scholarly journals IoT based Smart Vehicle Monitoring System : A Systematic Literature Review

2021 ◽  
pp. 12-20
Author(s):  
Tanvir Rahman
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Navigation System ◽  
Tracking System ◽  
Health And Safety ◽  
Vehicle Tracking ◽  
Safe Driving ◽  
Driving Experience ◽  
Google Map Api ◽  
Smart Vehicle

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.

MACHINE LEARNING ON CONGESTION ANALYSIS BASED REAL-TIME NAVIGATION SYSTEM

2011 ◽  
Vol 20 (04) ◽  
pp. 753-781
Author(s):  
KAI CHEN ◽  
KIA MAKKI ◽  
NIKI PISSINOU
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Real Time ◽  
Navigation System ◽  
Prediction Models ◽  
Urban Traffic ◽  
Route Guidance ◽  
Highway Traffic ◽  
Data Set ◽  
Guidance Systems

In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,1 it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment.

scholarly journals Cloud-Based Vehicle Tracking System

2020 ◽  
Vol 2 (4) ◽  
pp. 21-30
Author(s):  
Ali Mustafa ◽  
Mohammed I. Aal-Nouman ◽  
Osama A. Awad
Keyword(s):  
Real Time ◽  
Tracking System ◽  
Large Data ◽  
Vehicle Tracking ◽  
General Packet Radio Service ◽  
Remote Server ◽  
Real Time Tracking ◽  
The Cost ◽  
Police Vehicles ◽  
Vehicle Tracking System

 The need for vehicle tracking system in real time is growth continues due to increase the cases of theft. This type of system in real time needs to transmit large data with huge number of HTTP request to the server to keep tracking and monitoring in real time, thus causes spend extremely high cost every month for transportation the information on tracking vehicles to server therefor the needs for reducing the number of transportation and data size that transmits in each HTTP request to save expenses. This paper designed and implement an integrated vehicle tracking system in real time to track vehicle anywhere and anytime. This system is divided into two parts: vehicle tracking part and monitoring part. Tracking part is represented by installation the electronic devices in the vehicle using modern Global Positioning System (GPS), microcontroller Arduino UNO R3 and SIM800L GSM/GPRS modem. GPS is determined location of the vehicle via received coordinates from satellites such as latitude and latitude with accuracy ranging approximately 2.5 meters; the coordinates faked to add a type of protection to information on vehicles without effecting on characterizing real time tracking before sending it via a General Packet Radio service (GPRS). The monitoring part is in the cloud and will receive the coordinates and displays it on a map in a web page. The main contribution of this system is it reduced data size that sent from in-vehicle device via selected only necessary data for tracking vehicle from NEMA sentences of GPS and reduced number of HTTP request that sent to remote server via constrain the transmission of information with the movement of vehicles, since when vehicle moved the coordinates each 10s and did not send anything when the vehicle stopped thus will reduce the cost of expenses every month. This system can be utilized to track and monitoring the vehicles of large universities, companies, organization and also can be used in army vehicles and police vehicles.      

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