Page 188 - Kaleidoscope Academic Conference Proceedings 2024
P. 188

2024 ITU Kaleidoscope Academic Conference









                                REFERENCES                    [19] S. Ramraj, N. Uzir, R. Sunil, and S. Banerjee, “Experimenting
                                                                 xgboost algorithm for prediction and classification of different
                                                                 datasets,” International Journal of Control Theory and Applica-
                [1] K. C. Rath, A. Khang, and D. Roy, “The role of internet of  tions, vol. 9, no. 40, pp. 651–662, 2016.
                   things (iot) technology in industry 4.0 economy,” in Advanced  [20] A. Kamal, M. G. R. Alam, M. R. Hassan, T. S. Apon, and
                   IoT Technologies and Applications in the Industry 4.0 Digital  M. M. Hassan, “Explainable indoor localization of ble devices
                   Economy. CRC Press, 2024, pp. 1–28.           through rssi using recursive continuous wavelet transformation
                [2] M. Nassereddine and A. Khang, “Applications of internet of  and xgboost classifier,” Future Generation Computer Systems,
                   things (iot) in smart cities,” in Advanced IoT Technologies and  vol. 141, pp. 230–242, 2023.
                   Applications in the Industry 4.0 Digital Economy.  CRC Press,  [21] C. Jain, G. V. S. Sashank, S. Markkandan et al., “Low-cost ble
                   2024, pp. 109–136.                            based indoor localization using rssi fingerprinting and machine
                [3] F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor  learning,” in 2021 sixth international conference on wireless
                   localization systems and technologies,” IEEE Communications  communications, signal processing and networking (WiSPNET).
                   Surveys & Tutorials, vol. 21, no. 3, pp. 2568–2599, 2019.  IEEE, 2021, pp. 363–367.
                [4] J.  Leng,  W.  Sha,  B.  Wang,  P.  Zheng,  C.  Zhuang,  [22] E. Jedari, Z. Wu, R. Rashidzadeh, and M. Saif, “Wi-fi based
                   Q.  Liu,  T.  Wuest,  D.  Mourtzis,  and  L.  Wang,  indoor location positioning employing random forest classifier,”
                   “Industry  5.0:  Prospect  and  retrospect,”  Journal  of  in 2015 international conference on indoor positioning and
                   Manufacturing  Systems,  vol.  65,  pp.  279–295,  2022.  indoor navigation (IPIN). IEEE, 2015, pp. 1–5.
                   [Online].  Available:  https://www.sciencedirect.com/science/  [23] M. T. Hoang, Y. Zhu, B. Yuen, T. Reese, X. Dong, T. Lu,
                   article/pii/S0278612522001662                 R. Westendorp, and M. Xie, “A soft range limited k-nearest
                [5] M. Sharma, A. Tomar, and A. Hazra, “Edge computing for  neighbors algorithm for indoor localization enhancement,” IEEE
                   industry 5.0: fundamental, applications and research challenges,”  Sensors Journal, vol. 18, no. 24, pp. 10 208–10 216, 2018.
                   IEEE Internet of Things Journal, 2024.     [24] R. Wang, H. Luo, Q. Wang, Z. Li, F. Zhao, and J. Huang, “A spa-
                [6] A. Yassin, Y. Nasser, M. Awad, A. Al-Dubai, R. Liu, C. Yuen,  tial–temporal positioning algorithm using residual network and
                   R. Raulefs, and E. Aboutanios, “Recent advances in indoor lo-  lstm,” IEEE Transactions on Instrumentation and Measurement,
                   calization: A survey on theoretical approaches and applications,”  vol. 69, no. 11, pp. 9251–9261, 2020.
                   IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp.  [25] A. P. Dempster, “Upper and lower probabilities induced by a
                   1327–1346, 2016.                              multivalued mapping,” in Classic works of the Dempster-Shafer
                [7] G. I. Hapsari, R. Munadi, B. Erfianto, and I. D. Irawati, “Future  theory of belief functions. Springer, 2008, pp. 57–72.
                   research and trends in ultra-wideband indoor tag localization,”  [26] G. Shafer, A mathematical theory of evidence.  Princeton
                   IEEE Access, 2024.                            university press, 1976, vol. 42.
                [8] C. Lyu, X. Hu, Z. Niu, B. Yang, J. Jin, and C. Ge, “A light-weight  [27] A. Achroufene, A. Chibani, and Y. Amirat, “Using dempster-
                   neural network for marine acoustic signal recognition suitable for  shafer theory for rss-based indoor localization,” pp. 1–8, 2020.
                   fiber-optic hydrophones,” Expert Systems with Applications, vol.  [28] P. Kasebzadeh, G.-S. Granados, and E. S. Lohan, “Indoor local-
                   235, p. 121235, 2024.                         ization via wlan path-loss models and dempster-shafer combin-
                [9] R. Kumar, S. Singh, and V. K. Chaurasiya, “A low-cost and effi-  ing,” pp. 1–6, 2014.
                   cient spatial–temporal model for indoor localization “h-lstmf”,”  [29] R. Kumar, J. Torres-Sospedra, and V. K. Chaurasiya, “Datasets
                   IEEE Sensors Journal, vol. 23, no. 6, pp. 6117–6128, 2023.  for indoor positioning with single-ap wi-fi fingerprinting,” 2023.
               [10] B. Soner and S. Coleri, “Visible light communication based
                   vehicle localization for collision avoidance and platooning,” IEEE
                   Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2167–
                   2180, 2021.
               [11] B. Yang, L. Guo, R. Guo, M. Zhao, and T. Zhao, “A novel
                   trilateration algorithm for rssi-based indoor localization,” IEEE
                   Sensors Journal, vol. 20, no. 14, pp. 8164–8172, 2020.
               [12] I. Toy, A. Durdu, and A. Yusefi, “Localization using two different
                   imu sensor-based dead reckoning system,” in 2024 23rd Interna-
                   tional Symposium INFOTEH-JAHORINA (INFOTEH).  IEEE,
                   2024, pp. 1–5.
               [13] R. Greer, A. Gopalkrishnan, M. Keskar, and M. M. Trivedi,
                   “Patterns of vehicle lights: Addressing complexities of camera-
                   based vehicle light datasets and metrics,” Pattern Recognition
                   Letters, vol. 178, pp. 209–215, 2024.
               [14] B. Wang, Q. Chen, L. T. Yang, and H.-C. Chao, “Indoor smart-
                   phone localization via fingerprint crowdsourcing: Challenges and
                   approaches,” IEEE Wireless Communications, vol. 23, no. 3, pp.
                   82–89, 2016.
               [15] N. Swangmuang and P. Krishnamurthy, “Location fingerprint
                   analyses toward efficient indoor positioning,” in 2008 Sixth
                   Annual IEEE International Conference on Pervasive Computing
                   and Communications (PerCom).  IEEE, 2008, pp. 100–109.
               [16] K. Zia, H. Iram, M. Aziz-ul Haq, and A. Zia, “Comparative study
                   of classification techniques for indoor localization of mobile
                   devices,” pp. 1–5, 2018.
               [17] Y.-Y. Song and L. Ying, “Decision tree methods: applications for
                   classification and prediction,” Shanghai archives of psychiatry,
                   vol. 27, no. 2, p. 130, 2015.
               [18] S. Suthaharan and S. Suthaharan, “Decision tree learning,” Ma-
                   chine Learning Models and Algorithms for Big Data Classifica-
                   tion: Thinking with Examples for Effective Learning, pp. 237–
                   269, 2016.








                                                          – 144 –
   183   184   185   186   187   188   189   190   191   192   193