A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7622
Keywords:
GNSS, GPS, IoT, RTK, machine learning, deep learning

Main Article Content

Komal Agarwal
Research Scholar, Vishwakarma University Pune, India
Bharati Ainapure
Associate Professor, Vishwakarma University Pune, India
Ashish Shukla
Scientist/Engineer- SG, Space Applications Center, ISRO, Ahmedabad, India

Abstract

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.

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How to Cite
Agarwal, K. ., Ainapure, B. ., & Shukla, A. . (2023). A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 220–230. https://doi.org/10.17762/ijritcc.v11i10s.7622
Issue
Vol. 11 No. 10s (2023)
Section
Articles

References

S. Jin, E. Cardellach, and F. Xie, “GNSS Remote Sensing,” vol. 19, 2014, doi: 10.1007/978-94-007-7482-7.

M. S. Grewal, L. R. (Lawrence R. Weill, and A. P. Andrews, “Global positioning systems, inertial navigation, and integration,” p. 525, 2007, Accessed: Oct. 19, 2021. [Online]. Available: https://books.google.com/books/about/Global_Positioning_Systems_Inertial_Navi.html?id=6P7UNphJ1z8C.

L. N. Thin, L. Y. Ting, N. A. Husna, and M. H. Husin, “GPS systems literature: Inaccuracy factors and effective solutions,” Int. J. Comput. Networks Commun., vol. 8, no. 2, pp. 123–131, 2016, doi: 10.5121/ijcnc.2016.8211.

P. Singal and D. R. S. Chhillar, “A review on GPS and its applications in Computer Science,” Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, pp. 1295–1302, 2014, [Online]. Available: http://ijcsmc.com/docs/papers/May2014/V3I5201499b16.pdf.

M. Fortunato, J. Critchley-Marrows, M. Siutkowska, M. L. Ivanovici, E. Benedetti, and W. Roberts, “Enabling high accuracy dynamic applications in urban environments using PPP and RTK on android multi-frequency and multi-GNSS smartphones,” Eur. Navig. Conf. ENC 2019, no. 1, pp. 1–9, 2019, doi: 10.1109/EURONAV.2019.8714140.

P. Shukla, A. Sehgal, Sonia, and D. Sherawat, “Healthcare Disaster Prediction with IoT, Data Analytics, and Machine Learning,” Glob. Healthc. Disasters Predict. Unpredictable with Emerg. Technol., pp. 71–92, Jan. 2022.

R. E. Guinness, “Beyond where to how: A Machine Learning approach for sensing mobility contexts using smartphone sensors †,” Sensors, vol. 15, pp. 16–20, 2015, doi: 10.3390/s150509962.

H. A. Saleh, “Artificial Intelligence for optimizing the GNSS Carrier Phase-based Positioning.” pp. 407–416, Jan. 24, 2003, Accessed: Oct. 25, 2021. [Online]. Available: http://www.ion.org/publications/abstract.cfm?jp=p&articleID=3784.

M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (80-. )., vol. 349, no. 6245, pp. 255–260, Jul. 2015, doi: 10.1126/SCIENCE.AAA8415.

J. L. Duffany, “Artificial intelligence in GPS navigation systems,” ICSTE 2010 - 2010 2nd Int. Conf. Softw. Technol. Eng. Proc., vol. 1, pp. 382–387, 2010, doi: 10.1109/ICSTE.2010.5608862.

P. Kumar, P. K. Srivastava, P. Tiwari, and R. K. Mall, “Application of GPS and GNSS technology in geosciences,” GPS GNSS Technol. Geosci., pp. 415–427, 2021, doi: 10.1016/b978-0-12-818617-6.00018-4.

A. Siemuri, K. Selvan, H. Kuusniemi, P. Valisuo, and M. E. Elmusrati, “A systematic review of Machine Learning Techniques for GNSS use Cases,” IEEE Trans. Aerosp. Electron. Syst., vol. 58, no. 6, pp. 1–42, 2022, doi: 10.1109/taes.2022.3219366.

