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.

The capabilities of quantum computers, such as the number of supported qubits and maximum circuit depth, have grown exponentially in recent years. Commercially relevant applications that take advantage of quantum computing are expected to be available soon. In this paper, we shed light on the possibilities of accelerating database tasks using quantum computing with examples of optimizing queries and transaction schedules and present some open challenges for future studies in the field.

Quantum computers are known to be efficient for solving combinatorial problems like finding optimal schedules for processing transactions in parallel without blocking. We show how Grover’s search algorithm for quantum computers can be applied for finding an optimal transaction schedule via generating code from the problem instance. We compare our approach with existing approaches for traditional computers and quantum annealers in terms of preprocessing, runtime, space and code length complexity. Furthermore, we show by experiments the expected number of optimal solutions of this problem as well as suboptimal ones. With the help of an estimator of the number of solutions, we further speed up our optimizer for optimal and suboptimal transaction schedules.

Managing and querying Semantic Web are important issues for Semantic Web applications. Therefore, we have developed a Semantic Web database system with logically and physically optimized SPARQL engines to manage and query RDF data, named LuposDate. In order to present the functionalities of the LUPOSDATE system and engines, we have developed an online demonstration, which is available at http://www.ifis.uni-luebeck.de/index.php?id=luposdate-demo.

Caching stores the results of previously answered queries in order to answer succeeding queries faster by reusing these results. We propose two different approaches for using caches of XSLT transformed XML data in order to answer queries. The first approach checks whether or not a current query Q can be directly answered from the result of a previously answered query Qi stored in the cache. The new query is otherwise submitted to the source over the network, the answer of the query is determined, transmitted back to the client, and stored in the cache. The second approach determines only the intersection Q-Qi and integrates the result of Q--Qi into the previous results in the cache, which requires applying a numbering scheme for the output of the XSLT stylesheet. We show by experimental results that the second approach can significantly speed up the answering time in comparison to the first approach, but is not significantly slower in few worst cases than the second approach.