Manuscript Number : IJSRSET207647

IoT based Smart Vehicle Monitoring System : A Systematic Literature Review

Authors(1) :-Tanvir Rahman

This paper provides a complete over view of the current research state of Smart vehicle tracking System with GPS and cellular network. This paper consists of several review aiming to reveal the relevance and methodologies of this research area and create a foundation for future work. In this paper an advanced vehicle observation and IOT based tracking system and autopilot navigation system based on Machine Learning and neural Networking is proposed with all possible scientific validations of the model. The primary purpose of monitoring the vehicles which are moving from one place to the other in order to provide better A.I based autopilot navigation system, safety and security. The proposed method Combined the idea of Java programming, Neural networking concept with machine learning capability processing data with MediaTek mobile processor and its sophisticated features of storing data into several databases. Google Map Engine API v3 was used to display and sense the graphical images of the map and a Vision recognition server system is used to compare and represent the map API in a more realistic look. The proposed project includes the implementation of Global Positioning System (GPS), GPRS and GSM technology for vehicle tracking and monitoring on real time basic purpose using SIM module.[3] The GPS receiver installed o tracking device provides real-time Geolocation Co-ordinate of site of the vehicle; 3 adjacent GSM cellphone tower stations will continuously broadcast co-ordinate of locations and the GPRS technology with TCP based protocol sends the tracking information to the central Monitoring and Imaging server which consist of 3 child servers i)data processing sever, ii) Image and vision based server and iii)A.I. based machine learning server calculate data and minimize the information and maps with the help of Google map API and thus an decision message for next Move/driving path is generated and transmitted to Smart Controlling Device to execute the instructions and to display it in the Monitor of car display and Integrated logged-IN andriod based Google Map API version 3 app on real time basic. Hence, this system will monitor all the driving steps of the driver and provide the real time driving suggestions and feedback to the driver to ensure smooth and safe driving experience. The sensors like temperature sensor ,altitude sensor and smoke sensor send data to the neural processing Server which diagnoses the health and safety measures of the vehicles and generates a report on Car display and andriod App interface if any risk issue is found by sensors.

Authors and Affiliations

Tanvir Rahman
Department of computer Science and Engineering (CSE), Stamford University, Bangladesh, Dhaka, Bangladesh

GPS, Autopilot system, GPRS, Machine learning, Safe-driving, wireless communication

  1. MihirGarude, NirmalHaldikar (September 2014) Real Time Position Tracking System Using GoogleMaps APIV3, Volume4, Issue9, edn., International Journal of Scientific and Research Publications:
  2. A.Anusha,Syed Musthak Ahmed (2017)VEHICLE TRACKINGAND MONITORING SYSTEM TO ENHANCE THE SAFETY AND SECURITY DRIVING USING IoT, edn., 2017 International Conference on Recent Trends in Electrical, Electronics and Computing Technologies: .
  3. Andreas Geiger, Martin Lauer, Christian Wojek, Christoph Stiller, and Raquel Urtasun (2014) 3D Traffic Scene Understanding from MovablePlatforms, VOL. 36, NO. 5, MAY edn., IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE: .
  4. C. Andrieu, N. de Freitas, A. Doucet, andM. I. Jordan, “An introductionto MCMC for machine learning,” Mach. Learn., vol. 50, no. 1–2, pp. 5–43, 2003.
  5. M. Andriluka, S. Roth, and B. Schiele, “Monocular 3D pose estimation and tracking by detection,” in Proc. CVPR, San Francisco, CA, USA, 2010.
  6. S.Bao,M.Sun,and S.Savarese, “Toward coherent object detection and scene layout understanding,” in Proc. CVPR, San Francisco, CA, USA, 2010.
  7. O. Barinova, V. Lempitsky, E. Tretyak, and P. Kohli, “Geometric image parsing in man-made environments,” in Proc. ECCV, Berlin, Germany, 2010.
  8. C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. New York, NY, USA: Springer, 2006.
  9. M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, and L. Van Gool, “Robust tracking-by-detection using a detector confidence particle filter,” in Proc. ICCV, Kyoto, Japan, 2009. Autonomous Challenge,” Annu. Rev. Control, vol. 36, no.
  10. A.Geiger,P. Lenz,andR.Urtasun,“Arewereadyforautonomous driving? The KITTI vision benchmark suite,” in Proc. CVPR,Providence, RI, USA, 201
  11. Matthias Galster,Matthias Galster, Danny Weyns,Dan Tofan,BartoMichalik, and Paris Avgeri(MARCH 2014) Variability in Software Systems— A Systematic Literature Review, VOL. 40
  12. A. Geiger, M. Roser, and R. Urtasun, “Efficient large-scale stereo matching,” in Proc. ACCV, Queenstown, New Zealand, 2010.
  13. A. Geiger, C. Wojek, and R. Urtasun, “Joint 3D estimation of objects and scene layout,” in Proc. NIPS, 2011.
  14. A. Gupta, A. A. Efros, and M. Hebert, “Blocks world revisited:
  15. A. Ess, B. Leibe, K. Schindler, and L. V. Gool, “Robust multi-person tracking from a mobile platform,” IEEE Trans. Team members contribution Pattern Anal. Mach. Intell., vol. 31, no. 10, pp. 1831–1846, Oct.
  16. G. Hinton, “Training products of experts by minimizing contrastivedivergence,” Neural Comput., vol. 14, no. 8, pp. 1771–1800 TransPattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2196–2210, Dec. 2009.,
  17. Parvez, M.Z.Ahmed, Rahman, “A theoretical model of GSM network based vehicle tracking system”, Electrical and Computer Engineering.
  18. Bing-fei Wu, Ying-Han Chen “An efficient Web-Based Tracking System through reduction of Redundant Connection”,

Publication Details

Published in : Volume 8 | Issue 1 | January-February 2021
Date of Publication : 2021-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 12-20
Manuscript Number : IJSRSET207647
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Tanvir Rahman, " IoT based Smart Vehicle Monitoring System : A Systematic Literature Review, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 1, pp.12-20, January-February-2021. Available at doi : https://doi.org/10.32628/IJSRSET207647      Journal URL : https://res.ijsrset.com/IJSRSET207647

Article Preview