S. D. Taabu, F. M. D’Ujanga, and T. Ssenyonga, “Prediction of ionospheric scintillation using neural network over East African region during ascending phase of sunspot cycle 24,” Adv. Sp. Res., vol. 57, no. 7, pp. 1570–1584, Apr. 2016, doi: 10.1016/J.ASR.2016.01.014.

G. Blewitt, “Basics of the GPS Technique: Observation Equations §,” 1997.

D. N. Aloi and M. S. Sharawi, “High fidelity antenna model validation results of a GNSS multipath limiting antenna,” IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 3–14, 2011, doi: 10.1109/TAES.2011.5705655.

J. BESER and B. W. PARKINSON, “The application of NAVSTAR Differential GPS in the Civilian Community,” Navigation, vol. 29, no. 2, pp. 107–136, 1982, doi: 10.1002/j.2161-4296.1982.tb00795.x.

Y. Jiang and Y. Cui, “Study on Differential GPS ( DGPS ): method for reducing the measurement error of CNNS,” vol. 484, pp. 75–80, 2012, doi: 10.4028/www.scientific.net/AMR.482-484.75.

P. J. G. Teunissen and S. Verhagen, “GNSS carrier phase ambiguity resolution: challenges and open problems,” Int. Assoc. Geod. Symp., vol. 133, pp. 785–792, 2009, doi: 10.1007/978-3-540-85426-5_90.

K. J. Manjunath, K. L. Sudha, S. K. Raman, and Vignesam, “Investigation of GDOP for IRNSS,” Proc. 2017 3rd Int. Conf. Appl. Theor. Comput. Commun. Technol. iCATccT 2017, pp. 7–12, 2018, doi: 10.1109/ICATCCT.2017.8389098.

H. U. Kim and T. S. Bae, “Deep learning-based GNSS network-based real-time kinematic improvement for autonomous ground vehicle navigation,” J. Sensors, vol. 2019, 2019, doi: 10.1155/2019/3737265.

L. S. Monteiro, T. Moore, and C. Hill, “What is the accuracy of DGPS?,” J. Navig., vol. 58, no. 2, pp. 207–225, 2005, doi: 10.1017/S037346330500322X.

U. Robustelli, J. Paziewski, and G. Pugliano, “Observation quality assessment and performance of GNSS standalone positioning with code pseudoranges of dual-frequency android smartphones,” Sensors, vol. 21, no. 6, pp. 1–19, 2021, doi: 10.3390/s21062125.

N. Kubo, K. Kobayashi, and R. Furukawa, “GNSS multipath detection using continuous time-series C/N0,” Sensors (Switzerland), vol. 20, no. 14, pp. 1–25, 2020, doi: 10.3390/s20144059.

L. T. Hsu, S. S. Jan, P. D. Groves, and N. Kubo, “Multipath mitigation and NLOS detection using vector tracking in urban environments,” GPS Solut., vol. 19, no. 2, pp. 249–262, Apr. 2015, doi: 10.1007/S10291-014-0384-6.

J. Geng and G. Li, “On the feasibility of resolving Android GNSS carrier-phase ambiguities,” J. Geod., vol. 93, no. 12, pp. 2621–2635, 2019, doi: 10.1007/s00190-019-01323-0.

R. Sun et al., “Improving GPS code phase positioning accuracy in urban environments using Machine Learning,” IEEE Internet Things J., vol. 8, no. 8, pp. 7065–7078, 2021, doi: 10.1109/JIOT.2020.3037074.

L. T. Hsu, “GNSS multipath detection using a machine learning approach,” IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, vol. 2018-March, pp. 1–6, 2018, doi: 10.1109/ITSC.2017.8317700.

B. Xu, Q. Jia, Y. Luo, and L. T. Hsu, “Intelligent GPS L1 LOS/multipath/NLOS classifiers based on correlator-, RINEX- and NMEA-level measurements,” Remote Sens., vol. 11, no. 16, 2019, doi: 10.3390/rs11161851.

Q.-H. Phan, S.-L. Tan, I. Mcloughlin, and D.-L. Vu, “A unified framework for GPS code and carrier-phase multipath mitigation using Support Vector Regression,” Adv. Artif. Neural Syst., vol. 2013, 2013, doi: 10.1155/2013/240564.

