IEEE Communications Surveys and Tutorials最新文献

{"title":"Solutions for Sustainable and Resilient Communication Infrastructure in Disaster Relief and Management Scenarios","authors":"Bilal Karaman;Ilhan Basturk;Sezai Taskin;Engin Zeydan;Ferdi Kara;Esra Aycan Beyazıt;Miguel Camelo;Emil Björnson;Halim Yanikomeroglu","doi":"10.1109/COMST.2025.3610793","DOIUrl":"10.1109/COMST.2025.3610793","url":null,"abstract":"As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. To this end, this paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover from disaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters. We also explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis. The paper then presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies. Finally, we address the open issues and challenges in realizing sustainable and resilient communication infrastructures and provide insights into future research directions. By incorporating lessons learned from various disaster scenarios, this paper presents strategic recommendations that enhance the resilience and adaptability of communication systems in the context of disaster relief and management.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"716-760"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077606","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}

{"title":"Quantum AI-Enhanced IoT–Fog Communication: A Survey From Cybersecurity and Data Privacy Perspective","authors":"Antônio Roberto L. de Macêdo;Senthil Kumar Jagatheesaperumal;Kelton Augusto Pontara da Costa;Kamal Acharya;Houbing Song;Mohsen Guizani;Victor Hugo C. de Albuquerque","doi":"10.1109/COMST.2025.3622378","DOIUrl":"10.1109/COMST.2025.3622378","url":null,"abstract":"The need for data privacy in the next-generation communication networks encompassing the Internet of Things (IoT) and Fog infrastructure has become very significant. This enforces the need for Quantum Artificial Intelligence (AI) approaches to safeguard them. The evolving new means of threats, which are complex and challenging to predict, make conventional security solutions difficult to address and mitigate. To counteract them proactively and efficiently, most organizations have started using AI solutions, which analyze and predict patterns of threats. However, most recent threats demand more robust solutions integrated into the network infrastructure. To sort out most of the demands of IoT-Fog communication services, we present a comprehensive review of Quantum AI to provide a secure and robust framework. Specifically, we provide a taxonomy to summarize the studies on Quantum AI over the IoT-Fog infrastructure, intended to provide predictive maintenance, mitigating threats, and robust defense strategies. Furthermore, we propose the integration of Neurosymbolic AI, which combines the pattern recognition power of neural networks with the reasoning capabilities of symbolic systems, thereby enabling context-aware threat detection and explainable decision-making in critical infrastructure security. In addition, we also emphasize network protocol security and communication privacy issues, particularly in industrial and cyber-physical system networks. Finally, we discuss prominent research challenges and open-ended future research directions for Quantum AI in next-generation wireless networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"3637-3665"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397777","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}

{"title":"Multi-Satellite Collaborations: Conception, Merits, Mechanisms, and Prospects","authors":"Gaofeng Pan;Xuyang Zhang;Pengxu Chen;Shuai Wang;Rui Zhang;Zizheng Hua;Dusit Niyato;Pei Xiao;Marco Di Renzo;George K. Karagiannidis","doi":"10.1109/COMST.2026.3673328","DOIUrl":"10.1109/COMST.2026.3673328","url":null,"abstract":"As tasks become more diverse and business volume surges in satellite communication scenarios, improving the performance and coordination capabilities of satellite communication systems has become crucial. Satellite communications (SatComs) are characterized by specialized tasks, orbital operation, extensive coverage, and complex link environments. However, relying on a single satellite can introduce performance bottlenecks and coverage blind spots. To address these limitations, this paper explores multi-satellite collaboration (MSC) within space information networks. MSC defined as the coordinated operation of two or more satellites within a given spatiotemporal region, with or without ground node assistance, has therefore emerged as a trans-formative paradigm to jointly accomplish specific missions. First, we analyze the applicability boundaries of single-satellite and MSC modes, highlighting the intrinsic advantages of collaborative architectures for coverage enhancement and resilience. Then, we comprehensively review key enabling technologies for collaboration across various orbital scenarios, including intra-orbit and inter-orbit, as well as network formation and topology construction, signal level, access level, link level, routing level, and transmission resource allocation. We also discuss how emerging technologies, such as artificial intelligence and cross-domain integration, can improve MSC performance. The paper aims to provide theoretical References and technical directions for designing and optimizing future efficient and intelligent MSC systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5561-5595"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440130","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}

