LiDAR has gained popularity in autonomous driving due to advantages like long measurement distance, rich 3D information, and stability in harsh environments. Place Recognition (PR) enables vehicles to identify previously visited locations despite variations in appearance, weather, and viewpoints, even determining their global location within prior maps. This capability is crucial for accurate localization in autonomous driving. Consequently, LiDAR-based Place Recognition (LPR) has emerged as a research hotspot in robotics. However, existing reviews predominantly concentrate on Visual Place Recognition (VPR), leaving a gap in systematic reviews on LPR. This paper bridges this gap by providing a comprehensive review of LPR methods, thus facilitating and encouraging further research. We commence by exploring the relationship between PR and autonomous driving components. Then, we delve into the problem formulation of LPR, challenges, and relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets and evaluation metrics and envision promising future directions. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition. We plan to maintain an up-to-date project on https://github.com/ShiPC-AI/LPR-Survey.
{"title":"LiDAR-Based Place Recognition For Autonomous Driving: A Survey","authors":"Yongjun Zhang, Pengcheng Shi, Jiayuan Li","doi":"10.1145/3707446","DOIUrl":"https://doi.org/10.1145/3707446","url":null,"abstract":"LiDAR has gained popularity in autonomous driving due to advantages like long measurement distance, rich 3D information, and stability in harsh environments. Place Recognition (PR) enables vehicles to identify previously visited locations despite variations in appearance, weather, and viewpoints, even determining their global location within prior maps. This capability is crucial for accurate localization in autonomous driving. Consequently, LiDAR-based Place Recognition (LPR) has emerged as a research hotspot in robotics. However, existing reviews predominantly concentrate on Visual Place Recognition (VPR), leaving a gap in systematic reviews on LPR. This paper bridges this gap by providing a comprehensive review of LPR methods, thus facilitating and encouraging further research. We commence by exploring the relationship between PR and autonomous driving components. Then, we delve into the problem formulation of LPR, challenges, and relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets and evaluation metrics and envision promising future directions. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition. We plan to maintain an up-to-date project on https://github.com/ShiPC-AI/LPR-Survey.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"19 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmet Oztoprak, Reza Hassanpour, Aysegul Ozkan, Kasim Oztoprak
Wireless Sensor Networks (WSNs) represent an innovative technology that integrates compact, energy-efficient sensors with wireless communication functionalities, facilitating instantaneous surveillance and data gathering from the surrounding environment. WSNs are utilized across diverse domains, such as environmental monitoring, industrial automation, healthcare, smart agriculture, home automation, and beyond. Due to the inherent characteristics of WSNs they face many security challenges ranging from resource-based attacks, such as energy depletion or computational overload, to eavesdropping, interception, and tampering. Moreover, the dynamic and often ad-hoc deployment of sensors in varying environments increases their vulnerability to physical intrusion attacks, the distributed and collaborative nature of WSNs raises concerns about data integrity, as compromised nodes can potentially propagate misleading or malicious information throughout the network. In this article, we categorize WSN attacks, identifying vulnerabilities and corresponding mitigation strategies. We also explore current research directions in WSN security, emphasizing the challenges in addressing these issues.