Y. Lee and B. Park, “Nonlinear regression-based GNSS multipath modelling in deep urban area,” Mathematics, vol. 10, no. 3, 2022, doi: 10.3390/math10030412.

D. J. Jwo, J. C. Wu, and K. L. Ho, “Support vector machine assisted GPS navigation in limited satellite visibility,” Comput. Mater. Contin., vol. 69, no. 1, pp. 555–574, 2021, doi: 10.32604/CMC.2021.018320.

B. Bidikar, B. Prasad Chapa, M. Vinod Kumar, and G. Sasibhushana Rao, “GPS signal multipath error mitigation technique,” Satell. Mission. Technol. Geosci., Jul. 2020, doi: 10.5772/INTECHOPEN.92295.

B. Guermah, H. El Ghazi, T. Sadiki, and H. Guermah, “A Robust GNSS LOS/Multipath signal classifier based on the fusion of information and machine learning for intelligent transportation systems,” 2018 IEEE Int. Conf. Technol. Manag. Oper. Decis. ICTMOD 2018, no. 1, pp. 94–100, 2018, doi: 10.1109/ITMC.2018.8691272.

G. Bassma, S. Tayeb, and E. G. Hassan, “GNSS positioning enhancement based on NLOS multipath biases estimation using Gaussian Mixture Noise,” Int. J. Mob. Comput. Multimed. Commun., vol. 9, no. 1, pp. 21–39, Jan. 2018, doi: 10.4018/IJMCMC.2018010102.

R. Sun, G. Wang, W. Zhang, L. T. Hsu, and W. Y. Ochieng, “A gradient boosting decision tree based GPS signal reception classification algorithm,” Appl. Soft Comput., vol. 86, p. 105942, Jan. 2020, doi: 10.1016/J.ASOC.2019.105942.

Y. Wang, J. Xu, R. Yang, and X. Zhan, “GNSS multipath detection based on decision tree algorithm in urban canyons,” Lect. Notes Electr. Eng., vol. 773 LNEE, pp. 375–383, 2021, doi: 10.1007/978-981-16-3142-9_35.

Arief Hidayat, Kusworo Adi, Bayu Surarso. (2023). Prediction of Various Computational Parameters using Naive Bayes and Felder and Silverman Methods. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 434–443. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2692

R. Yozevitch, B. Ben Moshe, and A. Weissman, “A robust GNSS LOS/NLOS signal classifier,” Navig. J. Inst. Navig., vol. 63, no. 4, pp. 427–440, Dec. 2016, doi: 10.1002/NAVI.166.

T. Y. Chiou, T. E. Tseng, and A. L. Tao, “Performance of Machine Learning models in determining the GNSS position usage for a loosely coupled GNSS/IMU system,” Proc. 32nd Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2019, pp. 154–174, Sep. 2019, doi: 10.33012/2019.16898.

L. T. Hsu, “GNSS multipath detection using a machine learning approach,” IEEE Conf. Intell. Transp. Syst. Proceedings, ITSC, vol. 2018-March, pp. 1–6, Mar. 2018, doi: 10.1109/ITSC.2017.8317700.

H. Wang, S. Pan, W. Gao, Y. Xia, and C. Ma, “Multipath/NLOS detection based on K-Means Clustering for GNSS/INS tightly coupled system in urban areas,” Micromachines 2022, Vol. 13, Page 1128, vol. 13, no. 7, p. 1128, Jul. 2022, doi: 10.3390/MI13071128.

S. Zhang, “GPS multipath detection and mitigation.”

C. Savas and F. Dovis, “Multipath detection based on K-means Clustering,” Proc. 32nd Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2019, pp. 3801–3811, Sep. 2019, doi: 10.33012/2019.17028.

A. K. Aggarwal, “Enhancement of GPS position accuracy using machine vision and deep learning techniques,” J. Comput. Sci., vol. 16, no. 5, pp. 651–659, 2020, doi: 10.3844/jcssp.2020.651.659.

A. K. Shukla and S. A. Sinha, “Unsupervised Machine Learning approach for multipath classification of NavIC signals,” Proc. 35th Int. Tech. Meet. Satell. Div. Inst. Navig. (ION GNSS+ 2022), pp. 2618–2624, 2022, doi: 10.33012/2022.18439.

