Information Retrieval for IoT and WoT: State-of-the-Art, Taxonomy Framework, and Evolutionary Directions

The explosive growth of Internet of Things (IoT) and Web of Things (WoT) technologies, characterized by a vast diversity of devices and data formats, producing vast volumes of information at a high pace in real time necessitates a paradigm shift in information retrieval (IR) systems. Traditional IR struggles to navigate the dynamic landscapes of these interconnected environments. This work proposes a multidimensional taxonomy framework to bridge this critical gap. Our framework not only unifies existing classification approaches but also delves into the analysis of traditional IR subtasks, thereby establishing a cohesive foundation for future advancements in IR tailored to the evolving IoT/WoT landscape. We further contribute by identifying key challenges and posing open research questions, thus propelling the development of next-generation IR techniques specifically tailored to the intricate search demands of the evolving IoT and WoT cyber-world.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-12-25
DOI
https://doi.org/10.1109/jiot.2024.3522219
Citations
1
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.35

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article

Information Retrieval for IoT and WoT: State-of-the-Art, Taxonomy Framework, and Evolutionary Directions

Juan M. Fernández‐Luna, Annalina Caputo, Cristyan Manta-Caro
1 citations
IEEE Internet of Things Journal
IoT and Edge/Fog Computing
0.35
article

Information Retrieval for IoT and WoT: State-of-the-Art, Taxonomy Framework, and Evolutionary Directions

Juan M. Fernández‐Luna, Annalina Caputo, Cristyan Manta-Caro
article en
1 citations

Abstract

The explosive growth of Internet of Things (IoT) and Web of Things (WoT) technologies, characterized by a vast diversity of devices and data formats, producing vast volumes of information at a high pace in real time necessitates a paradigm shift in information retrieval (IR) systems. Traditional IR struggles to navigate the dynamic landscapes of these interconnected environments. This work proposes a multidimensional taxonomy framework to bridge this critical gap. Our framework not only unifies existing classification approaches but also delves into the analysis of traditional IR subtasks, thereby establishing a cohesive foundation for future advancements in IR tailored to the evolving IoT/WoT landscape. We further contribute by identifying key challenges and posing open research questions, thus propelling the development of next-generation IR techniques specifically tailored to the intricate search demands of the evolving IoT and WoT cyber-world.

IEEE Internet of Things JournalVol. 12(6)
Universidad de Granada (ES), Dublin City University (IE)
Universidad de Granada
Openalex Percentile: Top 35%
IoT and Edge/Fog Computing
0.35
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Information Retrieval for IoT and WoT: State-of-the-Art, Taxonomy Framework, and Evolutionary Directions — Juan M. Fernández‐Luna, Annalina Caputo, et al. · IEEE Internet of Things Journal (2024) | TGRS Research Map | TGRS