{"title":"Security Challenges, Mitigation Strategies, and Future Trends in Wireless Sensor Networks: A Review","authors":"Ahmet Oztoprak, Reza Hassanpour, Aysegul Ozkan, Kasim Oztoprak","doi":"10.1145/3706583","DOIUrl":"https://doi.org/10.1145/3706583","url":null,"abstract":"Wireless Sensor Networks (WSNs) represent an innovative technology that integrates compact, energy-efficient sensors with wireless communication functionalities, facilitating instantaneous surveillance and data gathering from the surrounding environment. WSNs are utilized across diverse domains, such as environmental monitoring, industrial automation, healthcare, smart agriculture, home automation, and beyond. Due to the inherent characteristics of WSNs they face many security challenges ranging from resource-based attacks, such as energy depletion or computational overload, to eavesdropping, interception, and tampering. Moreover, the dynamic and often ad-hoc deployment of sensors in varying environments increases their vulnerability to physical intrusion attacks, the distributed and collaborative nature of WSNs raises concerns about data integrity, as compromised nodes can potentially propagate misleading or malicious information throughout the network. In this article, we categorize WSN attacks, identifying vulnerabilities and corresponding mitigation strategies. We also explore current research directions in WSN security, emphasizing the challenges in addressing these issues.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"12 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enterprise Architecture (EA) is a systematic and holistic approach to designing and managing an organization's information systems components, aiding in optimizing resources, managing risk, and facilitating change. It weighs different architectural quality attributes against each other to achieve the most advantageous architecture. However, the evaluation of EA lacks a systematic approach. This study employs a Systematic Literature Review, analyzing, in detail, 109 articles carefully selected from 3644 papers published since 2005. The key outcome of the research reveals that a crucial factor for the extensive worldwide adoption of EA evaluation methods lies in the automation of the assessment and architecture modeling processes, particularly emphasizing the facet of data collection. The automation of EA evaluation will empower organizations to streamline their processes, make data-driven decisions, and respond more effectively to change, ultimately contributing to their competitiveness and long-term success in the global market. The study identifies diverse evaluation methods, determines evaluation criteria, examines the extent to which these methods have been verified in practice, and provides directions for further research and advancement.
{"title":"A Systematic Literature Review of Enterprise Architecture Evaluation Methods","authors":"Norbert Rudolf Busch, Andrzej Zalewski","doi":"10.1145/3706582","DOIUrl":"https://doi.org/10.1145/3706582","url":null,"abstract":"Enterprise Architecture (EA) is a systematic and holistic approach to designing and managing an organization's information systems components, aiding in optimizing resources, managing risk, and facilitating change. It weighs different architectural quality attributes against each other to achieve the most advantageous architecture. However, the evaluation of EA lacks a systematic approach. This study employs a Systematic Literature Review, analyzing, in detail, 109 articles carefully selected from 3644 papers published since 2005. The key outcome of the research reveals that a crucial factor for the extensive worldwide adoption of EA evaluation methods lies in the automation of the assessment and architecture modeling processes, particularly emphasizing the facet of data collection. The automation of EA evaluation will empower organizations to streamline their processes, make data-driven decisions, and respond more effectively to change, ultimately contributing to their competitiveness and long-term success in the global market. The study identifies diverse evaluation methods, determines evaluation criteria, examines the extent to which these methods have been verified in practice, and provides directions for further research and advancement.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"46 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Object selection and manipulation are the foundation of VR interactions. With the rapid development of VR technology and the field of virtual object selection and manipulation, the literature demands a structured understanding of the core research challenges and a critical reflection of the current practices. To provide such understanding and reflections, we systematically reviewed 106 papers. We identified classic and emerging topics, categorized existing solutions, and evaluated how success was measured in these publications. Based on our analysis, we discuss future research directions and propose a framework for developing and determining appropriate solutions for different application scenarios.
{"title":"Object Selection and Manipulation in VR Headsets: Research Challenges, Solutions, and Success Measurements","authors":"Difeng Yu, Tilman Dingler, Eduardo Velloso, Jorge Goncalves","doi":"10.1145/3706417","DOIUrl":"https://doi.org/10.1145/3706417","url":null,"abstract":"Object selection and manipulation are the foundation of VR interactions. With the rapid development of VR technology and the field of virtual object selection and manipulation, the literature demands a structured understanding of the core research challenges and a critical reflection of the current practices. To provide such understanding and reflections, we systematically reviewed 106 papers. We identified classic and emerging topics, categorized existing solutions, and evaluated how success was measured in these publications. Based on our analysis, we discuss future research directions and propose a framework for developing and determining appropriate solutions for different application scenarios.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"259 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and data planes, offering advantages such as enhanced manageability, flexibility, and network programmability, as well as the ability to introduce new solutions to address security threats. However, its innovative design also poses new vulnerabilities and threats, especially susceptibility to low-rate threats. To this end, this paper presents a comprehensive overview of low-rate threats in programmable networks. It explores low-rate threats and countermeasures within the SDN architecture, encompassing the data plane, control plane, control channel, and application plane, together with traditional low-rate threats and countermeasures in SDN. Furthermore, the paper offers detailed insight into threats and countermeasures against low-rate attacks exploiting SDN vulnerabilities and low-rate attacks related to Programmable Data Plane (PDP). Additionally, it presents a comparative analysis and discussion of low-rate attacks versus high-volume attacks, along with suggestions for enhancing SDN security. This thorough review aims to assist researchers in developing more resilient and dependable countermeasures against low-rate threats in programmable networks.