M. Socharoentum, H. A. Karimi, and Y. Deng, “A machine learning approach to detect non-line-of-sight GNSS signals in Nav2Nav,” 23 rd ITS World Congr., no. October, pp. 10–14, 2016.

N. I. Ziedan, “Optimized position estimation in multipath environments using Machine Learning,” Proc. 34th Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2021, pp. 3437–3451, Sep. 2021, doi: 10.33012/2021.17880.

W. Vigneau et al., “Neural Networks algorithms prototyping to mitigate GNSS multipath for LEO positioning applications.” pp. 1752–1762, Sep. 29, 2006, Accessed: Feb. 21, 2023. [Online]. Available: http://www.ion.org/publications/abstract.cfm?jp=p&articleID=7068.

P. Ramos-Bosch, M. Hernández-Pajares, J. M. Juan, and J. Sanz, “Real time GPS positioning of LEO satellites mitigating pseudorange multipath through neural networks,” Navigation, vol. 54, no. 4, pp. 309–315, Dec. 2007, doi: 10.1002/J.2161-4296.2007.TB00411.X.

M. Y. Klimenko and A. V. Veitsel, “Evaluation of neural network-based multipath mitigation approach for the GNSS receivers,” 2021 Syst. Signal Synchronization, Gener. Process. Telecommun. SYNCHROINFO 2021 - Conf. Proc., Jun. 2021, doi: 10.1109/SYNCHROINFO51390.2021.9488410.

D. Min, M. Kim, J. Lee, and J. Lee, “Deep neural network based multipath mitigation method for carrier based Differential GNSS Systems,” Proc. Inst. Navig. Pacific Positioning, Navig. Timing Meet. Pacific PNT, vol. 2019-April, pp. 451–466, Apr. 2019, doi: 10.33012/2019.16856.

A. V. Kanhere, S. Gupta, A. Shetty, and G. Gao, “Improving GNSS positioning using neural network-based corrections,” Proc. 34th Int. Tech. Meet. Satell. Div. Inst. Navig. (ION GNSS+ 2021), pp. 3068–3080, 2021, doi: 10.33012/2021.17999.

A. A. Abdallah and Z. M. Kassas, “Deep Learning-aided spatial discrimination for multipath mitigation,” 2020 IEEE/ION Position, Locat. Navig. Symp. PLANS 2020, no. i, pp. 1324–1335, 2020, doi: 10.1109/PLANS46316.2020.9109935.

M. N. de Sousa and R. S. Thomä, “Enhancement of localization systems in nlos urban scenario with multipath ray tracing fingerprints and machine learning,” Sensors (Switzerland), vol. 18, no. 11, 2018, doi: 10.3390/s18114073.

T. Suzuki and Y. Amano, “Nlos multipath classification of gnss signal correlation output using machine learning,” Sensors, vol. 21, no. 7, pp. 1–19, 2021, doi: 10.3390/s21072503.

N. Sokhandan, A. Broumandan, and G. Lachapelle, “GNSS Multipath Mitigation using Low Complexity Adaptive Equalization Algorithms,” undefined, 2014.

Hussain Bukhari, S. N. . (2021). Data Mining in Product Cycle Prediction of Company Mergers . International Journal of New Practices in Management and Engineering, 10(03), 01–05. https://doi.org/10.17762/ijnpme.v10i03.127

S. Kim, J. Byun, and K. Park, “Machine Learning-Based GPS multipath detection method using dual antennas,” Apr. 2022, Accessed: Jun. 08, 2022. [Online]. Available: http://arxiv.org/abs/2204.14001.

M. Orabi, A. A. Abdallah, J. Khalife, and Z. M. Kassas, “A machine learning multipath mitigation approach for opportunistic navigation with 5G signals,” Proc. 34th Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2021, pp. 2895–2909, 2021, doi: 10.33012/2021.17990.

M. Orabi, J. Khalife, A. A. Abdallah, Z. M. Kassas, and S. S. Saab, “A Machine Learning approach for GPS code phase estimation in multipath environments,” 2020 IEEE/ION Position, Locat. Navig. Symp. PLANS 2020, pp. 1224–1229, 2020, doi: 10.1109/PLANS46316.2020.9110155.

