Abstract
In massive IoT (Internet of Things) environment, which constitutes a goal of the 5G standard, the communication channel of an MTC (machine-type-communication) device with narrowband transmission has problems that are deep fading and interference with the transmission performance. For improved communications in IoT environment, researchers aimed to increase the transmission throughput by using larger contention windows between channel hops when the probability of successful data communication drops during the previous time step to prevent the rapid increase in the number of connected devices. Hence, researchers propose a model to change the interframe spacing according to the number of contention windows for hopping and data prioritization by quickly determining network traffic variations in a massive network environment. Specifically, researchers propose CSMA/CA (carrier-sense multiple access with collision avoidance) channel hopping to prevent deep fading and obtain multiuser diversity in multichannel environments as well as improve the channel quality. Researchers confirmed that the proposed CSMA/CA channel hopping provides a data transfer rate about twice that of conventional TDMA (time-division multiple access) channel hopping for 60 connected devices. As a result of analyzing data transfer rates in channel hopping, it was shown that the collision probability is not high when the number of nodes is small, so the number of channels and contention window size are not significantly affected, but as the number of nodes increases, the number of channels and contention window size are greatly affected.













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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A3B03029906, No. 2018R1D1A1B07049758 and No. 2020R1I1A3072312). And this work was supported by the research grant of the Kongju National University in 2020.
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Kim, G., Jeong, J. CSMA/CA channel hopping in IoT environment toward intelligent multi-user diversity. J Supercomput 77, 11930–11945 (2021). https://doi.org/10.1007/s11227-021-03754-z
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DOI: https://doi.org/10.1007/s11227-021-03754-z