Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

Yan Liang; Song Chen; Xin Dong; Tu Liu

doi:10.1016/j.jnlest.2024.100234

Journal of Electronic Science and Technology ›› 2024, Vol. 22 ›› Issue (1) :100234 DOI: 10.1016/j.jnlest.2024.100234

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Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

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^a School of Computer Engineering, Chengdu Technological University, Chengdu, 611730, China

^b HAN Networks Corporation Limited, Beijing, 100094, China

^* E-mail addresses: lyan3@cdtu.edu.cn (Y. Liang),

chensong_mail@163.com (S. Chen),

dbenecia@qq.com (X. Dong),

lucas.liu@han-networks.com (T. Liu).

Yan Liang was born in Gansu, China in 1986. She received the B.S. degree in software engineering from Wuhan University of Technology, Wuhan, China in 2008 and the M.S. degree in computer application technology from Central China Normal University, Wuhan, China in 2011. Currently, she is a lecturer with the School of Computer Engineering, Chengdu Technological University (CDTU), Chengdu, China. Her research interests include deep reinforcement learning, intelligent computing, and spatial indexing technology.

Song Chen was born in Sichuan, China in 1976. He received the B.S. and M.S. degrees from University of Electronic Science and Technology of China (UESTC), Chengdu, China in 1999 and 2002, respectively. He received the Ph.D. degree in computer application technology from UESTC in 2010. Currently, he is a professor with the School of Computer Engineering, CDTU. His research interests include network management and artificial intelligence applications.

Xin Dong was born in Chongqing, China in 1979. She received the B.S. and M.S. degrees in information engineering from UESTC in 2001 and 2004, respectively. Currently, she is an associate professor with the School of Computer Engineering, CDTU. Her research interests include machine learning, data analysis, and software testing.

Tu Liu was born in Shanxi, China in 1986. He received the B.S. degree in network engineering from Hebei University of Technology, Tianjin, China in 2008. Currently, he is the manager of the Business Software Development Department, HAN Networks Corporation Limited, Beijing, China. His research interests include wireless authentication technology and localization algorithms.

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Abstract

The fingerprinting-based approach using the wireless local area network (WLAN) is widely used for indoor localization. However, the construction of the fingerprint database is quite time-consuming. Especially when the position of the access point (AP) or wall changes, updating the fingerprint database in real-time is difficult. An appropriate indoor localization approach, which has a low implementation cost, excellent real-time performance, and high localization accuracy and fully considers complex indoor environment factors, is preferred in location-based services (LBSs) applications. In this paper, we proposed a fine-grained grid computing (FGGC) model to achieve decimeter-level localization accuracy. Reference points (RPs) are generated in the grid by the FGGC model. Then, the received signal strength (RSS) values at each RP are calculated with the attenuation factors, such as the frequency band, three-dimensional propagation distance, and walls in complex environments. As a result, the fingerprint database can be established automatically without manual measurement, and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods. The proposed indoor localization approach, which estimates the position step by step from the approximate grid location to the fine-grained location, can achieve higher real-time performance and localization accuracy simultaneously. The mean error of the proposed model is 0.36 m, far lower than that of previous approaches. Thus, the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization. It also shows high-accuracy performance with a fast running speed even under a large-size grid. The results indicate that the proposed method can also be suitable for precise marketing, indoor navigation, and emergency rescue.

Keywords

Fine-grained grid computing (FGGC) / Indoor localization / Path loss / Random forest / Reference points (RPs)

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Yan Liang, Song Chen, Xin Dong, Tu Liu. Fine-grained grid computing model for Wi-Fi indoor localization in complex environments. Journal of Electronic Science and Technology, 2024, 22(1): 100234 DOI:10.1016/j.jnlest.2024.100234

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Funding

This work was supported by the Open Project of Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education under Grant No. YYZN-2023-4 and the Ph.D. Fund of Chengdu Technological University under Grant No. 2020RC002.

Declaration of competing interest

The authors declare the following personal relationships which may be considered as competing interests: Tu Liu is currently employed by HAN Networks Corporation Limited, Beijing, China. Other authors declare that there are no competing interests.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	A.R.C. Claridades, J. Lee, Developing a data model of indoor points of interest to support location-based services, J. Sensor, (2020). (Aug. 2020) 8885384:1–16.

[2]	Y.-L. Zhao, J.-Q. Liang, Y.-F. Cui, X.-P. Sha, W.-J. Li, Adaptive 3D position estimation of pedestrians by wearing one ankle sensor, IEEE Sensor. J. 20 (19) (Oct. 2020) 11642-11651.

