Open Access Open Access  Restricted Access Subscription Access

Review of Emerging Technology: LiFi

Sangram Patil

Abstract


Perhaps you've found a few solutions concerning Li-Fi and its dynamic revives to different events quicker than Wi-Fi, the ongoing distant Web headway standard, yet what is Li-Fi certainly? By what technique may we utilize it? Will Li-Fi abrogate Wi-Fi? Li-Fi. This paper assists you with seeing how Li-Fi is reasonable going to change our modernized world. In the event that you are involving remote web in a bistro, taking it from the singular nearby, or seeing for data transmission at a social occasion, you might get confused at the moderate rates you face when more than one contraption is connected with the structure. To grasp this issue, a German Physicist-Harald Haas has presented one more progression known as "information through light" which deduces transmission of information through Drove lights which contrast in powers snappier than the trademark eye can follow. This improvement depends upon the power and limit of the light conveying diode. This paper draws its idea on headway and working of Li-Fi based construction and contrasts its presentation and the current remote arrange impels.


Full Text:

PDF

References


Ulfa, A. M., Afandi Saputra, Y., & Nguyen, P. T. (2019). Role of artificial intelligence in pharma science. Journal of critical reviews, 7(1), 2020.

Prakash, R., & Agarwal, P. (2014). The new era of transmission and communication technology: Li-fi (light fidelity) led & ted based approach. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 3(2), 285-290.

Shewale, A. S. (2020). Key Security Problems-Research Challenges Regarding Cloud Computing. Recent Trends in Androids and IOS Applications, 1(3).

Shewale, A. S. (2020). System Intrusion Detection and Countermeasure Selection in Virtual Network Systems. Journal of Advancement in Parallel Computing, 2(3), 1–4.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251- 257.

Gomez, F. J., & Schmidhuber, J. (2005, June). Co-evolving recurrent neurons learn deep memory POMDPs. In Proceedings of the 7th annual conference on Genetic and evolutionary computation (pp. 491-498).


Refbacks

  • There are currently no refbacks.