物联网应用中的可解释人工智能研究综述

doi:10.11772/j.issn.1001-9081.2024070927

《计算机应用》唯一官方网站 ›› 2025, Vol. 45 ›› Issue (7): 2169-2179.DOI: 10.11772/j.issn.1001-9081.2024070927

物联网应用中的可解释人工智能研究综述

赵小阳¹, 许新征²(), 李仲年²

^1.中国矿业大学徐海学院，江苏徐州 221008
^2.中国矿业大学计算机科学与技术学院，江苏徐州 221116

收稿日期:2024-07-03 修回日期:2024-10-17 接受日期:2024-10-18 发布日期:2025-07-10 出版日期:2025-07-10
通讯作者: 许新征
作者简介:赵小阳（1999—），女，宁夏吴忠人，硕士，CCF会员，主要研究方向：深度神经网络的可解释性、物联网
许新征（1980—），男，安徽宿州人，教授，博士生导师，博士，CCF高级会员，主要研究方向：机器学习、数据挖掘、医学图像处理 xuxinzh@163.com
李仲年（1990—），男，江苏徐州人，讲师，博士，CCF会员，主要研究方向：机器学习、数据挖掘、医学图像处理。
基金资助:
国家自然科学基金资助项目(61976217);国家自然科学基金资助项目(62306320)

Research review on explainable artificial intelligence in internet of things applications

Xiaoyang ZHAO¹, Xinzheng XU²(), Zhongnian LI²

^1.Xuhai College，China University of Mining and Technology，Xuzhou Jiangsu 221008，China
^2.School of Computer Science and Technology，China University of Mining and Technology，Xuzhou Jiangsu 221116，China

Received:2024-07-03 Revised:2024-10-17 Accepted:2024-10-18 Online:2025-07-10 Published:2025-07-10
Contact: Xinzheng XU
About author:ZHAO Xiaoyang， born in 1999， M. S. Her research interests include interpretability of deep neural networks， internet of things.
XU Xinzheng， born in 1980， Ph. D.， professor. His research interests include machine learning， data mining， medical image processing.
LI Zhongnian， born in 1990， Ph. D.， lecturer. His research interests include machine learning， data mining， medical image processing.
Supported by:
National Natural Science Foundation of China(61976217)

摘要/Abstract

摘要：

在物联网（IoT）时代，人工智能（AI）与IoT的结合已经成为推动技术发展和应用创新的重要趋势。随着设备连接数量的指数级增长，提升终端用户对智能系统的信任度变得尤为关键。可解释人工智能（XAI）指能提供它们的决策过程和结果解释的AI系统。XAI的出现推动了AI技术的发展，并增强了用户对AI系统的信任。因此，对IoT应用中的XAI研究进行综述。首先，介绍IoT和XAI的相关背景及意义；其次，介绍XAI的定义及关键技术；接着，介绍传统AI驱动的IoT应用的最新进展和XAI驱动的IoT应用的最新进展；最后，对XAI在IoT应用中的未来发展方向和相关挑战分别进行总结和展望。

关键词: 可解释人工智能, 深度学习, 物联网, 智慧医疗, 智慧工业, 智慧城市

Abstract:

In the era of Internet of Things （IoT）， the integration of Artificial Intelligence （AI） and IoT has become a significant trend driving technological advancements and application innovations. With the exponential growth in the number of connected devices， enhancing end-users’ trust in intelligent systems has become especially critical. Explainable Artificial Intelligence （XAI） refers to AI systems capable of providing their decision-making processes and outcome explanations. The emergence of XAI has propelled the development of AI technology and increased users’ trust in AI systems. Therefore， a research review on XAI in IoT applications was performed. Firstly， the background and significance of IoT and XAI were discussed. Secondly， the definition and key technologies of XAI were presented. Thirdly， the recent progress in traditional AI-driven IoT applications as well as XAI-driven IoT applications were introduced. Finally， the future development directions of XAI in IoT applications were prospected and the associated challenges were summarized.

Key words: Explainable Artificial Intelligence (XAI), deep learning, Internet of Things (IoT), smart healthcare, smart industry, smart city

中图分类号:

TP181

赵小阳, 许新征, 李仲年. 物联网应用中的可解释人工智能研究综述[J]. 计算机应用, 2025, 45(7): 2169-2179.

Xiaoyang ZHAO, Xinzheng XU, Zhongnian LI. Research review on explainable artificial intelligence in internet of things applications[J]. Journal of Computer Applications, 2025, 45(7): 2169-2179.

图/表 8

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感知类别	应用场景	代表性数据集
通用场景感知	图像识别	ImageNet、CIFAR10/100
	目标检测/追踪	COCO、MOT
	文本识别	LSVT
以人为主体感知	人脸识别	FFHQ、YouTube Faces DB
	指纹识别	FVC2000
	姿态估计	COCO、MPII
	手势识别	DVS128
	动作识别	ActivityNet
听觉感知	语音识别	CHiME-3、VoxCeleb
听觉感知	说话人识别	TIMIT APCSC、Common Voice
自然语言处理	机器翻译	TedTalks、WMT
多媒体分析	图像字幕	COCO、Flickr30k
多媒体分析	跨媒体检索	PKU XMediaNet
3D感知	深度估计	KITTI、NYU Depth Dataset V2
	定位	KITTI
	地图构建	TUM Visual-Inertial

感知类别	应用场景	代表性数据集
通用场景感知	图像识别	ImageNet、CIFAR10/100
	目标检测/追踪	COCO、MOT
	文本识别	LSVT
以人为主体感知	人脸识别	FFHQ、YouTube Faces DB
	指纹识别	FVC2000
	姿态估计	COCO、MPII
	手势识别	DVS128
	动作识别	ActivityNet
听觉感知	语音识别	CHiME-3、VoxCeleb
听觉感知	说话人识别	TIMIT APCSC、Common Voice
自然语言处理	机器翻译	TedTalks、WMT
多媒体分析	图像字幕	COCO、Flickr30k
多媒体分析	跨媒体检索	PKU XMediaNet
3D感知	深度估计	KITTI、NYU Depth Dataset V2
	定位	KITTI
	地图构建	TUM Visual-Inertial

心血管疾病预测分类模型		AUC值
传统机器学习算法驱动模型	基于自适应算法AdaBoost	75.0
	结合朴素贝叶斯的多模态整合框架	79.9
	基于逻辑回归算法LR和带径向基函数的支持向量机算法SVM	87.2
	基于监督学习的环境辅助生活预测模型	83.0
XAI驱动模型		89.0

心血管疾病预测分类模型		AUC值
传统机器学习算法驱动模型	基于自适应算法AdaBoost	75.0
	结合朴素贝叶斯的多模态整合框架	79.9
	基于逻辑回归算法LR和带径向基函数的支持向量机算法SVM	87.2
	基于监督学习的环境辅助生活预测模型	83.0
XAI驱动模型		89.0

物联网应用中的可解释人工智能研究综述

Research review on explainable artificial intelligence in internet of things applications

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