Open Access
ARTICLE
Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks
Seokhoon Kim1, Dae-Young Kim2, *
1 Department of Computer Software Engineering, Soonchunhyang University, Asan-si, 31538, Korea.
2 School of Computer Software, Daegu Catholic University, Gyeongsan-si, 38430, Korea.
* Corresponding Author: Dae-Young Kim. Email: .
Computers, Materials & Continua 2020, 64(1), 1-15. https://doi.org/10.32604/cmc.2020.09545
Received 27 December 2019; Accepted 26 February 2020; Issue published 20 May 2020
Abstract
The Internet of Things (IoT) has enabled various intelligent services, and IoT
service range has been steadily extended through long range wide area communication
technologies, which enable very long distance wireless data transmission. End-nodes are
connected to a gateway with a single hop. They consume very low-power, using very low
data rate to deliver data. Since long transmission time is consequently needed for each
data packet transmission in long range wide area networks, data transmission should be
efficiently performed. Therefore, this paper proposes a multicast uplink data transmission
mechanism particularly for bad network conditions. Transmission delay will be increased
if only retransmissions are used under bad network conditions. However, employing
multicast techniques in bad network conditions can significantly increase packet delivery
rate. Thus, retransmission can be reduced and hence transmission efficiency increased.
Therefore, the proposed method adopts multicast uplink after network condition
prediction. To predict network conditions, the proposed method uses a deep neural
network algorithm. The proposed method performance was verified by comparison with
uplink unicast transmission only, confirming significantly improved performance.
Keywords
Cite This Article
S. Kim and D. Kim, "Adaptive data transmission method according to wireless state in long range wide area networks,"
Computers, Materials & Continua, vol. 64, no.1, pp. 1–15, 2020. https://doi.org/10.32604/cmc.2020.09545
Citations