The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).
If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.
Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.
How to cite (IEEE):
H. Park, & J. Jeon
"Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks," JOIV : International Journal on Informatics Visualization, vol. 6, no. 2-2, , pp. 463-468, Aug. 2022.
https://doi.org/10.30630/joiv.6.2-2.1125
How to cite (APA):
Park, H., & Jeon, J.
(2022).
Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks.
JOIV : International Journal on Informatics Visualization, 6(2-2), 463-468.
https://doi.org/10.30630/joiv.6.2-2.1125
How to cite (Chicago):
Park, Hyunjoo, AND Jeon, Junheon.
"Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks" JOIV : International Journal on Informatics Visualization [Online], Volume 6 Number 2-2 (19 August 2022)
https://doi.org/10.30630/joiv.6.2-2.1125
How to cite (Vancouver):
Park H, & Jeon J .
Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks.
JOIV : International Journal on Informatics Visualization [Online]. 2022 Aug;
6(2-2):463-468.
https://doi.org/10.30630/joiv.6.2-2.1125
How to cite (Harvard):
Park, H., & Jeon, J.
,2022.
Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks.
JOIV : International Journal on Informatics Visualization, [Online] 6(2-2), pp. 463-468.
https://doi.org/10.30630/joiv.6.2-2.1125
How to cite (MLA8):
Park, Hyunjoo, & Junheon Jeon.
"Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks." JOIV : International Journal on Informatics Visualization [Online], 6.2-2 (2022): 463-468. Web. 2 Oct. 2022
, https://doi.org/10.30630/joiv.6.2-2.1125
BibTex Citation Data :
@article{JOIV1125,
author = {Hyunjoo Park and Junheon Jeon},
title = {Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {6},
number = {2-2},
year = {2022},
keywords = {Artificial intelligence; energy efficient; machine learning; WSNs; QoS support; MAC protocol.},
abstract = {Each sensor node in WSN is typically equipped with a limited capacity small battery. Energy-efficient communication is therefore considered a key component of network life extension. In addition, as the utilization of the sensor network increases, duplicate data and abnormal data is also collected to reduce the accuracy of the data in various environments. AI is used to recognize data anomaly values and increase packets' accuracy by removing out-of-range data. This can improve performance through optimal data transmission, resulting in increased network life, energy efficiency, and reliability. This paper proposes a protocol called MLQ-MAC that reflects the above. MLQ-MAC uses AI techniques to consider different types of data packets. The data collected by the sensor removes the measurement anomaly and duplicate data and stores it in a different transmission queue by priority. Efficient data transfer is possible by using an AI Discriminator for accurate classification before being stored on a transmission queue. The AI-Discriminator classifies a variety of factors, including the collection environment, characteristics of network applications, and so on. It also uses two new technologies: self-adaptation and scheduling for efficient transmission. In the protocol, the receiver adjusts the duty cycle according to to transmit urgency to improve network QoS. Finally, the simulation results show that the MLQ-MAC protocol reduces energy consumption at the receiver by up to 3.4% and per bit by up to 2.3% and improves packet delivery accuracy by up to 3%.},
issn = {2549-9904}, pages = {463--468}, doi = {10.30630/joiv.6.2-2.1125},
url = {https://joiv.org/index.php/joiv/article/view/1125}
}
Refworks Citation Data :
@article{{JOIV}{1125}, author = {Park, H., Jeon, J.}, title = {Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {2-2}, year = {2022}, doi = {10.30630/joiv.6.2-2.1125}, url = {} }Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
View JOIV Stats
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.