IEEE Communications Surveys and Tutorials最新文献

{"title":"Integrating Communication, Sensing and Security: Progress and Prospects of PLS in ISAC Systems","authors":"Waqas Aman;Elmehdi Illi;Saif Al-Kuwari;Marwa Qaraqe","doi":"10.1109/COMST.2026.3691091","DOIUrl":"https://doi.org/10.1109/COMST.2026.3691091","url":null,"abstract":"The sixth generation of wireless networks (6G) defines several key performance indicators (KPIs) for assessing its networks, mainly in terms of reliability, coverage, and sensing. In this regard, remarkable attention has been paid recently to the integrated sensing and communication (ISAC) paradigm as an enabler for efficiently and jointly performing communication and sensing using the same spectrum and hardware resources. On the other hand, ensuring communication and data security has been an imperative requirement for wireless networks throughout their evolution. The physical-layer security (PLS) concept paved the way to catering to the security needs in wireless networks in a sustainable way while guaranteeing theoretically secure transmissions, independently of the computational capacity of adversaries. Therefore, it is of paramount importance to consider a balanced trade-off between communication reliability, sensing, and security in future networks, such as the fifth generation of wireless networks (5G) and beyond and the 6G. In this paper, a comprehensive and system-wise review of designed secure ISAC systems, from a PLS point of view, is provided. In particular, the impact of various physical-layer techniques, schemes, and wireless technologies to ensure the sensing-security trade-off is studied from the surveyed work. In addition, the review is enriched by notable takeaways on each transmission technique and wireless technology’s potential and limitations in establishing robust and secure ISAC networks. Different from previous related surveys, the current work reviews and categorizes PLS schemes in the context of ISAC under three main categories, namely confidentiality, communication covertness, and authentication, covering a broader secrecy perspective with different types of attacks, such as eavesdropping, sensing spoofing, and jamming. The paper also serves as a tutorial by presenting several theoretical foundations on secure ISAC networks, which represents a practical guide for readers to develop novel secure ISAC network designs.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6318-6353"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003650","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":"GNSS Jamming and Spoofing Threats in UAV Navigation: Countermeasure Status and Challenges","authors":"Yejia Zeng;Zukun Lu;Xiaoyu Zhao;Zhu Xiao;Shaojie Ni;Zhu Han;Keqin Li","doi":"10.1109/COMST.2026.3680438","DOIUrl":"10.1109/COMST.2026.3680438","url":null,"abstract":"Uncrewed aerial vehicles (UAVs) have become indispensable in both civilian and military applications. However, their reliance on Global Navigation Satellite System (GNSS)-based navigation exposes them to escalating threats from sophisticated jamming and spoofing attacks. These threats severely compromise operational safety and mission integrity, necessitating the development of effective countermeasures. This survey provides a comprehensive overview of GNSS interference threats specific to UAV navigation, with a focus on the analysis of jamming and spoofing attacks, and summarizes state-of-the-art techniques for interference detection and mitigation. We first review the principles of satellite navigation for UAVs and identify the inherent vulnerability of GNSS signals to interference. Then, we systematically examine existing countermeasures, including signal processing-based, array antenna-based, and artificial intelligence-based approaches, highlighting their effectiveness against jamming and spoofing. Despite these advances, significant challenges remain in ensuring robust UAV navigation in adversarial electromagnetic environments, particularly those arising from resource constraints, limited algorithmic flexibility, and the lack of UAV-specific countermeasures. To address these issues, we propose a hierarchical framework that integrates robust signal reception, intelligent algorithm optimization, and system-level collaboration. This framework provides practical strategies for enhancing the resilience of next-generation UAV navigation systems to complex interference.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"5909-5948"},"PeriodicalIF":34.