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Enhancing Quality of Service in Internet of Things: Deep Learning Approach and Its Challenges
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Author(s): Udayakumar K. (SRM Institute of Science and Technology, India), Ramamoorthy S. (SRM Institute of Science and Technology, India)and Poorvadevi R. (Sri Chandrasekharendra Saraswathi ViswaMahavidyalaya, India)
Copyright: 2023
Pages: 26
Source title:
Convergence of Deep Learning and Internet of Things: Computing and Technology
Source Author(s)/Editor(s): T. Kavitha (New Horizon College of Engineering (Autonomous), India & Visvesvaraya Technological University, India), G. Senbagavalli (AMC Engineering College, Visvesvaraya Technological University, India), Deepika Koundal (University of Petroleum and Energy Studies, Dehradun, India), Yanhui Guo (University of Illinois, USA)and Deepak Jain (Chongqing University of Posts and Telecommunications, China)
DOI: 10.4018/978-1-6684-6275-1.ch007
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Abstract
The potential growth of internet of things (IoT) brings people and things together to handle daily tasks in a smart way. The major advancement the IoT offers is quality data sensing and faster data analytics through hurdle-free communication. The increasing number of devices and heterogeneous network natures unwrap more challenges in terms of quality of service. Currently, the deep learning algorithm explores different dimensions of service quality gradually in IoT scenarios. In order to effectively handle a dynamic IoT environment, it is essential that the design of IoT must be supplemented with an intelligent agent for providing effective QoS. The traditional methods are not capable of utilizing historical data to find insights into service quality improvement. In this chapter, a comprehensive analysis of deep learning techniques for improving QoS of the internet of things is carried out. Deep learning solutions for improving QoS and the challenges involved are compared. The deep reinforcement learning (DRL) for improving QoS in IoT and its evaluation technique are also explored.
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