Low power Smart Vehicle Tracking, Monitoring, Collision avoidance and Antitheft System

2020 ◽  
Author(s):  
Saurabh S. Chakole ◽  
Neema A. Ukani
Keyword(s):  
Low Power ◽  
Collision Avoidance ◽  
Vehicle Tracking ◽  
Smart Vehicle

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.

scholarly journals Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3334 ◽  
Author(s):  
Maciej Nikodem ◽  
Mariusz Słabicki ◽  
Tomasz Surmacz ◽  
Paweł Mrówka ◽  
Cezary Dołęga
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Short Range ◽  
Vehicle Tracking ◽  
Radio Communication ◽  
Large Area ◽  
Camera Tracking ◽  
Graphic Processing Units ◽  
Detection And Tracking ◽  
Tracking Algorithms

Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. However, this requires large volumes of data to be transmitted and may raise privacy issues. This paper presents a dedicated deep learning detection and tracking algorithms that can be run directly on the camera’s embedded system. This method significantly reduces the stream of data from the cameras, reduces the required communication bandwidth and expands the range of communication technologies to use. Consequently, it allows to use short-range radio communication to transmit vehicle-related information directly between the cameras, and implement the multi-camera tracking directly in the cameras. The proposed solution includes detection and tracking algorithms, and a dedicated low-power short-range communication for multi-target multi-camera tracking systems that can be applied in parking and intersection scenarios. System components were evaluated in various scenarios including different environmental and weather conditions.

Low Power Two-Tier GALS Architecture for Multi Robot Collision Avoidance

2013 ◽  
Author(s):  
Neeraj Pradhan ◽  
Roopak Dubey ◽  
K. Madhava Krishna ◽  
Shubhajit Roy Chowdhury
Keyword(s):  
Low Power ◽  
Collision Avoidance ◽  
Multi Robot

scholarly journals Smart Vehicle Tracking System using RFID

2020 ◽  
Vol 9 (3) ◽  
pp. 2428-2431
Keyword(s):  
Tracking System ◽  
Smartphone Application ◽  
Mobile User ◽  
Vehicle Tracking ◽  
Network Access ◽  
Points Of Interest ◽  
Web Access ◽  
Points Of View ◽  
Smart Vehicle ◽  
Vehicle Tracking System

A proficient vehicle tracking framework is projected and executed for the development of any vehicle following from any area at any time. The proposed arrangement takes points of interest of the two fundamental highlights in portable stage these days which are area administrations, predominantly GPS-GSM based, and essential communication administrations, principally SMS based. The utilization of RFID per user is to peruse the tag of a vehicle and the data will be sent to the framework, the highlights incorporate controlling the framework through UI. The contributions from RFID per users are persistently refreshed To Arduino for handling the information. The server gadget's primary duty is to give the careful area of the transport to the server, or the client if there should be an occurrence of SMS based inquiry from customer's gadget. Then again, customer's gadget can discover transport area either utilizing SMS administration or utilizing network access. If customer uses android mobile, user can introduce the application to follow the transport area utilizing web access. The server gadget will be put on the vehicle of enthusiasm with our application introduced inside it. It performs preferred from numerous points of view over other comparative vehicles following frameworks. The proposed framework utilized a prevalent innovation that joins a Smartphone application.

scholarly journals Collision Avoidance Resource Allocation for LoRaWAN

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1218
Author(s):  
Natalia Chinchilla-Romero ◽  
Jorge Navarro-Ortiz ◽  
Pablo Muñoz ◽  
Pablo Ameigeiras
Keyword(s):  
Resource Allocation ◽  
Low Power ◽  
Collision Avoidance ◽  
Propagation Model ◽  
System Capacity ◽  
Radio Link ◽  
New Paradigm ◽  
Correct Operation ◽  
Resource Allocation Algorithm ◽  
Iot Devices

The number of connected IoT devices is significantly increasing and it is expected to reach more than two dozens of billions of IoT connections in the coming years. Low Power Wide Area Networks (LPWAN) have become very relevant for this new paradigm due to features such as large coverage and low power consumption. One of the most appealing technologies among these networks is LoRaWAN. Although it may be considered as one of the most mature LPWAN platforms, there are still open gaps such as its capacity limitations. For this reason, this work proposes a collision avoidance resource allocation algorithm named the Collision Avoidance Resource Allocation (CARA) algorithm with the objective of significantly increase system capacity. CARA leverages the multichannel structure and the orthogonality of spreading factors in LoRaWAN networks to avoid collisions among devices. Simulation results show that, assuming ideal radio link conditions, our proposal outperforms in 95.2% the capacity of a standard LoRaWAN network and increases the capacity by almost 40% assuming a realistic propagation model. In addition, it has been verified that CARA devices can coexist with LoRaWAN traditional devices, thus allowing the simultaneous transmissions of both types of devices. Moreover, a proof-of-concept has been implemented using commercial equipment in order to check the feasibility and the correct operation of our solution.

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