{"title":"When SDN Meets Low-rate Threats: A Survey of Attacks and Countermeasures in Programmable Networks","authors":"Dan Tang, Rui Dai, Yudong Yan, Keqin Li, Wei Liang, Zheng Qin","doi":"10.1145/3704434","DOIUrl":"https://doi.org/10.1145/3704434","url":null,"abstract":"Low-rate threats are a class of attack vectors that are disruptive and stealthy, typically crafted for security vulnerabilities. They have been the significant concern for cyber security, impacting both conventional IP-based networks and emerging Software-Defined Networking (SDN). SDN is a revolutionary architecture that separates the control and data planes, offering advantages such as enhanced manageability, flexibility, and network programmability, as well as the ability to introduce new solutions to address security threats. However, its innovative design also poses new vulnerabilities and threats, especially susceptibility to low-rate threats. To this end, this paper presents a comprehensive overview of low-rate threats in programmable networks. It explores low-rate threats and countermeasures within the SDN architecture, encompassing the data plane, control plane, control channel, and application plane, together with traditional low-rate threats and countermeasures in SDN. Furthermore, the paper offers detailed insight into threats and countermeasures against low-rate attacks exploiting SDN vulnerabilities and low-rate attacks related to Programmable Data Plane (PDP). Additionally, it presents a comparative analysis and discussion of low-rate attacks versus high-volume attacks, along with suggestions for enhancing SDN security. This thorough review aims to assist researchers in developing more resilient and dependable countermeasures against low-rate threats in programmable networks.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amar Alsheavi, Ammar Hawbani, Wajdy Othman, XINGFU WANG, Gamil Qaid, Liang Zhao, Ahmed Al-Dubai, Liu Zhi, A.S. Ismail, Rutvij Jhaveri, Saeed Alsamhi, Mohammed A. A. Al-qaness
In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a subject of intricate security challenges spawned by its multifaceted manifestations. Central to securing IoT infrastructures is the crucial aspect of authentication, necessitating a comprehensive examination of its nuances, including benefits, challenges, opportunities, trends, and societal implications. In this paper, we thoroughly review the IoT authentication protocols, addressing the main challenges such as privacy protection, scalability, and human factors that may impact security. Through exacting analysis, we evaluate the strengths and weaknesses of existing authentication protocols and conduct a comparative performance analysis to evaluate their effectiveness and scalability in securing IoT environments and devices. At the end of this study, we summarize the main findings and suggest ways to improve the security of IoT devices in the future.
{"title":"IoT Authentication Protocols: Challenges, and Comparative Analysis","authors":"Amar Alsheavi, Ammar Hawbani, Wajdy Othman, XINGFU WANG, Gamil Qaid, Liang Zhao, Ahmed Al-Dubai, Liu Zhi, A.S. Ismail, Rutvij Jhaveri, Saeed Alsamhi, Mohammed A. A. Al-qaness","doi":"10.1145/3703444","DOIUrl":"https://doi.org/10.1145/3703444","url":null,"abstract":"In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a subject of intricate security challenges spawned by its multifaceted manifestations. Central to securing IoT infrastructures is the crucial aspect of authentication, necessitating a comprehensive examination of its nuances, including benefits, challenges, opportunities, trends, and societal implications. In this paper, we thoroughly review the IoT authentication protocols, addressing the main challenges such as privacy protection, scalability, and human factors that may impact security. Through exacting analysis, we evaluate the strengths and weaknesses of existing authentication protocols and conduct a comparative performance analysis to evaluate their effectiveness and scalability in securing IoT environments and devices. At the end of this study, we summarize the main findings and suggest ways to improve the security of IoT devices in the future.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large foundation models, including large language models, vision transformers, diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. To support the growth of these large models in a scalable and environmentally sustainable way, there has been a considerable focus on developing resource-efficient strategies. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects. It offers a comprehensive analysis and valuable insights gleaned from existing literature, encompassing a broad array of topics from cutting-edge model architectures and training/serving algorithms to practical system designs and implementations. The goal of this survey is to provide an overarching understanding of how current approaches are tackling the resource challenges posed by large foundation models and to potentially inspire future breakthroughs in this field.
