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Abstract The Internet of Things (IoT) paradigm is redefining our lives, allowing us to make “smart” decisions. This boom has also gained popularity in academic research, one of the core goals being to make IoT research more accessible to students and early researchers, while making sure that experienced researchers keep up with the changes happening in the research area. A large number of review papers are available covering surveys related to IoT vision enabling technologies, applications, key features, and future directions. Nevertheless, there is a lack of analysis of these reviews. This study provides a scientometric analysis of already available reviews in the field of IoT to identify upcoming research needs and bring simplicity to literature research. In total, 964 review articles in the field of IoT written in English and published in peer-reviewed journals and conferences from 2010 to April 2023 have been finalized from the Google Scholar database. Three broad categories of analysis have been performed on the 964 relevant collected literature, namely (a) statistical; (b) machine learning-based; and (c) evaluative analysis. An important differentiating feature of the current study is the use of machine learning for data exploration, thereby providing better interpretation. We find that the trend to review the field of IoT has increased in the last five years with only one article in 2010. This article identifies and quantifies the knowledge gaps to inform the community, industry, and government authorities about research directions for IoT. Furthermore, this scientometric analysis serves as a foundational resource for IoT researchers in identifying relevant and important survey papers that target their research fields in IoT.
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Stored only when both papers are in the corpus — 10 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.