Volume 6 , Issue 2 , PP: 101-107, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Mohammad Alshehri 1 *
Doi: https://doi.org/10.54216/JCIM.060203
Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.
Indoor localization, Smartphones, Tracking model, GSO algorithm, Kalman filter, Estimation error
[1] Wang, X., Yu, Z. and Mao, S., 2018, May. DeepML: Deep LSTM for indoor localization with smartphone magnetic and light sensors. In 2018 IEEE international conference on communications (ICC) (pp. 1-6). IEEE.
[2] Murata, M., Ahmetovic, D., Sato, D., Takagi, H., Kitani, K.M. and Asakawa, C., 2018, March. Smartphone-based indoor localization for blind navigation across building complexes. In 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 1-10). IEEE.
[3] Seco, F. and Jiménez, A.R., 2018. Smartphone-based cooperative indoor localization with RFID technology. Sensors, 18(1), p.266.
[4] Ashraf, I., Hur, S., Park, S. and Park, Y., 2020. DeepLocate: Smartphone based indoor localization with a deep neural network ensemble classifier. Sensors, 20(1), p.133.
[5] Kang, J., Lee, J. and Eom, D.S., 2018. Smartphone-based traveled distance estimation using individual walking patterns for indoor localization. Sensors, 18(9), p.3149.
[6] Wang, G., Wang, X., Nie, J. and Lin, L., 2019. Magnetic-based indoor localization using smartphone via a fusion algorithm. IEEE Sensors Journal, 19(15), pp.6477-6485.
[7] Poulose, A., Kim, J. and Han, D.S., 2019. A sensor fusion framework for indoor localization using smartphone sensors and Wi-Fi RSSI measurements. Applied Sciences, 9(20), p.4379.
[8] Ashraf, I., Hur, S. and Park, Y., 2019. Application of deep convolutional neural networks and smartphone sensors for indoor localization. Applied Sciences, 9(11), p.2337.
[9] Gu, F., Khoshelham, K., Shang, J., Yu, F. and Wei, Z., 2017. Robust and accurate smartphone-based step counting for indoor localization. IEEE Sensors Journal, 17(11), pp.3453-3460.
[10] Luo, J. and Fu, L., 2017. A smartphone indoor localization algorithm based on WLAN location fingerprinting with feature extraction and clustering. Sensors, 17(6), p.1339.
[11] Zhang, W., Sengupta, R., Fodero, J. and Li, X., 2017, December. 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.
[12] Ashraf, I., Hur, S., Shafiq, M., Kumari, S. and Park, Y., 2019. GUIDE: Smartphone sensorsābased pedestrian indoor localization with heterogeneous devices. International Journal of Communication Systems, 32(15), p.e4062.
[13] Ciabattoni, L., Foresi, G., Monteriù, A., Pepa, L., Pagnotta, D.P., Spalazzi, L. and Verdini, F., 2019. Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons. Journal of Ambient Intelligence and Humanized Computing, 10(1), pp.1-12.
[14] Chen, J., Zhang, Y. and Xue, W., 2018. Unsupervised indoor localization based on Smartphone Sensors, iBeacon and Wi-Fi. Sensors, 18(5), p.1378.
[15] Li, P., Yang, X., Yin, Y., Gao, S. and Niu, Q., 2020. Smartphone-based indoor localization with integrated fingerprint signal. IEEE Access, 8, pp.33178-33187.
[16] del Horno, M.M., Orozco-Barbosa, L. and García-Varea, I., 2021. A smartphone-based multimodal indoor tracking system. Information Fusion, 76, pp.36-45.
[17] Vy, T.D., Nguyen, T.L. and Shin, Y., 2021, April. Pedestrian Indoor Localization and Tracking Using Hybrid Wi-Fi/PDR for iPhones. In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (pp. 1-7). IEEE.
[18] Roy, P., Chowdhury, C., Kundu, M., Ghosh, D. and Bandyopadhyay, S., 2021. Novel weighted ensemble classifier for smartphone based indoor localization. Expert Systems with Applications, 164, p.113758.
[19] Roy, P. and Chowdhury, C., 2021. Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration. Multimedia Tools and Applications, 80(13), pp.20501-20525.
[20] Salimibeni, M., Hajiakhondi-Meybodi, Z., Malekzadeh, P., Atashi, M., Plataniotis, K.N. and Mohammadi, A., 2021, January. IoT-TD: IoT dataset for multiple model BLE-based indoor localization/tracking. In 2020 28th European Signal Processing Conference (EUSIPCO) (pp. 1697-1701). IEEE.
[21] Gobi, R., 2021. Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter. Multimedia Tools and Applications, 80(10), pp.15377-15390.
[22] Jin, Y., Hou, W., Li, G. and Chen, X., 2017. A glowworm swarm optimization-based maximum power point tracking for photovoltaic/thermal systems under non-uniform solar irradiation and temperature distribution. Energies, 10(4), p.541.