{"title":"Resource-efficient Algorithms and Systems of Foundation Models: A Survey","authors":"Mengwei Xu, Dongqi Cai, Wangsong Yin, Shangguang Wang, Xin Jin, Xuanzhe Liu","doi":"10.1145/3706418","DOIUrl":"https://doi.org/10.1145/3706418","url":null,"abstract":"Large foundation models, including large language models, vision transformers, diffusion, and LLM-based multimodal models, are revolutionizing the entire machine learning lifecycle, from training to deployment. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. To support the growth of these large models in a scalable and environmentally sustainable way, there has been a considerable focus on developing resource-efficient strategies. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects. It offers a comprehensive analysis and valuable insights gleaned from existing literature, encompassing a broad array of topics from cutting-edge model architectures and training/serving algorithms to practical system designs and implementations. The goal of this survey is to provide an overarching understanding of how current approaches are tackling the resource challenges posed by large foundation models and to potentially inspire future breakthroughs in this field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"84 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sakuna Harinda Jayasundara, Nalin Asanka Gamagedara Arachchilage, Giovanni Russello
Administrator-centered access control failures can cause data breaches, putting organizations at risk of financial loss and reputation damage. Existing graphical policy configuration tools and automated policy generation frameworks attempt to help administrators configure and generate access control policies by avoiding such failures. However, graphical policy configuration tools are prone to human errors, making them unusable. On the other hand, automated policy generation frameworks are prone to erroneous predictions, making them unreliable. Therefore, to find ways to improve their usability and reliability, we conducted a Systematic Literature Review analyzing 49 publications. The thematic analysis of the publications revealed that graphical policy configuration tools are developed to write and visualize policies manually. Moreover, automated policy generation frameworks are developed using machine learning (ML) and natural language processing (NLP) techniques to automatically generate access control policies from high-level requirement specifications. Despite their utility in the access control domain, limitations of these tools, such as the lack of flexibility, and limitations of frameworks, such as the lack of domain adaptation, negatively affect their usability and reliability, respectively. Our study offers recommendations to address these limitations through real-world applications and recent advancements in the NLP domain, paving the way for future research.
{"title":"SoK: Access Control Policy Generation from High-level Natural Language Requirements","authors":"Sakuna Harinda Jayasundara, Nalin Asanka Gamagedara Arachchilage, Giovanni Russello","doi":"10.1145/3706057","DOIUrl":"https://doi.org/10.1145/3706057","url":null,"abstract":"Administrator-centered access control failures can cause data breaches, putting organizations at risk of financial loss and reputation damage. Existing graphical policy configuration tools and automated policy generation frameworks attempt to help administrators configure and generate access control policies by avoiding such failures. However, graphical policy configuration tools are prone to human errors, making them unusable. On the other hand, automated policy generation frameworks are prone to erroneous predictions, making them unreliable. Therefore, to find ways to improve their usability and reliability, we conducted a Systematic Literature Review analyzing 49 publications. The thematic analysis of the publications revealed that graphical policy configuration tools are developed to write and visualize policies manually. Moreover, automated policy generation frameworks are developed using machine learning (ML) and natural language processing (NLP) techniques to automatically generate access control policies from high-level requirement specifications. Despite their utility in the access control domain, limitations of these tools, such as the lack of flexibility, and limitations of frameworks, such as the lack of domain adaptation, negatively affect their usability and reliability, respectively. Our study offers recommendations to address these limitations through real-world applications and recent advancements in the NLP domain, paving the way for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142742597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Decentralized finance (DeFi) represents a novel financial system but faces significant fraud challenges, leading to substantial losses. Recent advancements in artificial intelligence (AI) show potential for complex fraud detection. Despite growing interest, a systematic review of these methods is lacking. This survey correlates fraud types with DeFi project stages, presenting a taxonomy based on the project life cycle. We evaluate AI techniques, revealing notable findings such as the superiority of tree-based and graph-related models. Based on these insights, we offer recommendations and outline future research directions to aid researchers, practitioners, and regulators in enhancing DeFi security.
