{"title": "A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects", "authors": ["Yuntao Wang", "Zhou Su", "Shaolong Guo", "Minghui Dai", "Tom H. Luan", "Yiliang Liu"], "year": 2023, "publication_date": "2023-04-03", "doi": "10.1109/jiot.2023.3263909", "venue": "IEEE Internet of Things Journal", "volume": "10", "issue": "17", "pages": "14965-14987", "abstract": "By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as decentralized topology, information-centric routing and semantic communications entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3263909", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4362514681", "open_access": {"is_oa": true, "oa_status": "gold", "oa_url": "https://doi.org/10.36227/techrxiv.21972416", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "broad digital-twin survey; semantic communication is only peripheral context", "matched_topic_terms": ["semantic communication"]}
{"title": "Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics", "authors": ["Yahao Ding", "Zhaohui Yang", "Quoc‐Viet Pham", "Ye Hu", "Zhaoyang Zhang", "Mohammad Shikh‐Bahaei"], "year": 2023, "publication_date": "2023-12-11", "doi": "10.1109/jiot.2023.3341307", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "5", "pages": "7447-7473", "abstract": "The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3341307", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4389538543", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "distributed-learning survey/overview without a semantic communication link design", "matched_topic_terms": ["semantic communication"]}
{"title": "Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions", "authors": ["Ali Kashif Bashir", "Nancy Victor", "Sweta Bhattacharya", "Thien Huynh‐The", "Rajeswari Chengoden", "Gokul Yenduri", "Praveen Kumar Reddy Maddikunta", "Quoc‐Viet Pham", "Thippa Reddy Gadekallu", "Madhusanka Liyanage"], "year": 2023, "publication_date": "2023-08-14", "doi": "10.1109/jiot.2023.3304790", "venue": "IEEE Internet of Things Journal", "volume": "10", "issue": "24", "pages": "21873-21891", "abstract": "Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3304790", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4385819771", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "healthcare-metaverse federated-learning survey outside the communication-system scope", "matched_topic_terms": ["semantic communication"]}
{"title": "Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems", "authors": ["Bui Duc Son", "Tiến Hoa Nguyễn", "Trinh Van Chien", "Waqas Khalid", "Mohamed Amine Ferrag", "Wan Choi", "Merouane Abdelkader Debbah"], "year": 2024, "publication_date": "2024-03-06", "doi": "10.1109/jiot.2024.3373808", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "11", "pages": "19168-19187", "abstract": "The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3373808", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4392523536", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "security survey; semantic communication appears as background rather than the studied link", "matched_topic_terms": ["semantic communication"]}
{"title": "Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Survey", "authors": ["Thai-Hoc Vu", "Senthil Kumar Jagatheesaperumal", "Minh-Duong Nguyen", "Nguyễn Văn Huynh", "Sunghwan Kim", "Quoc‐Viet Pham"], "year": 2024, "publication_date": "2024-10-29", "doi": "10.1109/jiot.2024.3487627", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "2", "pages": "1266-1290", "abstract": "The success of artificial intelligence (AI) in multiple disciplines and vertical domains in recent years has promoted the evolution of mobile networking and the future Internet toward an AI-integrated Internet of Things (IoT) era. Nevertheless, most AI techniques rely on data generated by physical devices (e.g., mobile devices and network nodes) or specific applications (e.g., fitness trackers and mobile gaming). Therefore, generative AI (GAI), a.k.a. AI-generated content (AIGC), has emerged as a powerful AI paradigm; thanks to its ability to efficiently learn complex data distributions and generate synthetic data to represent the original data in various forms. This impressive feature is projected to transform the management of mobile networking and diversify the current services and applications provided. On this basis, this work presents a concise tutorial on the role of GAIs in mobile and wireless networking. In particular, this survey first provides the fundamentals of GAI and representative GAI models, serving as an essential preliminary to the understanding of GAI’s applications in mobile and wireless networking. Then, this work provides a comprehensive review of state-of-the-art studies and GAI applications in network management, wireless security, semantic communication, and lessons learned from the open literature. Finally, this work summarizes the current research on GAI for mobile and wireless networking by outlining important challenges that need to be resolved to facilitate the development and applicability of GAI in this edge-cutting area.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3487627", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4403863507", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "broad wireless GAI survey retained only as a search trace", "matched_topic_terms": ["semantic communication"]}
