Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks
作者机构:Department of Computer Software EngineeringSoonchunhyang UniversityAsan-si31538Korea School of Computer SoftwareDaegu Catholic UniversityGyeongsan-si38430Korea
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2020年第64卷第7期
页 面:1-15页
核心收录:
学科分类:0831[工学-生物医学工程(可授工学、理学、医学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 0805[工学-材料科学与工程(可授工学、理学学位)] 0701[理学-数学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 0801[工学-力学(可授工学、理学学位)]
基 金:This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2019-2015-0-00403)supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation) this work was supported by the Soonchunhyang University Research Fund
主 题:IoT wide area communication machine learning uplink transmission.
摘 要: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 ***-nodes are connected to a gateway with a single *** consume very low-power,using very low data rate to deliver *** long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently ***,this paper proposes a multicast uplink data transmission mechanism particularly for bad network *** delay will be increased if only retransmissions are used under bad network ***,employing multicast techniques in bad network conditions can significantly increase packet delivery ***,retransmission can be reduced and hence transmission efficiency ***,the proposed method adopts multicast uplink after network condition *** predict network conditions,the proposed method uses a deep neural network *** proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.