{"title":"AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective","authors":"Bingqiao Luo, Zhen Zhang, Qian Wang, Anli Ke, Shengliang Lu, Bingsheng He","doi":"10.1145/3705296","DOIUrl":"https://doi.org/10.1145/3705296","url":null,"abstract":"Decentralized finance (DeFi) represents a novel financial system but faces significant fraud challenges, leading to substantial losses. Recent advancements in artificial intelligence (AI) show potential for complex fraud detection. Despite growing interest, a systematic review of these methods is lacking. This survey correlates fraud types with DeFi project stages, presenting a taxonomy based on the project life cycle. We evaluate AI techniques, revealing notable findings such as the superiority of tree-based and graph-related models. Based on these insights, we offer recommendations and outline future research directions to aid researchers, practitioners, and regulators in enhancing DeFi security.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"65 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods. The essence of this technique lies in detecting subtle changes when Wi-Fi signals interact with the human body, which are then captured and interpreted by advanced algorithms. This paper initially provides an overview of the key methodologies in HAR and the evolution of non-contact sensing, introducing sensor-based recognition, computer vision, and Wi-Fi signal-based approaches, respectively. It then explores tools for Wi-Fi-based HAR signal collection and lists several high-quality datasets. Subsequently, the paper reviews various sensing tasks enabled by Wi-Fi signal recognition, highlighting the application of deep learning networks in Wi-Fi signal detection. The fourth section presents experimental results that assess the capabilities of different networks. The findings indicate significant variability in the generalization capacities of neural networks and notable differences in test accuracy for various motion analyses.
无线通信技术的最新进展使 Wi-Fi 信号在个人和专业环境中都变得不可或缺。利用这些信号进行人类活动识别（HAR）已成为一项尖端技术。与传统的视觉监控方法相比，利用 Wi-Fi 信号的波动进行人类活动识别（HAR）可增强隐私性。这种技术的精髓在于检测 Wi-Fi 信号与人体相互作用时的微妙变化，然后通过先进的算法捕捉和解读这些变化。本文首先概述了 HAR 的主要方法和非接触式传感的发展，分别介绍了基于传感器的识别、计算机视觉和基于 Wi-Fi 信号的方法。然后，论文探讨了基于 Wi-Fi 的 HAR 信号采集工具，并列出了几个高质量的数据集。随后，论文回顾了通过 Wi-Fi 信号识别实现的各种传感任务，重点介绍了深度学习网络在 Wi-Fi 信号检测中的应用。第四部分介绍了评估不同网络能力的实验结果。研究结果表明，神经网络的泛化能力存在很大差异，各种运动分析的测试准确性也存在明显差异。
{"title":"Wi-Fi Sensing Techniques for Human Activity Recognition: Brief Survey, Potential Challenges, and Research Directions","authors":"Fucheng Miao, Youxiang Huang, Zhiyi Lu, Tomoaki Ohtsuki, Guan Gui, Hikmet Sari","doi":"10.1145/3705893","DOIUrl":"https://doi.org/10.1145/3705893","url":null,"abstract":"Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods. The essence of this technique lies in detecting subtle changes when Wi-Fi signals interact with the human body, which are then captured and interpreted by advanced algorithms. This paper initially provides an overview of the key methodologies in HAR and the evolution of non-contact sensing, introducing sensor-based recognition, computer vision, and Wi-Fi signal-based approaches, respectively. It then explores tools for Wi-Fi-based HAR signal collection and lists several high-quality datasets. Subsequently, the paper reviews various sensing tasks enabled by Wi-Fi signal recognition, highlighting the application of deep learning networks in Wi-Fi signal detection. The fourth section presents experimental results that assess the capabilities of different networks. The findings indicate significant variability in the generalization capacities of neural networks and notable differences in test accuracy for various motion analyses.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"191 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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