Full text

Turn on search term navigation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

Details

Title
Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
Author
Al-Saedi, Ahmed A 1   VIAFID ORCID Logo  ; Veselka Boeva 1   VIAFID ORCID Logo  ; Casalicchio, Emiliano 2   VIAFID ORCID Logo  ; Exner, Peter 3   VIAFID ORCID Logo 

 Department of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden; [email protected] (V.B.); [email protected] (E.C.) 
 Department of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden; [email protected] (V.B.); [email protected] (E.C.); Department of Computer Science, Sapienza University of Rome, 00185 Roma, Italy 
 Sony, R&D Center Europe, SE-221 88 Lund, Sweden; [email protected] 
First page
5544
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2700779705
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.