Y. Quan, L. Lau, G. W. Roberts, X. Meng, and C. Zhang, “Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning,” Remote Sens., vol. 10, no. 12, 2018, doi: 10.3390/rs10122052.

E. Munin, A. Blais, and N. Couellan, “Convolutional neural network for multipath detection in GNSS receivers,” 2020 Int. Conf. Artif. Intell. Data Anal. Air Transp. AIDA-AT 2020, pp. 1–10, 2020, doi: 10.1109/AIDA-AT48540.2020.9049188.

Y. Tao et al., “Real-Time multipath mitigation in Multi-GNSS short baseline positioning via CNN-LSTM Method,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/6573230.

Christopher Davies, Matthew Martine, Catalina Fernández, Ana Flores, Anders Pedersen. Improving Automated Essay Scoring with Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/173

E. Munin, A. Blais, and N. Couellan, “GNSS multipath detection using embedded Deep CNN on Intel® Neural Compute Stick,” Proc. 33rd Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2020, pp. 2018–2029, Sep. 2020, doi: 10.33012/2020.17654.

A. Blais, N. Couellan, and E. Munin, “A novel image representation of GNSS correlation for deep learning multipath detection,” Array, vol. 14, no. July 2021, p. 100167, 2022, doi: 10.1016/j.array.2022.100167.

A. A. Abdallah, M. Orabi, and Z. M. Kassas, “Multipath mitigation of 5G signals via Reinforcement Learning for navigation in urban environments,” IEEE Veh. Technol. Conf., vol. 2022-June, 2022, doi: 10.1109/VTC2022-SPRING54318.2022.9861015.

Y. Mekonnen, S. Namuduri, L. Burton, A. Sarwat, and S. Bhansali, “Review—Machine Learning techniques in wireless sensor network based precision agriculture,” J. Electrochem. Soc., vol. 167, no. 3, p. 037522, 2020, doi: 10.1149/2.0222003jes.

F. Hussain, R. Hussain, S. A. Hassan, and E. Hossain, “Machine Learning in IoT security: current solutions and future challenges,” IEEE Commun. Surv. Tutorials, vol. 22, no. 3, pp. 1686–1721, Jul. 2020, doi: 10.1109/COMST.2020.2986444.

K. Lakshmanna et al., “A review on deep learning techniques for IoT data,” Electron. 2022, Vol. 11, Page 1604, vol. 11, no. 10, p. 1604, May 2022, doi: 10.3390/ELECTRONICS11101604.

K. Zhang et al., “Compacting Deep Neural Networks for Internet of Things: methods and applications,” IEEE Internet Things J., vol. 8, no. 15, pp. 11935–11959, Aug. 2021, doi: 10.1109/JIOT.2021.3063497.

X. Lai, T. Yang, Z. Wang, and P. Chen, “IoT implementation of Kalman Filter to improve accuracy of air quality monitoring and prediction,” Appl. Sci., vol. 9, no. 9, 2019, doi: 10.3390/app9091831.

L. Chen et al., “Robustness, security and privacy in location-based services for future IoT: A survey,” IEEE Access, vol. 5, pp. 8956–8977, 2017, doi: 10.1109/ACCESS.2017.2695525.

S. Bharati and P. Podder, “Machine and Deep Learning for IoT security and privacy: applications, challenges, and future directions,” Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/8951961.

S. Journal and U. Journal, “International Journal of Engineering & Advanced Technology (IJEAT),” doi: 10.35940/ijeat.F8830.088619.

S. Shaham, M. Ding, B. Liu, S. Dang, Z. Lin, and J. Li, “Privacy preserving location data publishing: A Machine Learning approach,” Feb. 2019, Accessed: May 28, 2023. [Online]. Available: http://arxiv.org/abs/1902.08934.

H. Li, X. Xue, Z. Li, L. Li, and J. Xiong, “Location privacy protection scheme for LBS in IoT,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/9948543.

S. M. Tahsien, H. Karimipour, and P. Spachos, “Machine learning based solutions for security of Internet of Things (IoT): A survey,” J. Netw. Comput. Appl., vol. 161, Jul. 2020, doi: 10.1016/J.JNCA.2020.102630.

