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基于上下文感知的边缘人工智能模型在无线传感器网络中的应用综述

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

机构信息

Department of Computer Science, Blekinge Institute of Technology, SE-371 79 Karlskrona, Sweden.

Department of Computer Science, Sapienza University of Rome, 00185 Roma, Italy.

出版信息

Sensors (Basel). 2022 Jul 25;22(15):5544. doi: 10.3390/s22155544.

DOI:10.3390/s22155544
PMID:35898044
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9371178/
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.

摘要

传感器技术的最新进展有望促进无线传感器网络（WSN）在工业、物流、医疗保健等领域的更多应用。另一方面，人工智能（AI）、机器学习（ML）和深度学习（DL）的进步正在成为处理来自边缘合成异构传感器的大量数据并通过更好地了解情况得出准确结论的主要解决方案。这两个领域（WSN 和 AI）的融合实现了更精确的测量、情境感知分析和预测，这对智能传感应用非常有用。本文对使用传感器技术的情境感知智能系统的最新发展进行了全面概述。此外，还讨论了它们的应用领域、相关挑战、采用 AI 解决方案的动机，重点关注边缘计算，即传感器和 AI 技术，并对现有研究差距进行了分析。本研究的另一个贡献是使用语义感知方法来提取与调查相关的主题。后者特别确定了该研究中包含的文章支持的十一个主要研究主题。从多个角度对这些主题进行了分析，以回答五个主要研究问题。最后，还讨论了潜在的未来研究方向。

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