Citation: Lakshmanna, K.; Kaluri, R.; Nagaraja, G.; Alzamil, Z.S.; Rajput, D.S.; Khan, A.A.; Haq, M.A.; Alhussen, A. A Review on Deep Learning Techniques for IoT Data. Electronics 2022, 11, 1604. https:// doi.org/10.3390/electronics11101604 Academic Editor: Ping-Feng Pai Received: 7 April 2022 Accepted: 6 May 2022 Published: 18 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Review A Review on Deep Learning Techniques for IoT Data Kuruva Lakshmanna 1 , Rajesh Kaluri 1 , Nagaraja Gundluru 1 , Zamil S. Alzamil 2 , Dharmendra Singh Rajput 1, *, Arfat Ahmad Khan 3, *, Mohd Anul Haq 2, * and Ahmed Alhussen 4, * 1 Vellore Institute of Technology (VIT), Vellore 632014, India; lakshman.kuruva@vit.ac.in (K.L.); rajesh.kaluri@vit.ac.in (R.K.); nagaraja.g@vit.ac.in (N.G.) 2 Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia; z.alzamil@mu.edu.sa 3 School of Manufacturing Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand 4 Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia * Correspondence: dharmendrasingh@vit.ac.in (D.S.R.); khansatwat@gmail.com (A.A.K.); m.anul@mu.edu.sa (M.A.H.); aa.alhussen@mu.edu.sa (A.A.) Abstract: Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments. Keywords: big data; deep learning; data analytics; internet of things; IoT 1. Introduction Applications based on smartphones, sensors and actuators are becoming more and more intelligent over the past decade and facilitate communication between devices and the performance of more complex tasks. The number of network devices exceeded the world population [1] in 2008 and the figure continues to increase exponentially until today. In the age of the Internet of Things (IoT), smartphones, built-in systems, wireless sensors and most every device are connected by a local network or the internet. The growth in Internet-of-Things (IoT), which includes smartphones [2], sensor networks [3], sensors unusual aerial vehicles (UAV) [4,5], cognitively smart systems [6], and so on has created a multitude of new applications across various mobile and remote platforms. The amount of data obtained from such devices often increases with the growing number of devices. Electronics 2022, 11, 1604. https://doi.org/10.3390/electronics11101604 https://www.mdpi.com/journal/electronics