Haseena Rahmath P, Vishal Srivastava, Kuldeep Chaurasia, Roberto G. Pacheco, Rodrigo S. Couto
Deep neural networks (DNNs) typically have a single exit point that makes predictions by running the entire stack of neural layers. Since not all inputs require the same amount of computation to reach a confident prediction, recent research has focused on incorporating multiple ”exits” into the conventional DNN architecture. Early-exit DNNs are multi-exit neural networks that attach many side branches to the conventional DNN, enabling inference to stop early at intermediate points. This approach offers several advantages, including speeding up the inference process, mitigating the vanishing gradients problems, reducing overfitting and overthinking tendencies. It also supports DNN partitioning across devices and is ideal for multi-tier computation platforms such as edge computing. This paper decomposes the early-exit DNN architecture and reviews the recent advances in the field. The study explores its benefits, designs, training strategies, and adaptive inference mechanisms. Various design challenges, application scenarios, and future directions are also extensively discussed.
{"title":"Early-Exit Deep Neural Network - A Comprehensive Survey","authors":"Haseena Rahmath P, Vishal Srivastava, Kuldeep Chaurasia, Roberto G. Pacheco, Rodrigo S. Couto","doi":"10.1145/3698767","DOIUrl":"https://doi.org/10.1145/3698767","url":null,"abstract":"Deep neural networks (DNNs) typically have a single exit point that makes predictions by running the entire stack of neural layers. Since not all inputs require the same amount of computation to reach a confident prediction, recent research has focused on incorporating multiple ”exits” into the conventional DNN architecture. Early-exit DNNs are multi-exit neural networks that attach many side branches to the conventional DNN, enabling inference to stop early at intermediate points. This approach offers several advantages, including speeding up the inference process, mitigating the vanishing gradients problems, reducing overfitting and overthinking tendencies. It also supports DNN partitioning across devices and is ideal for multi-tier computation platforms such as edge computing. This paper decomposes the early-exit DNN architecture and reviews the recent advances in the field. The study explores its benefits, designs, training strategies, and adaptive inference mechanisms. Various design challenges, application scenarios, and future directions are also extensively discussed.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384405","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}
Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li
Large Language Models (LLMs) have recently transformed both the academic and industrial landscapes due to their remarkable capacity to understand, analyze, and generate texts based on their vast knowledge and reasoning ability. Nevertheless, one major drawback of LLMs is their substantial computational cost for pre-training due to their unprecedented amounts of parameters. The disadvantage is exacerbated when new knowledge frequently needs to be introduced into the pre-trained model. Therefore, it is imperative to develop effective and efficient techniques to update pre-trained LLMs. Traditional methods encode new knowledge in pre-trained LLMs through direct fine-tuning. However, naively re-training LLMs can be computationally intensive and risks degenerating valuable pre-trained knowledge irrelevant to the update in the model. Recently, Knowledge-based Model Editing (KME), also known as Knowledge Editing or Model Editing , has attracted increasing attention, which aims to precisely modify the LLMs to incorporate specific knowledge, without negatively influencing other irrelevant knowledge. In this survey, we aim to provide a comprehensive and in-depth overview of recent advances in the field of KME. We first introduce a general formulation of KME to encompass different KME strategies. Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category. Moreover, representative metrics, datasets, and applications of KME are introduced accordingly. Finally, we provide an in-depth analysis regarding the practicality and remaining challenges of KME and suggest promising research directions for further advancement in this field.
{"title":"Knowledge Editing for Large Language Models: A Survey","authors":"Song Wang, Yaochen Zhu, Haochen Liu, Zaiyi Zheng, Chen Chen, Jundong Li","doi":"10.1145/3698590","DOIUrl":"https://doi.org/10.1145/3698590","url":null,"abstract":"Large Language Models (LLMs) have recently transformed both the academic and industrial landscapes due to their remarkable capacity to understand, analyze, and generate texts based on their vast knowledge and reasoning ability. Nevertheless, one major drawback of LLMs is their substantial computational cost for pre-training due to their unprecedented amounts of parameters. The disadvantage is exacerbated when new knowledge frequently needs to be introduced into the pre-trained model. Therefore, it is imperative to develop effective and efficient techniques to update pre-trained LLMs. Traditional methods encode new knowledge in pre-trained LLMs through direct fine-tuning. However, naively re-training LLMs can be computationally intensive and risks degenerating valuable pre-trained knowledge irrelevant to the update in the model. Recently, Knowledge-based Model Editing (KME), also known as Knowledge Editing or Model Editing , has attracted increasing attention, which aims to precisely modify the LLMs to incorporate specific knowledge, without negatively influencing other irrelevant knowledge. In this survey, we aim to provide a comprehensive and in-depth overview of recent advances in the field of KME. We first introduce a general formulation of KME to encompass different KME strategies. Afterward, we provide an innovative taxonomy of KME techniques based on how the new knowledge is introduced into pre-trained LLMs, and investigate existing KME strategies while analyzing key insights, advantages, and limitations of methods from each category. Moreover, representative metrics, datasets, and applications of KME are introduced accordingly. Finally, we provide an in-depth analysis regarding the practicality and remaining challenges of KME and suggest promising research directions for further advancement in this field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142384404","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}
Elena Battaglia, Federico Peiretti, Ruggero Gaetano Pensa
Since its early formulations, co-clustering has gained popularity and interest both within and outside the machine learning community as a powerful learning paradigm for clustering high-dimensional data with good explainability properties. The simultaneous partitioning of all the modes of the input data tensors (rows and columns in a data matrix) is both a method for improving clustering on one mode while performing dimensionality reduction on the other mode(s), and a tool for providing an actionable interpretation of the clusters in the main mode as summaries of the features in each other mode(s). Hence, it is useful in many complex decision systems and data science applications. In this paper, we survey the the co-clustering literature by reviewing the main co-clustering methods, with a special focus on the work done in the last twenty-five years. We identify, describe and compare the main algorithmic categories, and provide a practical characterization with respect to similar unsupervised techniques. Additionally, we also try to explain why it is still a powerful tool despite the apparent recent decreasing interest shown by the machine learning community. To this purpose, we review the most recent trends in co-clustering research and outline the open problems and promising future research perspectives.
