Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

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 proces...

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Published inSensors (Basel, Switzerland) Vol. 22; no. 15; p. 5544
Main Authors Al-Saedi, Ahmed A., Boeva, Veselka, Casalicchio, Emiliano, Exner, Peter
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 25.07.2022
MDPI
Subjects
Artificial intelligence
Computer centers
Computer Communication Networks
computer network
context-awareness
edge computing
Global positioning systems
GPS
human
Humans
Internet of Things
Review
Sensors
wireless communication
wireless sensor network
Wireless Technology
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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.
AbstractList 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.
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.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.
Author Al-Saedi, Ahmed A.
Boeva, Veselka
Casalicchio, Emiliano
Exner, Peter
AuthorAffiliation 2 Department of Computer Science, Sapienza University of Rome, 00185 Roma, Italy
3 Sony, R&D Center Europe, SE-221 88 Lund, Sweden; peter.exner@sony.com
1 Department of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden; veselka.boeva@bth.se (V.B.); emiliano.casalicchio@uniroma1.it (E.C.)
AuthorAffiliation_xml – name: 2 Department of Computer Science, Sapienza University of Rome, 00185 Roma, Italy
– name: 1 Department of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden; veselka.boeva@bth.se (V.B.); emiliano.casalicchio@uniroma1.it (E.C.)
– name: 3 Sony, R&D Center Europe, SE-221 88 Lund, Sweden; peter.exner@sony.com
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  orcidid: 0000-0001-6061-0861
  surname: Al-Saedi
  fullname: Al-Saedi, Ahmed A.
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  fullname: Boeva, Veselka
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  fullname: Casalicchio, Emiliano
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  orcidid: 0000-0002-9629-786X
  surname: Exner
  fullname: Exner, Peter
BackLink https://urn.kb.se/resolve?urn=urn:nbn:se:bth-23537$$DView record from Swedish Publication Index
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Snippet 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...
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SubjectTerms Artificial intelligence
Computer centers
Computer Communication Networks
computer network
context-awareness
edge computing
Global positioning systems
GPS
human
Humans
Internet of Things
Review
Sensors
wireless communication
wireless sensor network
Wireless Technology
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Title Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview
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Volume 22
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