Deep learning for intelligent IoT: Opportunities, challenges and solutions

被引:40
|
作者
Bin Zikria, Yousaf [1 ]
Afzal, Muhammad Khalil [2 ]
Kim, Sung Won [1 ]
Marin, Andrea [3 ]
Guizani, Mohsen [4 ]
机构
[1] Yeungnam Univ, Dept Informat & Commun Engn, 280 Daehak Ro, Gyongsan 38541, South Korea
[2] COMSATS Univ, Wah Campus, Islamabad 47010, Wah Cantt, Pakistan
[3] Univ Ca Foscari Venezia, Torino 155, I-30172 Venice, Italy
[4] Univ Idaho, Moscow, ID 83843 USA
关键词
Internet of Things (IoT); Machine learning; Deep learning; Reinforcement learning; Network protocols; Network security; Health-based IoT; Industrial Internet of Things (IIoT); Intelligent Transportation System (ITS); Big data; OPTIMIZATION; NETWORK;
D O I
10.1016/j.comcom.2020.08.017
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Next-generation wireless networks have to be robust and self-sustained. Internet of things (IoT) is reshaping the technological adaptation in the daily life of human beings. IoT applications are highly diverse, and they range from critical applications like smart city, health-based industries, to industrial IoT. Machine learning (ML) techniques are integrated into IoT to make the network efficient and autonomous. Deep learning (DL) is one of the types of ML, and it is computationally complex and expensive. One of the challenges is to merge deep learning methods with IoT to overall improve the efficiency of the IoT applications. An amalgamation of these techniques, maintaining a balance between computational cost and efficiency is crucial for next-generation IoT networks. In consideration of the requirements of ML and IoT and seamless integration demands overhauling the whole communication stack from physical layer to application layer. Hence, the applications build on top of modified stack will be significantly benefited, and It also makes it easy to widely deploy the network.
引用
收藏
页码:50 / 53
页数:4
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