Abstract:
In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of conventional artificial intelligence techniques, such as optimization approaches and game theory approaches, for resource management are common, while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.