{"title":"Co-clustering: a Survey of the Main Methods, Recent Trends and Open Problems","authors":"Elena Battaglia, Federico Peiretti, Ruggero Gaetano Pensa","doi":"10.1145/3698875","DOIUrl":"https://doi.org/10.1145/3698875","url":null,"abstract":"Since its early formulations, co-clustering has gained popularity and interest both within and outside the machine learning community as a powerful learning paradigm for clustering high-dimensional data with good explainability properties. The simultaneous partitioning of all the modes of the input data tensors (rows and columns in a data matrix) is both a method for improving clustering on one mode while performing dimensionality reduction on the other mode(s), and a tool for providing an actionable interpretation of the clusters in the main mode as summaries of the features in each other mode(s). Hence, it is useful in many complex decision systems and data science applications. In this paper, we survey the the co-clustering literature by reviewing the main co-clustering methods, with a special focus on the work done in the last twenty-five years. We identify, describe and compare the main algorithmic categories, and provide a practical characterization with respect to similar unsupervised techniques. Additionally, we also try to explain why it is still a powerful tool despite the apparent recent decreasing interest shown by the machine learning community. To this purpose, we review the most recent trends in co-clustering research and outline the open problems and promising future research perspectives.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374651","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}
Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility towards securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim to overcome these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.
{"title":"Knowledge-based Cyber Physical Security at Smart Home: A Review","authors":"Azhar Alsufyani, Omar Rana, Charith Perera","doi":"10.1145/3698768","DOIUrl":"https://doi.org/10.1145/3698768","url":null,"abstract":"Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility towards securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim to overcome these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374658","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}
An organization’s privacy policy states how it collects, stores, processes, and shares its users’ personal information. The growing number of data protection laws and regulations as well as the numerous sectors where the organizations are collecting user information, has led to the investigation of privacy policies with regards to their accessibility, readability, completeness, comparison with organization’s actual data practices, use of machine learning/natural language processing for automated analysis, and comprehension/perception/concerns of end-users via summarization/visualization tools and user studies. However, there is limited work on systematically reviewing the existing research on this topic. We address this gap by conducting a systematic review of the existing privacy policy literature. To this end, we compiled and analyzed 202 papers (published till 31 st December 2023) that investigated privacy policies. Our work advances the field of privacy policies by summarizing the analysis techniques that have been used to study them, the data protection laws/regulations explored, and the sectors to which these policies pertain. We provide actionable insights for organizations to achieve better end-user privacy.
{"title":"A Systematic Review of Privacy Policy Literature","authors":"Yousra Javed, Ayesha Sajid","doi":"10.1145/3698393","DOIUrl":"https://doi.org/10.1145/3698393","url":null,"abstract":"An organization’s privacy policy states how it collects, stores, processes, and shares its users’ personal information. The growing number of data protection laws and regulations as well as the numerous sectors where the organizations are collecting user information, has led to the investigation of privacy policies with regards to their accessibility, readability, completeness, comparison with organization’s actual data practices, use of machine learning/natural language processing for automated analysis, and comprehension/perception/concerns of end-users via summarization/visualization tools and user studies. However, there is limited work on systematically reviewing the existing research on this topic. We address this gap by conducting a systematic review of the existing privacy policy literature. To this end, we compiled and analyzed 202 papers (published till 31 <jats:sup>st</jats:sup> December 2023) that investigated privacy policies. Our work advances the field of privacy policies by summarizing the analysis techniques that have been used to study them, the data protection laws/regulations explored, and the sectors to which these policies pertain. We provide actionable insights for organizations to achieve better end-user privacy.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374660","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}
Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.
