Abstract
One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment.
Highlights
Internet of Things (IoT) technology connects physical objects with the help of sensors and actuators by utilizing the existing infrastructure of communication networks, with the help of unlicensed wireless networks [1]
We briefly present the fundamental insights of Machine learning (ML) in persuasive applications for IoT-based systems
We propose Q-learning (QL) that is one of the prevailing algorithms of reinforcement learning (RL) as the future ML paradigm for channel access in contention-based dense wireless local area networks (WLAN) for IoT-based systems
Summary
Internet of Things (IoT) technology connects physical objects with the help of sensors and actuators by utilizing the existing infrastructure of communication networks, with the help of unlicensed wireless networks [1]. IoT technology uses the existing network infrastructure and communication technologies to ensure its strength. Sensors and actuators play a vital role in connecting the physical world to the digital world [2, 3]. The applications of IoT technology such as smart-cities, One of the key applications for the IoT is the eHealth service that targets sustaining patient health information in digital environments such as the Internet cloud with the help of advanced communication technologies. The World Health Organization (WHO) conducted a survey in 2013 and highlighted that upcoming decades would face the challenge of shortage of global health workforce, which would reach 12.9 million [4].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