[3]	M. Yang, B. Ai, R.-S. He, et al., V2V channel characterization and modeling for underground parking garages, China Commun. 16 (9) (Sept. 2019) 93-105.

[4]	A. Ghose, B.-B. Li, S.-Y. Liu, Mobile targeting using customer trajectory patterns, Manage. Sci. 65 (11) (Mar. 2019) 5027-5049.

[5]	A. Bastida-Castillo, C.D. Gómez-Carmona, E. De la Cruz-Sánchez, X. Reche-Royo, S.J. Ibáñez, J.P. Ortega, Accuracy and inter-unit reliability of ultra-wide-band tracking system in indoor exercise, Appl. Sci. 9 (5) (Mar. 2019) 939:1-11.

[6]	L. Ruan, L. Zhang, T. Zhou, Y. Long, An improved Bluetooth indoor positioning method using dynamic fingerprint window, Sensors 20 (24) (Dec. 2020) 7269:1-19.

[7]	V. Bianchi, P. Ciampolini, I. De Munari, RSSI-based indoor localization and identification for ZigBee wireless sensor networks in smart homes, IEEE T. Instrum. Meas. 68 (2) (Feb. 2019) 566-575.

[8]	S.M. Asaad, M.Y. Potrus, K.Z. Ghafoor, H.S. Maghdid, A. Mulahuwaish, Improving positioning accuracy using optimization approaches: a survey, research challenges and future perspectives, Wireless Pers. Commun. 122 (4) (Sept. 2022) 3393-3409.

[9]	Z. Wei, J.-L. Chen, H. Tang, H. Zhang, RSSI-based location fingerprint method for RFID indoor positioning: A review, Nondestr. Test. Eval. (Sept. 2023), https://doi.org/10.1080/10589759.2023.2253493.

[10]	W. Liu, Y.-G. Zhang, Z.-L. Deng, H.-Y. Zhou, Low-cost indoor wireless fingerprint location database construction methods: A review, IEEE Access 11 (Apr. 2023) 37535-37545.

[11]	S.J. Hayward, K. van Lopik, C. Hinde, A.A. West, A survey of indoor location technologies, techniques and applications in industry, Internet Things-Neth 20 (2022) 100608: 1-19.

[12]	M. Jia, S.B.A. Khattak, Q. Guo, X.-M. Gu, Y. Lin, Access point optimization for reliable indoor localization systems, IEEE T. Reliab. 69 (4) (Dec. 2020) 1424-1436.

[13]	X.-N. Li, Z.-C. Dai, L.-M. He, A k-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold, Meas. Sci. Technol. 34 (1) (Oct. 2023) 015108:1-10.

[14]	X.-Y. Tong, Y. Wan, Q.-R. Li, X.-H. Tian, X.-B. Wang, CSI fingerprinting localization with low human efforts, IEEE/ACM T. Network. 29 (1) (Feb. 2021) 372-385.

[15]	Y. Yang, A.-F. Zhou, H.-D. Ma, FineAP: fine-grained access point deployment strategy for 60 GHz millimeter-wave wireless networks, IEEE Commun. Lett. 27 (1) (Jan. 2023) 381-385.

[16]	Y.-Z. Wu, J. Kokkoniemi, C. Han, M. Juntti, Interference and coverage analysis for terahertz networks with indoor blockage effects and line-of-sight access point association, IEEE T. Wirel. Commun. 20 (3) (Mar. 2021) 1472-1486.

[17]	J.-H. Hu, A.-G. Zhang, Z. Chen, X.-P. Jin, Wi-Fi indoor localization based on long short-term memory neural network model of genetic algorithm, in: Proc. of 11th Intl. Conf. on Agro-Geoinformatics, Wuhan, China, (2023), pp. 1-4.

[18]	Y.-Z. Yuan, X. Yang, Q.-X. Lu, Y. Guo, Z.-X. Liu, X.-Y. Luo, An indoor location method based on features optimization for different regions with improved curve smoothness index, IEEE Sens. J. 23 (7) (Apr. 2023) 7362-7370.

[19]	B. Gao, F. Yang, N. Cui, K. Xiong, Y. Lu, Y.-W. Wang, A federated learning framework for fingerprinting-based indoor localization in multibuilding and multifloor environments, IEEE Internet Things 10 (3) (Feb. 2023) 2615-2629.