{"title":"Cybersecurity in Electric and Flying Vehicles: Threats, Challenges, AI Solutions & Future Directions","authors":"Hamed Alqahtani, Gulshan Kumar","doi":"10.1145/3697830","DOIUrl":"https://doi.org/10.1145/3697830","url":null,"abstract":"Electric and Flying Vehicles (EnFVs) represent a transformative shift in transportation, promising enhanced efficiency and reduced environmental impact. However, their integration into interconnected digital ecosystems poses significant cybersecurity challenges, including cyber-physical threats, privacy vulnerabilities, and supply chain risks. This paper comprehensively explores these challenges and investigates artificial intelligence (AI)-driven solutions to bolster EnFV cybersecurity. The study begins with an overview of EnFV cybersecurity issues, emphasizing the increasing complexity of threats in digital transportation systems. Methodologically, the paper reviews existing literature to identify gaps and assesses recent advancements in AI for cybersecurity applications. Key methodologies include AI-powered intrusion detection, threat analysis leveraging machine learning algorithms, predictive maintenance strategies, and enhanced authentication protocols. Results underscore the effectiveness of AI technologies in mitigating EnFV cybersecurity risks, demonstrating improved threat detection and response capabilities. The study concludes by outlining future research directions, highlighting the need for continued innovation in AI, quantum computing resilience, blockchain applications, and ethical considerations. These findings contribute to a clearer understanding of EnFV cybersecurity dynamics and provide a roadmap for enhancing the security and reliability of future transportation systems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374657","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}
Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Lio, Bruno Lepri, Andrea Passerini
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable, or which explainer should be preferred in a given setting. In this survey we fill these gaps by devising a systematic experimental study, which tests twelve explainers on eight representative message-passing architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research.
{"title":"Explaining the Explainers in Graph Neural Networks: a Comparative Study","authors":"Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Lio, Bruno Lepri, Andrea Passerini","doi":"10.1145/3696444","DOIUrl":"https://doi.org/10.1145/3696444","url":null,"abstract":"Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of methods both novel or adapted from other domains. To sort out this plethora of alternative approaches, several studies have benchmarked the performance of different explainers in terms of various explainability metrics. However, these earlier works make no attempts at providing insights into why different GNN architectures are more or less explainable, or which explainer should be preferred in a given setting. In this survey we fill these gaps by devising a systematic experimental study, which tests twelve explainers on eight representative message-passing architectures trained on six carefully designed graph and node classification datasets. With our results we provide key insights on the choice and applicability of GNN explainers, we isolate key components that make them usable and successful and provide recommendations on how to avoid common interpretation pitfalls. We conclude by highlighting open questions and directions of possible future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374628","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}
In recent years, Digital Twin (DT) technology has emerged as a significant technological advancement. A digital twin is a digital representation of a physical asset that mirrors its data model, behaviour, and interactions with other physical assets. Digital Twin aims to achieve adaptability, seamless data integration, modelling, simulation, automation, and real-time data management. The primary goal of this paper is to explore the role of agents in DT implementations, seeking to understand their predominant usage scenarios and purposes. From our perspective, agents serving as intelligent entities play a role in realising the features of DTs. This paper also discusses the gaps in DT, highlights future directions, and analyses various technologies integrated with multi-agent systems technologies in DT implementations. Finally, the paper briefly discusses an overview of an architecture to implement a DT for smart agriculture with multi-agents.
{"title":"The Role of Multi-Agents in Digital Twin Implementation: Short Survey","authors":"Kalyani Yogeswaranathan, Rem Collier","doi":"10.1145/3697350","DOIUrl":"https://doi.org/10.1145/3697350","url":null,"abstract":"In recent years, Digital Twin (DT) technology has emerged as a significant technological advancement. A digital twin is a digital representation of a physical asset that mirrors its data model, behaviour, and interactions with other physical assets. Digital Twin aims to achieve adaptability, seamless data integration, modelling, simulation, automation, and real-time data management. The primary goal of this paper is to explore the role of agents in DT implementations, seeking to understand their predominant usage scenarios and purposes. From our perspective, agents serving as intelligent entities play a role in realising the features of DTs. This paper also discusses the gaps in DT, highlights future directions, and analyses various technologies integrated with multi-agent systems technologies in DT implementations. Finally, the paper briefly discusses an overview of an architecture to implement a DT for smart agriculture with multi-agents.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374629","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}
Muhammad Tariq, Sohail Ahmad, Ahmad Jan Mian, Houbing Song
The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.
{"title":"Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey","authors":"Muhammad Tariq, Sohail Ahmad, Ahmad Jan Mian, Houbing Song","doi":"10.1145/3696414","DOIUrl":"https://doi.org/10.1145/3696414","url":null,"abstract":"The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374631","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}
Álvaro Sobrinho, Matheus Vilarim, Amanda Barbosa, Edmar Candeia Gurjão, Danilo F. S. Santos, Dalton Valadares, Leandro Dias da Silva
Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.
{"title":"Challenges and Opportunities in Mobile Network Security for Vertical Applications: A Survey","authors":"Álvaro Sobrinho, Matheus Vilarim, Amanda Barbosa, Edmar Candeia Gurjão, Danilo F. S. Santos, Dalton Valadares, Leandro Dias da Silva","doi":"10.1145/3696446","DOIUrl":"https://doi.org/10.1145/3696446","url":null,"abstract":"Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":null,"pages":null},"PeriodicalIF":16.6,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374630","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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