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11474471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147617621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Pinching-Antenna Systems: Optimization Techniques, Intelligent Designs, and Multifunctional Applications","authors":"Yang Lu;Weihao Mao;Xinke Xie;Shuhai Liao;Yanqing Xu;Yushen Lin;Ruichen Zhang;Bo Ai;Xianbin Wang;Zhiguo Ding","doi":"10.1109/COMST.2026.3710938","DOIUrl":"10.1109/COMST.2026.3710938","url":null,"abstract":"Mobile communication systems are undergoing a hardware-driven technological evolution, aiming to enhance the controllability and programmability of wireless propagation. Recently, pinching antennas (PAs) have emerged as an innovative flexible-antenna technology, capable of dynamically adjusting PA positions along waveguides to construct line-of-sight (LoS) links and mitigate large-scale path loss and LoS blockage. In parallel, integrating artificial intelligence (AI) into wireless system designs and enabling multifunctional wireless services have become an inevitable trend. PA systems particularly necessitate deep learning (DL)-enabled approaches due to their inherent optimization complexity, while PAs’ ability to customize wireless channel characteristics supports the deployment of multifunctional services. This article surveys the state-of-the-art studies to provide a comprehensive overview PAs. We first outline the fundamental principles of PAs, classify existing PA systems based on PA configurations, implementation schemes, and channel models, and summarize the key analysis results. Subsequently, we present the reviews of classic optimization techniques for PA-enabled information transmission, non-orthogonal multiple access (NOMA) schemes, and beyond fifth-generation (B5G) technologies. Next, we introduce DL-enabled PA designs, covering unsupervised learning, deep reinforcement learning (DRL), and convex-aided DL frameworks. We further summarize PA-enabled multifunctional service designs tailored to scenarios including information security, wireless powered networks, and integrated sensing and communication (ISAC). Finally, we highlight the critical challenges, open issues, and promising future research directions for PAs.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"6592-6627"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401329","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":"Reliable and Efficient Data Collection in UAV-Based IoT Networks","authors":"Joshi Poorvi;Alakesh Kalita;Mohan Gurusamy","doi":"10.1109/COMST.2025.3550274","DOIUrl":"10.1109/COMST.2025.3550274","url":null,"abstract":"IoT involves sensors for monitoring and wireless networks for efficient communication. However, resource-constrained IoT devices and limitations in existing wireless technologies hinder its full potential. Integrating UAVs into IoT networks can address some challenges by expanding its coverage, providing security, and bringing computing closer to IoT devices. Nevertheless, effective data collection in UAV-assisted IoT networks is hampered by factors, including dynamic UAV behaviour, environmental variables, connectivity instability, and security considerations. In this survey, we first explore various communication technologies for UAV-IoT networks, performance metrics, and simulation tools, followed by a categorization of the UAV-IoT system based on the service model of UAV, mode of operation of UAV and UE during data collection, and network topology. As this article primarily emphasizes reliable and efficient data collection in UAV-assisted IoT networks, we briefly discuss existing research on data accuracy and consistency, network connectivity, and data security and privacy to provide insights into reliable data collection. Additionally, we discuss efficient data collection strategies in UAV-based IoT networks, covering trajectory planning and constraint management, sensor network clustering, data aggregation, and UAV swarm formations. Further, advanced AI techniques are discussed, which enable efficient and reliable data transfer in UAV-IoT systems. Finally, we discuss future challenges and their possible solutions using different approaches such as Decentralized Reinforcement and Federated Learning, Generative AI, and hybrid optical-RF communication links with integration of blockchain for reliable, efficient, and secure data collection in UAV-assisted IoT networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"2531-2571"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599154","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 Survey on Intelligent Predictive Maintenance (IPdM) in the Era of Fully Connected Intelligence","authors":"Tianwen Zhu;Yongyi Ran;Xin Zhou;Yonggang Wen","doi":"10.1109/COMST.2025.3567802","DOIUrl":"10.1109/COMST.2025.3567802","url":null,"abstract":"Predictive Maintenance (PdM) refers to a maintenance paradigm that performs maintenance only after the analytical models predict certain failures or degradations. While traditional PdM has improved maintenance efficiency, its scalability is constrained by network limitations, resource allocation challenges, and the computational demands of AI-driven analytics. With the help of fully connected intelligence, Intelligent Predictive Maintenance (IPdM) overcomes these limitations by leveraging 6G, cloud-edge computing, and AI advancements, enabling a more adaptive and intelligent PdM framework. This survey contributes to the burgeoning IPdM academia from the perspective of communication and networking. Firstly, we explore how communication evolutions and network advancements facilitate IPdM deployment by presenting an integrated cloud-edge-device IPdM framework. Under this framework, we analyze the IPdM lifecycle and explore the challenges and solutions within each stage. Particularly, we discuss the novel technical enablers for IPdM in communication an AI. Afterwards, we explore how IPdM helps communication and networking by surveying the applications of IPdM in the regarding fields, such as data centres and sensor networks. Finally, we summarize the crucial future directions for promoting the further applications of IPdM in next-generation networking. We believe this work not only contributes to the academic discourse on IPdM but also offers valuable insights for industry professionals navigating the fourth industrial revolution.