., Martínez-Usó, A., Avariento, J. P., Arnau, T. J., Benedito-Bordonau, M., & Huerta, J. (2014). UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems. In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 261–270), IEEE.
  • Tsai, C. Y., Chou, S. Y., Lin, S. W., & Wang, W. H.. (2008). Location determination of mobile device for indoor WLAN application using neural network. Knowledge and Information Systems, 20(1), 81–93. https://doi.org/https://doi.org/10.1007/s10115-008-0154-2
  • Turabieh, H., & Sheta, A. (2019). Cascaded layered recurrent neural network for indoor localization in wireless sensor networks. In 2019 2nd International Conference on New Trends in COMPUTING Sciences (ICTCS) (pp. 1–6), IEEE.
  • Turgut, Z., Üstebay, S., Aydın, G. Z. G., & Sertbaş, A. (2019). Deep learning in indoor localization using WiFi. In International Telecommunications Conference (pp. 101–110), Springer.
  • Vilović, I., & Zovko-Cihlar, B. (2005). WLAN location determination model based on the artificial neural networks. In Proceedings ELMAR-2005 (pp. 287–290), IEEE.
  • Wang, F., Feng, J., Zhao, Y., Zhang, X., Zhang, S., & Han, J. (2019). Joint activity recognition and indoor localization with WiFi fingerprints. IEEE Access, 7(1), 80058–80068. https://doi.org/https://doi.org/10.1109/Access.6287639.
  • Wang, G., Abbasi, A., & Liu, H. (2021a). Dynamic phase calibration method for CSI-based indoor positioning. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0108–0113), IEEE.
  • Wang, G., Abbasi, A., & Liu, H. (2021b). WiFi-based environment adaptive positioning with transferable fingerprint features. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0123–0128), IEEE.
  • Wang, H., Li, J., Cui, W., Lu, X., Zhang, Z., Sheng, C., & Liu, Q. (2019). Mobile robot indoor positioning system based on k-ELM. The Journal of Sensors, 2019(97), 7547648. https://doi.org/https://doi.org/10.1155/2019/7547648
  • Wang, R., Li, Z., Luo, H., Zhao, F., Shao, W., & Wang, Q. (2019). A robust Wi-Fi fingerprint positioning algorithm using stacked denoising autoencoder and multi-layer perceptron. Remote Sensing, 11(11), 1293. https://doi.org/https://doi.org/10.3390/rs11111293
  • Wang, X. (2019). WiFi fingerprinting based indoor localization: When CSI tensor meets deep residual sharing learning. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
  • Wang, X., Gao, L., & Mao, S. (2015). PhaseFi: Phase fingerprinting for indoor localization with a deep learning approach. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6), IEEE.
  • Wang, X., Gao, L., & Mao, S. (2016). CSI phase fingerprinting for indoor localization with a deep learning approach. IEEE Internet of Things Journal, 3(6), 1113–1123. https://doi.org/https://doi.org/10.1109/JIOT.2016.2558659
  • Wang, X., Gao, L., & Mao, S. (2017). BiLoc: Bi-modal deep learning for indoor localization with commodity 5 GHz WiFi. IEEE Access, 5(1), 4209–4220. https://doi.org/https://doi.org/10.1109/ACCESS.2017.2688362.
  • Wang, X., Gao, L., Mao, S., & Pandey, S. (2015). DeepFi: Deep learning for indoor fingerprinting using channel state information. In 2015 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1666–1671), IEEE.
  • Wang, X., Gao, L., Mao, S., & Pandey, S. (2016). CSI-based fingerprinting for indoor localization: A deep learning approach. IEEE Transactions on Vehicular Technology, 66(1), 763–776.https://doi.org/https://doi.org/10.1109/TVT.2016.2545523
  • Wang, X., Wang, X., & Mao, S. (2017a). CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi. In 2017 IEEE International Conference on Communications (ICC) (pp. 1–6), IEEE.
  • Wang, X., Wang, X., & Mao, S. (2017b). ResLoc: Deep residual sharing learning for indoor localization with CSI tensors. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–6), IEEE.
  • Wang, X., Wang, X., & Mao, S. (2018a). Deep convolutional neural networks for indoor localization with CSI images. IEEE Transactions on Network Science and Engineering, 7(1), 316–327. https://doi.org/https://doi.org/10.1109/TNSE.6488902
  • Wang, X., Wang, X., & Mao, S. (2018b). RF sensing in the internet of things: A general deep learning framework. IEEE Communications Magazine, 56(9), 62–67. https://doi.org/https://doi.org/10.1109/MCOM.2018.1701277
  • Wang, X., Wang, X., Mao, S., Zhang, J., Periaswamy, S. C., & Patton, J. (2020). Indoor radio map construction and localization with deep Gaussian Processes. IEEE Internet of Things Journal, 7(11), 11238–11249. https://doi.org/https://doi.org/10.1109/JIoT.6488907
  • Wang, Y., Gao, J., Li, Z., & Zhao, L. (2020). Robust and accurate Wi-Fi fingerprint location recognition method based on deep neural network. Applied Sciences, 10(1), 321. https://doi.org/https://doi.org/10.3390/app10010321
  • Wu, B. F., Jen, C. L., & Chang, K. C. (2007). Neural fuzzy based indoor localization by Kalman filtering with propagation channel modeling. In 2007 IEEE International Conference on Systems, Man and Cybernetics (pp. 812–817), IEEE.
  • Wu, G. S., & Tseng, P. H. (2018). A deep neural network-based indoor positioning method using channel state information. In 2018 International Conference on Computing, Networking and Communications (ICNC) (pp. 290–294), IEEE.
