On a Novel Deep-Learning-Based Intelligent Partially Overlapping Channel Assignment in SDN-IoT

被引:90
作者
Tang, Fengxiao [1 ]
Mao, Bomin [1 ]
Fadlullah, Zubair Md. [1 ]
Kato, Nei [1 ,2 ,3 ]
机构
[1] Tohoku Univ, GSIS, Sendai, Miyagi, Japan
[2] Tohoku Univ, Sendai, Miyagi, Japan
[3] Tohoku Univ, ROEC, Sendai, Miyagi, Japan
基金
日本学术振兴会;
关键词
NETWORKS;
D O I
10.1109/MCOM.2018.1701227
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Recently, SDN has emerged as a promising technology to cost-effectively provide the scale and flexibility necessary for IoT services. In this article, we consider the wireless SDN for IoT, referred to as SDN-IoT, which is anticipated to smartly route traffic and use underutilized network resources to deliver IoT data to the cloud/Internet. However, the rapid increase of IoT devices and the subsequent massive surge of the IoT data traffic are expected to place a huge strain on the SDN-IoT. In this article, we focus on this issue and point out the importance of assigning suitable channels to each SDN-IoT switch to avoid potential network congestion. In particular, we consider how to exploit POC assignment in the SDN-IoT. However, our investigation reveals that the conventional fixed POC assignment algorithms are not viable for the highly dynamic large-scale SDN-IoT. Therefore, in this article, we propose a novel deep-learning-based intelligent POC assignment for the wireless SDN-IoT where the IoT data traffic dynamically changes. In particular, we envision two deep-learning-based strategies to predict the future IoT traffic load and to adaptively assign POCs according to predicted traffic load, respectively. Computer-based simulation results demonstrate that with the envisioned deep learning methods carried out at the SDN-IoT controller, our proposal achieves high accuracy of traffic load prediction and quick convergence of the channel assignment process. Additionally, in contrast with the conventional POC assignment algorithms, our proposal significantly improves the network performance.
引用
收藏
页码:80 / 86
页数:7
相关论文
共 15 条
[11]   Orchestration of RAN and Transport Networks for 5G: An SDN Approach [J].
Rostami, Ahmad ;
Ohlen, Peter ;
Wang, Kun ;
Ghebretensae, Zere ;
Skubic, Bjorn ;
Santos, Mateus ;
Vidal, Allan .
IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (04) :64-70
[12]   Channel assignment strategies for multiradio wireless mesh networks: Issues and solutions [J].
Skalli, Habiba ;
Ghosh, Samik ;
Das, Sajal K. ;
Lenzini, Luciano ;
Conti, Marco .
IEEE COMMUNICATIONS MAGAZINE, 2007, 45 (11) :86-93
[13]  
Tang F, 2017, IEEE WIREL COMMUN, VPP, P1
[14]   ON REMOVING ROUTING PROTOCOL FROM FUTURE WIRELESS NETWORKS: A REAL-TIME DEEP LEARNING APPROACH FOR INTELLIGENT TRAFFIC CONTROL [J].
Tang, Fengxiao ;
Mao, Bomin ;
Fadlullah, Zubair Md. ;
Kato, Nei ;
Akashi, Osamu ;
Inoue, Takeru ;
Mizutani, Kimihiro .
IEEE WIRELESS COMMUNICATIONS, 2018, 25 (01) :154-160
[15]  
Zhao W., 2015, IEEE T VEHIC TECH, V65