Vol.64, No.1, 2020, pp.1-15, doi:10.32604/cmc.2020.09545
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: kimdy81@cu.ac.kr.
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
IoT, wide area communication, machine learning, uplink transmission.
Cite This Article
Kim, S., Kim, D. (2020). Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks. CMC-Computers, Materials & Continua, 64(1), 1–15.