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Title	Deep Learning For Intelligent Iot: Opportunities, Challenges And Solutions
ID_Doc	17874
Authors	Zikria Y.B.; Afzal M.K.; Kim S.W.; Marin A.; Guizani M.
Year	2020
Published	Computer Communications, 164
DOI	http://dx.doi.org/10.1016/j.comcom.2020.08.017
Abstract	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. © 2020
Author Keywords	Big data; Deep learning; Health-based IoT; Industrial Internet of Things (IIoT); Intelligent Transportation System (ITS); Internet of Things (IoT); Machine learning; Network protocols; Network security; Reinforcement learning

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