[20]	D. Csik, Á. Odry, P. Sarcevic, Fingerprinting-based indoor positioning using data fusion of different radiocommunication-based technologies, Machines 11 (2) (Feb. 2023) 302:1-24.

[21]	J.-J. Wang, J. Park, An enhanced indoor positioning algorithm based on fingerprint using fine-grained CSI and RSSI measurements of IEEE 802.11n WLAN, Sensors 21 (8) (Apr. 2021) 2769:1-25.

[22]	B. Jia, B.-Q. Huang, H.-P. Gao, W. Li, L.-F. Hao, Selecting critical WiFi APs for indoor localization based on a theoretical error analysis, IEEE Access 7 (Mar. 2019) 36312-36321.

[23]	W.-X. Xue, K.-G. Yu, Q.-Q. Li, et al., Eight-diagram based access point selection algorithm for indoor localization, IEEE T. Veh. Technol. 69 (11) (Nov. 2020) 13196-13205.

[24]	H.A. Pham, Q.T.T. Nguyen, T.V. Le, An improved weighted k-nearest neighbors algorithm for high accuracy in indoor localization, in: Proc. of 25th Asia-Pacific Conf. on Communications, Ho Chi Minh City, Vietnam, (2019), pp. 24-27.

[25]	B.-Y. Wang, X.-L. Gan, X.-L. Liu, et al., A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning, IEEE Access 8 (Feb. 2020) 30591-30602.

[26]	Y. Tao, L. Zhao, Fingerprint localization with adaptive area search, IEEE Commun. Lett. 24 (7) (Jun. 2020) 1446-1450.

[27]	M. Zhou, Y.-H. Li, M.J. Tahir, X.-L. Geng, Y. Wang, W. He, Integrated statistical test of signal distributions and access point contributions for Wi-Fi indoor localization, IEEE T. Veh. Technol. 70 (5) (May 2021) 5057-5070.

[28]	A. Poulose, D.S. Han, Performance analysis of fingerprint matching algorithms for indoor localization, in: Proc. of Intl. Conf. on Artificial Intelligence in Information and Communication, Fukuoka, Japan, (2020), pp. 661-665.

[29]	Y. Zheng, J.-Y. Liu, M. Sheng, S. Han, Y. Shi, S. Valaee, Toward practical access point deployment for angle-of-arrival based localization, IEEE T. Commun. 69 (3) (Mar. 2021) 2002-2014.

[30]	Y. Zheng, J.-Y. Liu, M. Sheng, S. Valaee, Y. Shi, Obstacle-aware access points deployment for angle-of-arrival based indoor localization, in: Proc. of IEEE Intl. Conf. on Communications, Dublin, Ireland, (2020), pp. 1-6.

[31]	S.-Y. Liu, R. De Lacerda, J. Fiorina, WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping, in: Proc. of IEEE 93rd Vehicular Technology Conf., Helsinki, Finland, (2021), pp. 1-5.

[32]	X.-Y. Wang, The improvement and comparison study of distance metrics for machine learning algorithms for indoor Wi-Fi localization, IEEE Access 11 (Aug. 2023) 85513-85524.

[33]	F. Xu, X.-K. Hu, S.-W. Luo, J.-G. Shang, An efficient indoor Wi-Fi positioning method using virtual location of AP, ISPRS Int. J. Geo-Inf. 9 (4) (Apr. 2020) 261:1-16.

[34]	H.C. Yen, L.Y.O. Yang, Z.M. Tsai, 3-D indoor localization and identification through RSSI-based angle of arrival estimation with real Wi-Fi signals, IEEE T. Microw. Theory 70 (10) (Oct. 2022) 4511-4527.

[35]	X.-L. Yang, Q.-C. Li, M. Zhou, J.-C. Wang, Phase-calibration-based 3-D beamspace matrix pencil algorithm for indoor passive positioning and tracking, IEEE Sens. J. 23 (17) (Sept. 2023) 19670-19683.

[36]	J. Jun, L. He, Y. Gu, et al., Low-overhead WiFi fingerprinting, IEEE T. Mobile Comput. 17 (3) (Mar. 2018) 590-603.

[37]	R.-R. Wang, Z.-H. Li, H.-Y. Luo, F. Zhao, W.-H. Shao, Q. Wang, A robust Wi-Fi fingerprint positioning algorithm using stacked denoising autoencoder and multi-layer perceptron, Remote Sens.-Basel 11 (11) (May 2019) 1293:1-27.