E. Nossek, M. Suess, B. Belabbas, and M. Meurer, “Analysis of position and timing solutions for an APNT-system - A look on convergence, accuracy and integrity,” 27th Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS 2014, vol. 4, pp. 3040–3047, 2014.

P.-H. Jau et al., “Adopting Machine Learning to GNSS positioning on MediaTek P60 platform,” Proc. 31st Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS+ 2018, pp. 538–553, Sep. 2018, doi: 10.33012/2018.16000.

C. Isaia and M. P. Michaelides, “A review of wireless positioning techniques and technologies?: from smart sensors to 6G,” pp. 1–43, 2022.

D. Jagiwala and S. N. Shah, “Possibilities of AI algorithm execution in GNSS,” IIT.

J. Breßler, P. Reisdorf, M. Obst, and G. Wanielik, “GNSS positioning in Non-line-of-Sight context — a survey,” pp. 1147–1154, 2016.

J. Zidan et al., “GNSS vulnerabilities and existing solutions: a review of the literature,” doi: 10.1109/ACCESS.2020.2973759.

H. Yu, H. Han, J. Wang, H. Xiao, and C. Wang, “Single-frequency GPS/BDS RTK and INS ambiguity resolution and positioning performance enhanced with positional polynomial fitting constraint,” Remote Sens., vol. 12, no. 15, 2020, doi: 10.3390/RS12152374.

C. Shen et al., “Seamless GPS/Inertial Navigation System based on self-learning square-root Cubature Kalman Filter,” IEEE Trans. Ind. Electron., vol. 68, no. 1, pp. 499–508, 2021, doi: 10.1109/TIE.2020.2967671.

D. Harekal, V. Gs, and S. Ghongade, “Improving GPS / INS signal accuracy using Kalman Filter during gps outages,” vol. 29, no. 3, pp. 14883–14895, 2020.

P. Ramos-Bosch, M. Hernandez-Pajares, J. M. Juan, and J. Sanz, “Real time GPS positioning of LEO Satellites mitigating pseudorange multipath through neural networks,” Navig. J. Inst. Navig., vol. 54, no. 4, pp. 309–315, Dec. 2007, Accessed: Feb. 19, 2022. [Online]. Available: http://www.ion.org/publications/abstract.cfm?jp=j&articleID=102455.

M. Nguyen-H and C. Zhou, “Improving GPS/INS integration through neural networks,” 2010. [Online]. Available: http://sites.google.com/site/journaloftelecommunications/.

D. Bhatt, P. AggarwaL., V. Devabhaktuni, and P. Bhattacharya, “A new source difference artificial neural network for enhanced positioning accuracy,” Meas. Sci. Technol., vol. 23, no. 10, p. 105101, Aug. 2012, doi: 10.1088/0957-0233/23/10/105101.

“Improving Adaptive Kalman Filter in GPS/SDINS integration with Neural Network.” https://www.ion.org/publications/abstract.cfm?articleID=7555 (accessed Feb. 19, 2022).

C. Shen, Y. Zhang, J. Tang, H. Cao, and J. Liu, “Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature Kalman filter and neural networks,” Mech. Syst. Signal Process., vol. 133, p. 106222, 2019, doi: 10.1016/j.ymssp.2019.07.003.

Nader Al Bitar, A. Gavrilov, and W. Khalaf, “Artificial Intelligence based methods for accuracy improvement of Integrated Navigation Systems during GNSS signal outages: An analytical overview,” Gyroscopy Navig., vol. 11, no. 1, pp. 41–58, 2020, doi: 10.1134/S2075108720010022.

J. J. Wang, J. Wang, D. Sinclair, and L. Watts, “Designing a Neural Network for GPS / INS / PL Integration,” no. July, 2006.

A. Noureldin, A. El-Shafie, and M. Bayoumi, “GPS/INS integration utilizing dynamic neural networks for vehicular navigation,” Inf. Fusion, vol. 12, no. 1, pp. 48–57, 2011, doi: 10.1016/j.inffus.2010.01.003.