{"title":"Advanced Learning Algorithms for Integrated Sensing and Communication (ISAC) Systems in 6G and Beyond: A Comprehensive Survey","authors":"Nguyen Cong Luong;Thien Huynh-The;Thai-Hoc Vu;Duc Van Le;Huy T. Nguyen;Nguyen Duc Hai;Gia-Vuong Nguyen;Nguyen Duc Duy Anh;Dusit Niyato;Dong In Kim;Quoc-Viet Pham","doi":"10.1109/COMST.2025.3584333","DOIUrl":"10.1109/COMST.2025.3584333","url":null,"abstract":"Integrated sensing and communication (ISAC) allows the same hardware platform and resources to function sensing and communication simultaneously, which reduces the hardware size and addresses the spectrum congestion concerns. However, the sharing of the hardware and resources of the sensing and communication functions raises resource managements. Traditional optimization approaches are developed based on rigid mathematical models in ISAC systems. However, they face computation complexity and may not achieve the desired performance under the dynamics of the ISAC system environments. Machine learning with ability in learning features/patterns of data as well as approximating mathematical models has recently proposed to effectively solve the complicated ISAC problems. In this survey, we thus provide a comprehensive literature review on applications of learning algorithms for ISAC systems. Particularly, we review learning approaches proposed for emerging issues in ISAC systems, including beamforming designing/tracking, waveform design, spectrum allocation, time allocation, and power allocation, angle of arrival (AoA)/angle of departure (AoD) estimation, signal classification, and security issues. Moreover, we present applications of advanced learning methods for wireless sensing, which is considered to be an emerging sensing service of the next-generation networks. We conclude the survey with highlighting technical issues of learning algorithms and discussing future research directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"2572-2611"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533246","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}

{"title":"Missing Data Recovery Methods on Multivariate Time Series in IoT: A Comprehensive Survey","authors":"Kai Zhang;Qinmin Yang;Chao Li;Xin Sun;Jiming Chen","doi":"10.1109/COMST.2025.3585962","DOIUrl":"10.1109/COMST.2025.3585962","url":null,"abstract":"This survey comprehensively examines the challenges and methodologies for missing data recovery in Multivariate Time Series (MTS) within the context of Internet of Things (IoT) environments. It delves into the distinct mechanisms and patterns of missing data that are prevalent in IoT MTS, providing a detailed categorization of existing methods into three primary frameworks: non-data mining, traditional data mining, and deep learning approaches. Each category is critically analyzed to highlight strategic improvements tailored to MTS data and their applicability to various missing patterns of IoT. The paper also discusses the computational demand, convergence speed, real-time capability, and adaptability to missing patterns of these methods, offering a thorough evaluation of their strengths and weaknesses. By synthesizing current methodologies and identifying gaps in the literature from 2013 to 2024, this work identifies future research directions aimed at enhancing the robustness and efficiency of missing data recovery in complex IoT systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"149-180"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566289","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}

{"title":"A Comprehensive Survey on Cybersecurity Challenges and Defenses for Integrated Air, Ground, and Underwater Autonomous Vehicle Systems","authors":"Ishtiaq Ahmad;Ramsha Narmeen;Umair Ahmad Mughal;Liang Yang;Junjie Li;Yazeed Alkhrijah;Muhammad Khurram Khan","doi":"10.1109/COMST.2026.3708340","DOIUrl":"10.1109/COMST.2026.3708340","url":null,"abstract":"The rapid deployment of autonomous systems, including Connected Autonomous Vehicles (CAVs), Unmanned Aerial Vehicles (UAVs), and Autonomous Underwater Vehicles (AUVs), is enabling coordinated cross-domain operations across transportation, defense, environmental monitoring, and emergency response applications. However, the integration of such heterogeneous platforms introduces significant cybersecurity challenges arising from complex architectures, diverse communication technologies, dynamic operational conditions, and data-driven autonomous decision-making. Existing cybersecurity studies and defense mechanisms often focus on specific autonomous platforms or limited security layers, providing insufficient analysis of integrated cybersecurity interactions across heterogeneous UAV, CAV, and AUV environments. In this survey, we present a comprehensive analysis of cybersecurity threats, vulnerabilities, and defense mechanisms for integrated UAV, CAV, and AUV systems across single-domain, dual-domain, and fully integrated tri-domain operational scenarios. The survey systematically examines security challenges spanning physical, sensing, communication, network, control, and application layers, together with cross-domain attack propagation and collaborative security dependencies. In addition, we review machine learning-based intrusion detection techniques for integrated UAV, CAV, and AUV systems and present HybridEnsemble-ID as an example of a hybrid cybersecurity framework that integrates supervised, unsupervised, semi-supervised, and reinforcement learning approaches for adaptive threat detection in heterogeneous autonomous environments. Furthermore, we consolidate publicly available datasets, simulation platforms, emulators, and hardware-in-the-loop testbeds to support reproducible evaluation and benchmarking of cybersecurity mechanisms for integrated autonomous systems. Finally, we identify open challenges and future research directions, including unified cross-domain evaluation frameworks, deployment-oriented security validation, adaptive defense architectures, and standardized benchmarking methodologies for resilient cross-domain autonomous operations.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6512-6551"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148372233","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}

