An indoor wireless visual sensor network basing on light-fidelity communication

Mosaif, Afaf; Rakrak, Said

doi:10.1109/wincom.2017.8238159

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In the existing system, vehicle-to-vehicle communication uses a radar sensor [15] followed up in horizontal and vertical directions. With the aid of historical data, movement transmitted by vehicle to vehicle is proposed to measure the trajectory of the preceding vehicle route of the estimation process.…”

Section: G Existing Systemmentioning

confidence: 99%

An Integrated Vehicle to Vehicle Communication Control System Using Li-Fi Technology

Shakkeera¹,

Y²,

Nivedha³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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In recent years, vehicle-to-vehicle communication has become a significant aspect of detecting anomalous environmental activities through many wireless devices. Transferring data from one place to another is one of the important daily activities. Though using many wireless devices like Wi-Fi-Bluetooth readers helps us connect, there are also many limitations like providing signal only in the shortest range with fewer security features, speed, and range between the communication. These challenges will give rise to the solution using recent technology developed by Light Fidelity (Li-Fi), which will concise the vehicle-to-vehicle communication to optical networking technology. The proposed system with Li-Fi technology includes an ultrasonic sensor, gas sensor, vibration sensor, temperature sensor, LCD display, normal robot setup, Li-Fi transmitter, and receiver. If any abnormal circumstances are in front of the vehicle, the vehicle will be stopped, and a notification will be sent to the beside vehicle. Li-Fi transmitter and receiver are connected to the microcontroller's UART (Universal Asynchronous Receiver/Transmitter) function. In the proposed system, the object is detected using a Machine Learning (ML) algorithm called the Haar Cascade classification algorithm, where a cascade classification process is trained from the collection of positive and negative images. The proposed system increases the performance metrics like data transfer speed and decreases the time for transferring the communication data. Finally, the system saves many lives of persons from road accidents.

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Section: G Existing Systemmentioning

confidence: 99%