J. Li, N. Song, G. Yang, M. Li, and Q. Cai, “Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm,” Inf. Fusion, vol. 35, pp. 1–10, 2017, doi: 10.1016/j.inffus.2016.08.001.

C. Ordóñez, J. R. Rodríguez-Pérez, J. J. Moreira, J. M. Matías, and E. Sanz-Ablanedo, “Machine Learning techniques applied to the assessment of GPS accuracy under the forest canopy,” J. Surv. Eng., vol. 137, no. 4, pp. 140–149, 2011, doi: 10.1061/(asce)su.1943-5428.0000049.

I. Belhajem, Y. Ben Maissa, and A. Tamtaoui, “Improving vehicle localization in a smart city with low cost sensor networks and support vector machines,” Mob. Networks Appl., vol. 23, no. 4, pp. 854–863, 2018, doi: 10.1007/s11036-017-0879-9.

H. fa Dai, H. wei Bian, R. ying Wang, and H. Ma, “An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network,” Def. Technol., vol. 16, no. 2, pp. 334–340, 2020, doi: 10.1016/j.dt.2019.08.011.

S. Gite and H. Agrawal, “On context awareness for multisensor data fusion in IoT,” Adv. Intell. Syst. Comput., vol. 381, pp. 85–93, 2016, doi: 10.1007/978-81-322-2526-3_10.

Z. Zou, T. Huang, L. Ye, and K. Song, “CNN based adaptive Kalman Filter in high-dynamic condition for low-cost navigation system on highspeed UAV,” 2020 5th Asia-Pacific Conf. Intell. Robot Syst. ACIRS 2020, pp. 103–108, 2020, doi: 10.1109/ACIRS49895.2020.9162601.

C. Qu, F. B. Sorbelli, R. Singh, P. Calyam, and S. K. Das, “Environmentally-aware and energy-efficient multi-drone coordination and networking for disaster response,” IEEE Trans. Netw. Serv. Manag., pp. 1–1, 2023, doi: 10.1109/TNSM.2023.3243543.

G. Sundhari and D. V. Babu, “Embedded and machine learning based flood monitoring system using IoT,” AIP Conf. Proc., vol. 2523, no. 1, p. 020024, Jan. 2023, doi: 10.1063/5.0111042.

M. S. Abdalzaher, H. A. Elsayed, M. M. Fouda, and M. M. Salim, “Employing Machine Learning and IoT for earthquake early warning system in smart cities,” Energies, vol. 16, no. 1, pp. 1–22, 2023, doi: 10.3390/en16010495.

A. Dabir, R. Solkar, M. Kumbhar, and G. Narayanan, “GPS and IOT equipped smart walking stick,” Proc. 2018 IEEE Int. Conf. Commun. Signal Process. ICCSP 2018, pp. 322–326, Nov. 2018, doi: 10.1109/ICCSP.2018.8524472.

N. Patil and B. Iyer, “Health monitoring and tracking system for soldiers using Internet of Things(IoT),” Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2017, vol. 2017-January, pp. 1347–1352, Dec. 2017, doi: 10.1109/CCAA.2017.8230007.

P. Mahida, S. Shahrestani, and H. Cheung, “Deep learning-based positioning of visually impaired people in indoor environments,” Sensors 2020, Vol. 20, Page 6238, vol. 20, no. 21, p. 6238, Oct. 2020, doi: 10.3390/S20216238.

S. Nayak and R. Patgiri, “A vision on intelligent medical service for emergency on 5G and 6G communication era,” EAI Endorsed Trans. Internet Things, vol. “6,” no. 22, p. 166293, Jul. 2020, doi: 10.4108/EAI.17-8-2020.166293.

A. C. De Araujo and A. Etemad, “End-to-End prediction of parcel delivery time with Deep Learning for smart-city applications,” IEEE Internet Things J., vol. 8, no. 23, pp. 17043–17056, 2021, doi: 10.1109/JIOT.2021.3077007.

S. Potluri, S. N. Mohanty, K. S. Rao, and T. Choudhury, “GPS-based route choice model for smart transportation system: bringing intelligence into vehicular cloud,” Lect. Notes Data Eng. Commun. Technol., vol. 132, pp. 865–878, 2022, doi: 10.1007/978-981-19-2347-0_67/COVER.