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"633-671"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920243","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":"Blockchain Security Risk Assessment in Quantum Era, Migration Strategies, and Proactive Defense","authors":"Yaser Baseri;Abdelhakim Hafid;Yahya Shahsavari;Dimitrios Makrakis;Hassan Khodaiemehr","doi":"10.1109/COMST.2025.3621113","DOIUrl":"10.1109/COMST.2025.3621113","url":null,"abstract":"The advent of Quantum Computing (QC) poses significant threats to the cryptographic foundations of Blockchain (BC) systems, as quantum algorithms like Shor’s and Grover’s undermine the security of public-key cryptography and hash functions. This research conducts a comprehensive risk assessment of quantum vulnerabilities across critical BC components, including consensus mechanisms, smart contracts, and digital wallets. Leveraging the STRIDE threat modeling framework, we analyze threat vectors specific to QC, identifying key areas most susceptible to quantum-enabled attacks, such as private key compromise, consensus disruptions, and smart contract integrity risks. Our contributions provide actionable mitigation strategies, including a detailed security blueprint for quantum resilience, encompassing the integration of Post-Quantum Cryptography (PQC) and the adoption of quantum-resistant hash functions. We offer implementation best practices, focusing on key management, secure coding, and network security to strengthen BC components against quantum threats. To mitigate the risk of QC during transition from classical to quantum-resistant BCs, we present two hybrid BC architectures. As part of a comprehensive quantum resilience strategy, these architectures facilitate a secure and scalable migration by integrating platform-specific adaptations that balance security, adaptability, and operational efficiency. Our analysis extends to major BC platforms, including Bitcoin, Ethereum, Ripple, Litecoin, and Zcash, providing platform-specific vulnerability assessments and highlighting unique weaknesses in the quantum era. By identifying vulnerabilities, developing proactive defense strategies, and adopting a structured hybrid migration approach, this research equips BC stakeholders with a robust framework to achieve long-term quantum resilience. Finally, we explore challenges and research directions for integrating emerging technologies, including quantum machine learning, Artificial Intelligence (AI), and Web3, with BC systems, and discuss new threats that may arise from this convergence in the QC era.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"2925-2964"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145282627","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":"Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey","authors":"Honglei Ma;Erwu Liu;Wei Ni;Zhijun Fang;Rui Wang;Yongbin Gao;Dusit Niyato;Ekram Hossain","doi":"10.1109/COMST.2025.3623258","DOIUrl":"10.1109/COMST.2025.3623258","url":null,"abstract":"Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellent penetration capabilities. Integration with Space-Air-Ground-Underground (SAGUI) networks in next-generation mobile communication systems requires a well-defined network architecture. A recent discovery in MIC research, MI fast fading, remains in its early stages and presents unique challenges. This paper provides a comprehensive survey on through-the-earth (TTE) MIC, covering MI applications, channel modeling, point-to-point MIC design, relay techniques, network frameworks, and emerging technologies. We compare various MIC applications to highlight TTE-specific challenges and review the principles of channel modeling, addressing both MI slow fading and MI fast fading, along with its potential impact on existing MIC theories. We conduct a fine-grained decomposition of MI channel power gain into four distinct physical parameters, and propose a novel geometric model to analyze MI fast fading. We also summarize MI relay techniques, examine crosstalk effects in relay and high-density networks, and explore key research tasks within the OSI framework for a holistic MI network protocol in SAGUI. To bridge the gaps identified, we propose a MIC framework that supports TCP/IP and Linux, enabling full implementation of existing and emerging MIC solutions. This framework empowers researchers to leverage Linux resources and deep learning platforms for accelerated development of MIC in SAGUI networks. Remaining research challenges, open issues, and promising novel techniques are further identified to advance MIC research.