Energies 2021, 14, 6384. https://doi.org/10.3390/en14196384 www.mdpi.com/journal/energies Review Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review Nasser Kimbugwe 1,2 , Tingrui Pei 1,3, * and Moses Ntanda Kyebambe 2 1 School of Computer Science, Xiangtan University, Xiangtan 411105, China; nasser.kimbugwe@mak.ac.ug 2 Department of Networks, College of Computing & I.S, Makerere University, Kampala 7062, Uganda; moses.ntanda@mak.ac.ug 3 Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan 411105, China * Correspondence: peitingrui@xtu.edu.cn Abstract: The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life‐critical and finance‐critical systems are now IoT‐based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above‐mentioned challenges in order to en‐ hance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL tech‐ niques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT‐based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT‐based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state‐of‐the‐art re‐ search and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL‐based algo‐ rithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL tech‐ niques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT. Keywords: internet of things; quality of service; machine learning; deep learning 1. Introduction Computers, smartphones, systems, wireless sensors, actuators, and virtually every single automated device are connected together through the internet, creating the “Inter‐ net of Things (IoT)”, as shown in Figure 1. The communication can be either through long‐ range mobile networks, such as WiMAX, GSM, GRPS, and cellular networks, such as LTE, 3G, 4G, and 5G, or through short‐range technologies, such as Bluetooth, Wi‐Fi, and ZigBee. Because of the massive usage of IoT networks, applications, and services in all aspects of our daily life, guaranteeing high levels of Quality of Service is very critical. Our daily life is massively dependent on the IoT in many aspects. Almost every de‐ vice currently has internet capabilities, and it is estimated that by 2040 the number of connected devices on the internet will exceed 75 billion, generating over 100 trillion GB of Citation: Kimbugwe, N.; Pei, T.; Kyebambe, M.N. Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A review. Energies 2021, 14, 6384. https://doi.org/10.3390/en14196384 Academic Editors: Jaume Segura‐ Garcia and Santiago Felici‐Castell Received: 4 September 2021 Accepted: 3 October 2021 Published: 6 October 2021 Publisher’s Note: MDPI stays neu‐ tral with regard to jurisdictional claims in published maps and institu‐ tional affiliations. Copyright: © 2021 by the authors. Li‐ censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con‐ ditions of the Creative Commons At‐ tribution (CC BY) license (http://crea‐ tivecommons.org/licenses/by/4.0/).