[108] J. Fayyad, M. A. Jaradat, D. Gruyer, and H. Najjaran, “Deep learning sensor fusion for autonomous vehicle perception and localization: A review,” Sensors (Switzerland), vol. 20, no. 15, pp. 1–34, 2020, doi: 10.3390/s20154220.

[109] S. Uma and R. Eswari, “Accident prevention and safety assistance using IOT and machine learning,” J. Reliab. Intell. Environ., vol. 8, no. 2, pp. 79–103, Jun. 2022, doi: 10.1007/S40860-021-00136-3.

R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5602–5618, Sep. 2022, doi: 10.1016/J.JKSUCI.2021.05.013.

J. T. Raj and J. Sankar, “IoT based smart school bus monitoring and notification system,” 5th IEEE Reg. 10 Humanit. Technol. Conf. 2017, R10-HTC 2017, vol. 2018-January, pp. 89–92, Feb. 2018, doi: 10.1109/R10-HTC.2017.8288913.

I. Belhajem, Y. Ben Maissa, and A. Tamtaoui, “A robust low cost approach for real time car positioning in a smart city using Extended Kalman Filter and evolutionary machine learning,” Colloq. Inf. Sci. Technol. Cist, vol. 0, pp. 806–811, Jul. 2016, doi: 10.1109/CIST.2016.7804998.

J. O. Payero, A. Mirzakhani-Nafchi, A. Khalilian, X. Qiao, and R. Davis, “Development of a low-cost Internet-of-Things (IoT) system for monitoring soil water potential using Watermark 200SS Sensors,” Adv. Internet Things, vol. 7, no. 03, pp. 71–86, 2017, doi: 10.4236/ait.2017.73005.

D. N. Rewadkar and C. N. Aher, “Vehicle tracking and positioning in GSM network using optimized SVM model,” 2nd Int. Conf. Curr. Trends Eng. Technol. ICCTET 2014, pp. 187–191, Nov. 2014, doi: 10.1109/ICCTET.2014.6966285.

N. G. Rezk, E. E. D. Hemdan, A. F. Attia, A. El-Sayed, and M. A. El-Rashidy, “An efficient iot based smart farming system using machine learning algorithms,” Multimed. Tools Appl., vol. 80, no. 1, pp. 773–797, Jan. 2021, doi: 10.1007/s11042-020-09740-6.

W. S. Kim, W. S. Lee, and Y. J. Kim, “A review of the applications of the Internet of Things (IoT) for agricultural automation,” J. Biosyst. Eng., vol. 45, no. 4, pp. 385–400, Dec. 2020, doi: 10.1007/s42853-020-00078-3.

X. E. Pantazi, D. Moshou, T. Alexandridis, R. L. Whetton, and A. M. Mouazen, “Wheat yield prediction using machine learning and advanced sensing techniques,” Comput. Electron. Agric., vol. 121, pp. 57–65, 2016, doi: 10.1016/j.compag.2015.11.018.

P. Chandra Pandey, A. K. Tripathi, and J. K. Sharma, An evaluation of GPS opportunity in market for precision agriculture. Elsevier Inc., 2021.

S. Mahato, A. Santra, S. Dan, P. Verma, P. Banerjee, and A. Bose, “Visibility anomaly of GNSS satellite and support from regional system,” Curr. Sci., vol. 119, no. 11, pp. 1774–1782, Dec. 2020, doi: 10.18520/cs/v119/i11/1774-1782.

P. Fraga-Lamas, L. Ramos, V. Mondéjar-Guerra, and T. M. Fernández-Caramés, “A review on IoT deep learning UAV systems for autonomous obstacle detection and collision avoidance,” Remote Sens., vol. 11, no. 18, 2019, doi: 10.3390/rs11182144.

J. Paziewski, M. Fortunato, A. Mazzoni, and R. Odolinski, “An analysis of multi-GNSS observations tracked by recent Android smartphones and smartphone-only relative positioning results,” Meas. J. Int. Meas. Confed., vol. 175, p. 109162, 2021, doi: 10.1016/j.measurement.2021.109162.