  • Wu, P., Imbiriba, T., LaMountain, G., Vilà-Valls, J., & Closas, P. (2019). WiFi fingerprinting and tracking using neural networks. In Proceedings of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2019) (pp. 2314–2324), Miami, FL, USA.
  • Xiao, L., Behboodi, A., & Mathar, R. (2017). A deep learning approach to fingerprinting indoor localization solutions. In 2017 27th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1–7), IEEE.
  • Xingli, G., Yaning, L., Ruihui, Z. (2018). Indoor positioning technology based on deep neural networks. In 2018 Ubiquitous Positioning, Indoor Navigation and Location-based Services (UPINLBS) (pp. 1–6), IEEE.
  • Xu, C., Jia, Z., Chen, P., & Wang, B. (2016). CSI-based autoencoder classification for Wi-Fi indoor localization. In 2016 Chinese Control and Decision Conference (CCDC) (pp. 6523–6528), IEEE.
  • Xu, Y., & Sun, Y. (2012). Neural network-based accuracy enhancement method for WLAN indoor positioning. In 2012 IEEE Vehicular Technology Conference (VTC Fall) (pp. 1–5), IEEE.
  • Xu, Y., Zhou, M., & Ma, L. (2009). WiFi indoor location determination via ANFIS with PCA methods. In 2009 IEEE International Conference on Network Infrastructure and Digital Content (pp. 647–651), IEEE.
  • Xue, J., Liu, J., Sheng, M., Shi, Y., & Li, J. (2020). A WiFi fingerprint based high-adaptability indoor localization via machine learning. China Communications, 17(7), 247–259. https://doi.org/https://doi.org/10.1109/CC.6245522
  • Zhang, G., Wang, P., Chen, H., & Zhang, L. (2019). Wireless indoor localization using convolutional neural network and Gaussian process regression. Sensors, 19(11), 2508. https://doi.org/https://doi.org/10.3390/s19112508
  • Zhang, H., Du, H., Ye, Q., & Liu, C. (2019). Utilizing CSI and RSSI to achieve high-precision outdoor positioning: A deep learning approach. In ICC 2019-2019 IEEE International Conference on Communications (ICC) (pp. 1–6), IEEE.
  • Zhang, L., Chen, Z., Cui, W., Li, B., Chen, C., Cao, Z., & Gao, K. (2020). Wifi-based indoor robot positioning using deep fuzzy forests. IEEE Internet of Things Journal, 7(11), 10773–10781. https://doi.org/https://doi.org/10.1109/JIoT.6488907
  • Zhang, M., Jia, J., Chen, J., Deng, Y., Wang, X., & Aghvami, A. H. (2021). Indoor localization fusing WiFi with Smartphone inertial sensors using LSTM networks. IEEE Internet of Things Journal, 8(17), 13608–13623. https://doi.org/https://doi.org/10.1109/JIOT.2021.3067515
  • Zhang, T., & Yi, M. (2018). The enhancement of WiFi fingerprint positioning using convolutional neural network. In Proceedings of the 2018 International Conference on Computer, Communication and Network Technology, Wuzhen, China, 29-30 June 2018.
  • Zhang, W., Liu, K., Zhang, W., Zhang, Y., & Gu, J. (2014). Wi-Fi positioning based on deep learning. In 2014 IEEE International Conference on Information and Automation (ICIA) (pp. 1176–1179), IEEE.
  • Zhang, W., Liu, K., Zhang, W., Zhang, Y., & Gu, J. (2016). Deep neural networks for wireless localization in indoor and outdoor environments. Neurocomputing, 194(C), 279–287. https://doi.org/https://doi.org/10.1016/j.neucom.2016.02.055.
  • Zhang, W., Sengupta, R., Fodero, J., & Li, X. (2017). DeepPositioning: Intelligent fusion of pervasive magnetic field and WiFi fingerprinting for smartphone indoor localization via deep learning. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 7–13), IEEE.
  • Zhang, Z., Lee, M., & Choi, S. (2020). Deep learning-based indoor positioning system using multiple fingerprints. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 491–493), IEEE.
  • Zhao, B., Zhu, D., Xi, T., Jia, C., Jiang, S., & Wang, S. (2019). Convolutional neural network and dual-factor enhanced variational Bayes adaptive Kalman filter based indoor localization with Wi-Fi. Computer Networks, 162(1), 106864. https://doi.org/https://doi.org/10.1016/j.comnet.2019.106864.
  • Zhong, Y., Yuan, Z., Zhao, S., & Luo, X. (2018). A Wifi positioning method based on stack auto encoder. In 2018 7th International Conference on Digital Home (ICDH) (pp. 286–293), IEEE.
  • Zhou, C., & Gu, Y. (2017). Joint positioning and radio map generation based on stochastic variational Bayesian inference for FWIPS. In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–10), IEEE.
  • Zhou, C., & Wieser, A. (2016). Application of backpropagation neural networks to both stages of fingerprinting based WIPS. In 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location based Services (UPINLBS) (pp. 207–217), IEEE.
  • Zhou, R., Hao, M., Lu, X., Tang, M., & Fu, Y. (2018). Device-free localization based on CSI fingerprints and deep neural networks. In 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) (pp. 1–9), IEEE.
  • Zhou, Z., Yu, J., Yang, Z., & Gong, W. (2020). MobiFi: Fast deep-learning based localization using mobile WiFi. In Globecom 2020-2020 IEEE Global Communications Conference (pp. 1–6), IEEE.
  • Zhu, C., Xu, L., Liu, X. Y., & Qian, F. (2018). Tensor-generative adversarial network with two-dimensional sparse coding: Application to real-time indoor localization. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–6), IEEE.
  • Zou, J., Guo, X., Li, L., Zhu, S., & Feng, X. (2018). Deep regression model for received signal strength based WiFi localization. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP) (pp. 1–4), IEEE.