Analyzing the Research Trends of IoT Using Topic Modeling

The internet of things (IoT) is one of the most rapidly growing technologies. Therefore, the interest in industry and academia has been increasing. The published research data have evolved in IoT because of scientific advances in this field. Since science plays a vital role in decision-making, this study examines the thematic landscape of research on IoT, which may contribute to understanding the research field's structure allows for critical reflections and the identification of blind spots for advancing this field. The current study applies a text mining approach on 25966 Scopus-indexed abstracts and titles published from 2008 to 2020 on a latent Dirichlet allocation-based topic model. In this study, various models in the range of 1-100 topics were created. Examination of coherence scores was combined with manual analysis; the 25-topic model was chosen as an optimal one. The statistical methods employed highlight the timely trends of the extracted topics, intellectual topic structure and resulting communities in the topic network. The study carpingly depicts the quantitative results from an IoT perspective. The statistical analysis depicts that IoT publications has exponential growth rate. The hotspot of the IoT research can be concluded as 'intrusion attack detection', 'cloud and edge computing', 'energy consumption', 'access channels', 'algorithm optimization' and 'healthcare and medical'. The topics that reflect the wireless sensor networks, security and privacy, high-range signal, devices and context aware computing and sensor control and monitoring have stable trends. This study identifies research focus on the development of low-energy consumption systems (Green IoT), application of high-range signals and their performance in tracking and identification, and data analytics (Big data IoT). Furthermore, the research focuses on industrial solutions towards diseases diagnosis and its treatment in health sector. Finally, in agriculture sector for intelligent manufacturing, research focuses on the application of image recognition for plant and food analysis.