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"2263-2305"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145397778","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":"When Federated Learning Meets Quantum Computing: Survey and Research Opportunities","authors":"Aakar Mathur;Ashish Gupta;Sajal K. Das","doi":"10.1109/COMST.2025.3634143","DOIUrl":"10.1109/COMST.2025.3634143","url":null,"abstract":"Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"1351-1380"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545647","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":"Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions","authors":"Bo Yang;Ruihuai Liang;Weixin Li;Han Wang;Xuelin Cao;Zhiwen Yu;Samson Lasaulce;Mérouane Debbah;Mohamed-Slim Alouini;H. Vincent Poor;Chau Yuen","doi":"10.1109/COMST.2026.3656241","DOIUrl":"10.1109/COMST.2026.3656241","url":null,"abstract":"While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI’s role in network optimization. We focus on the two dominant paradigms of generative models, including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and organize our discussion around a categorization we introduce, dividing network optimization problems into two primary formulations: one-shot optimization and Markov decision process (MDP). We first trace key developments, including foundational contributions from the broader AI community, and systematically categorize current efforts in networking. We also briefly review frontier applications of GDMs and LPTMs in other related networking tasks, providing additional context for this survey. Building on this, we present theoretical generalization bounds for GDMs in both one-shot and MDP settings. The established bounds also enable an equivalent analysis of LPTMs. Most importantly, we reflect on the overestimated perception of GenAI’s general capabilities and caution against the all-in-one illusion it may convey. We highlight critical limitations, including difficulties in satisfying hard constraints, limited concept understanding, and the inherently probabilistic nature of outputs. We also propose key directions for future research, such as bridging the gap between generation and optimization. Although these two tasks are increasingly integrated in practical applications, they differ fundamentally in both objectives and underlying mechanisms, necessitating a deeper understanding of their theoretical connections. Ultimately, this survey aims to provide researchers and practitioners with a structured overview and a deeper insight into the strengths, limitations, and potential of GenAI in network optimization.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"4555-4580"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146042882","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":"Zero-Knowledge Proof-Based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey","authors":"Zhibo Xing;Zijian Zhang;Ziang Zhang;Zhen Li;Meng Li;Jiamou Liu;Zongyang Zhang;Yi Zhao;Qi Sun;Liehuang Zhu;Giovanni Russello","doi":"10.1109/COMST.2025.3561657","DOIUrl":"10.1109/COMST.2025.3561657","url":null,"abstract":"Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Simultaneously, the growth of communication networks has facilitated the efficient collection of large-scale training data. Traditional centralized machine learning, however, requires collecting data from users, raising significant concerns about privacy and security. Decentralized approaches, where participants exchange computation results instead of raw private data, mitigate these risks but introduce challenges related to trust and verifiability. A critical issue arises: How can one ensure the integrity and validity of computation results shared by other participants? Existing survey articles predominantly address security and privacy concerns in decentralized machine learning, whereas this survey uniquely highlights the emerging issue of verifiability. Recognizing the critical role of zero-knowledge proofs in ensuring verifiability, we present a comprehensive review of Zero-Knowledge Proof-based Verifiable Machine Learning (ZKP-VML). To clarify the research problem, we present a definition of ZKP-VML consisting of four algorithms and several key security properties. In addition, we provide an overview of the current research landscape by systematically organizing the research timeline and categorizing existing schemes based on their security properties. Furthermore, through an in-depth analysis of each existing scheme, we summarize their technical contributions and optimization strategies, aiming to uncover common design principles underlying ZKP-VML schemes. Building on the reviews and analysis presented, we identify current research challenges and suggest future research directions. To the best of our knowledge, this is the most comprehensive survey to date on verifiable decentralized machine learning and ZKP-VML.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"28 ","pages":"985-1024"},"PeriodicalIF":50.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841642","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}