{"title":"Cooperative Safety Intelligence Over V2X Networks: A Survey","authors":"Jiaxun Zhang;Qian Xu;Zhenning Li;Yuan Wu;Chengzhong Xu;Keqiang Li","doi":"10.1109/COMST.2026.3723946","DOIUrl":"10.1109/COMST.2026.3723946","url":null,"abstract":"Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into a networked intelligence problem involving distributed sensing, cooperative perception, and coordinated decision-making. This survey reviews recent progress in V2X-enabled cooperative safety intelligence through a unified Sensor–Perception–Decision (SPD) framework, which characterizes how distributed observations are exchanged, fused, calibrated, and transformed into safety-ready evidence for risk-aware intervention. Within this framework, V2X networking conditions, multi-modal sensing, cooperative perception, and decision-making are analyzed as coupled components of a safety-intelligence pipeline. Cross-layer communication and networking constraints, including latency, synchronization, bandwidth, packet or feature loss, reliability, trust, and PQoS, are explicitly considered to assess whether shared evidence remains timely, robust, and actionable. Compared with prior V2X safety surveys, this work organizes the literature around a formal SPD safety loop and synthesizes representative methods, datasets, benchmarks, and platforms under communication-aware constraints from 2017 to 2026. It further examines semantic and task-oriented evidence exchange, bandwidth-efficient cooperative perception, ISAC-enabled V2X systems, safety-ready outputs, and evaluation practices under practical conditions such as delayed or lossy messages, pose misalignment, NLOS coverage, and heterogeneous deployment. The survey concludes with a roadmap toward scalable data infrastructure, PQoS-aware evaluation, embodied predictive intelligence, and trustworthy human-in-the-loop cooperation for next-generation V2X safety systems.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6836-6867"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148728600","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}

{"title":"Point Cloud for Wireless Communications: A Comprehensive Survey on Fundamentals, Techniques, and Applications","authors":"Yu Zheng;Kezhi Wang;Zhicheng Du;Wenji Xi;Gang Yu;Jinbo Hou;Haonan Hu;Jie Zhang","doi":"10.1109/COMST.2026.3742692","DOIUrl":"10.1109/COMST.2026.3742692","url":null,"abstract":"Point cloud has recently emerged as a promising technology for next-generation wireless communication systems, offering precise geometric representations of complex environments. Despite growing interest, a comprehensive survey on its integration with wireless communications remains lacking. To bridge this gap, this survey provides a thorough review of state-of-the-art methods and applications of the point cloud across various wireless communication tasks over the last six years. We first introduce the fundamentals of point cloud for wireless communications, including acquisition techniques, preprocessing and representation methods, as well as open datasets and simulation tools. Subsequently, we investigate how point cloud effectively enhances key wireless communication tasks such as propagation channel modeling, blockage management, beam management, and proactive handoff. Additionally, emerging applications are extensively reviewed, including point cloud-enabled semantic wireless communications, wireless localization, metasurface coverage optimization, and digital-twin systems. Alongside these opportunities, we identify key challenges such as real-time processing of large-scale point clouds, robustness to environmental variations, multimodal fusion and calibration, generalization across diverse scenarios, the extension from single-user to multi-user wireless settings, the need for standardized benchmarking and open datasets, and associated privacy and security risks. We also outline promising research directions to guide the development of point cloud–enabled wireless networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"7544-7576"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149152365","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}

{"title":"Intrusion Detection and Prevention for Intra- and Inter-Vehicle Networks: A Comprehensive Survey","authors":"Aya El-Fatyany;Jabar Mahmood;Michael Abebe Berwo;Xiaohang Wang;Li Lu;Qiang Xue;Kui Ren","doi":"10.1109/COMST.2026.3674515","DOIUrl":"10.1109/COMST.2026.3674515","url":null,"abstract":"The security of intra- and inter-vehicle networks relies heavily on the implementation of intrusion detection systems (IDS) and intrusion prevention systems (IPS). These crucial technologies serve as the primary defence mechanisms against cyber threats, continuously monitoring and mitigating malicious activities within vehicles and across interconnected vehicular networks to ensure secure and reliable communication. This survey examines research works of IDS and IPS for intra- and inter-vehicle networks from 2020 to 2025, introducing an innovative taxonomy. This study analyzes various intrusion detection and prevention strategies, as well as attack surfaces, scenarios, and benchmark datasets for intra- and inter-vehicle networks. Furthermore, this paper emphasizes the necessity of collaborative IDS/IPS for both intra- and inter-vehicle networks and discuses directions for future research in this field.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5345-5378"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471239","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}

{"title":"Large Language Models for Optimization in Next-Generation Wireless Network Management: A Survey","authors":"Bisheng Wei;Ruihong Jiang;Ruichen Zhang;Yinqiu Liu;Dusit Niyato;Yaohua Sun;Yang Lu;Yonghui Li;Shiwen Mao;Chau Yuen;Marco Di Renzo;Mugen Peng","doi":"10.1109/COMST.2026.3682137","DOIUrl":"10.1109/COMST.2026.3682137","url":null,"abstract":"The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), face practical challenges in meeting stringent latency and scalability requirements, especially in large-scale, highly dynamic, and reconfiguration-sensitive deployments in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs’ transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5713-5746"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147664120","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}