{"title": "CASIT: Collective Intelligent Agent System for Internet of Things", "authors": ["Ningze Zhong", "Yi Wang", "Rui Xiong", "Yingyue Zheng", "Yang Li", "Mingjun Ouyang", "Dan Shen", "Xiangwei Zhu"], "year": 2024, "publication_date": "2024-02-19", "doi": "10.1109/jiot.2024.3366906", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "11", "pages": "19646-19656", "abstract": "In the last few years, the bottleneck of bandwidth in Internet of Thing (IoT) has driven expectations to figure out new ways to preprocess the information needed to be transmitted. The ways which were used before are not smart enough and they cannot align to the users’ need. Large language model (LLM)-based intelligent agent is a very hot concept in AI community, which aims to save various problems via adapting LLM to different industries. In this article, we present a collective intelligent agent system for the IoT (CASIT) that is a pioneering LLM-agent-based IoT system. We put forward a IoT framework that can be used to lots of scenarios. CASIT refers to a system based on multiple intelligent LLM agents, which realizes complex tasks through cooperation and makes full use of collective intelligence. In order to solve the problems, we designed the Memory Mechanism and Summary Mechanism that enable LLMs to efficiently process the data by comparing historical data with Local Knowledge and Chat History in the prompt. After experimental verification, we have found that our framework could accurately conclude the abnormal information, and it outperforms the single LLM system when we input 200 sets of temperature and humidity data from five different places. The system provides a new solution and method for information processing in all IoT systems. Our framework may also provide refreshing ideas for edge computing and semantic communication.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3366906", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4391929663", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "IoT agent framework without an evaluated semantic/JSCC transmission chain", "matched_topic_terms": ["semantic communication"]}
{"title": "Joint Sensing, Communication, and Computation in UAV-Assisted Systems", "authors": ["Hao Xie", "Tiankui Zhang", "Xiaoxia Xu", "Dingcheng Yang", "Yuanwei Liu"], "year": 2024, "publication_date": "2024-03-07", "doi": "10.1109/jiot.2024.3362937", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "18", "pages": "29412-29426", "abstract": "This paper proposes a joint sensing, communication and computation (JSCC) framework in unmanned aerial vehicle (UAV)-assisted systems, where multi-functional terminal devices (TDs) can perform high-accuracy radar sensing as well as offload computation data to an airborne mobile edge computing (MEC) server over the same frequency band. The key objective of the JSCC framework is to simultaneously minimize the transmitted sensing beampattern matching error whilst maximizing the minimum computation efficiency of TDs. This problem is formulated as a multi-objective optimization problem (MOOP) that jointly optimizes the transmit beampattern, computation offloading, and UAV trajectory. To achieve the computation-sensing trade-off region, we first transform the MOOP into a single-objective optimization problem (SOOP) via the 1-constraint method. To make it more tractable, a generalized Dinkelbach’s and successive convex approximation (GD-SCA) algorithm is proposed. Specifically, GD-SCA transfers the non-convex max-min fractional programming in the resultant SOOP by introducing a general auxiliary polynomial via generalized Dinkelbach’s algorithm. Thereafter, the transmit beampattern, computation offloading, and UAV trajectory optimization are decoupled into two nested subproblems, which can be iteratively solved by invoking successive convex approximation (SCA) method to handle the remaining non-convex components. The proposed GD-SCA can obtain high-quality suboptimal solutions of the original MOOP. We validate the effectiveness of the proposed algorithm by considering two multiple access techniques, i.e., non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA). Simulation results demonstrate that the proposed algorithm can achieve an improved computation-sensing trade-off region compared to conventional schemes especially when exploiting NOMA. Moreover, the multi-functional performance can be significantly improved while stringently guaranteeing both radar sensing and computation offloading requirements.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3362937", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4392550549", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "integrated-system paper without a semantic representation or DeepJSCC mechanism", "matched_topic_terms": ["JSCC"]}
{"title": "SSFU: Selective Semantic Feature Unlearning for Federated Learning in 6G Internet of Things Systems", "authors": ["Wathsara Daluwatta", "Ibrahim Khalil", "Shehan Edirimannage", "Charitha Elvitigala", "Jer Shyuan Ng", "Dusit Tao Niyato"], "year": 2025, "publication_date": "2025-10-27", "doi": "10.1109/jiot.2025.3625756", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "1", "pages": "852-867", "abstract": "In next-generation 6G Internet-of-Things (IoT) networks, semantic communication has emerged as a key paradigm that transforms raw data into high-level feature representations, thereby reducing communication overhead while enhancing interpretability. When combined with federated learning (FL), these semantic embeddings enable decentralized model training without centralizing raw data, preserving user privacy, and supporting large-scale collaboration. However, semantic features may inadvertently encode sensitive information or act as adversarial triggers, introducing new privacy risks that current unlearning techniques fail to address. To overcome this challenge, we propose Selective Semantic Feature Unlearning (SSFU), a novel framework that performs unlearning at the feature level rather than at the client level. SSFU employs an ensemble-based risk scoring mechanism to identify high-risk latent components, followed by gradient ascent and semantic masking to remove their influence. Unlike existing methods that depend on costly retraining or full client exclusion, SSFU preserves benign semantic knowledge and allows training to continue with minimal disruption. The framework guarantees bounded convergence, and empirical results on benchmark datasets show that SSFU effectively eliminates sensitive features while maintaining predictive accuracy. SSFU thus represents a robust, privacy-preserving FL framework tailored for semantic communication in 6G IoT systems.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3625756", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4415593904", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "federated unlearning paper; communication semantics are not the transmitted-object contribution", "matched_topic_terms": ["semantic communication"]}
{"title": "Artificial Intelligence of Things as a Foundation for Agentic AI Systems: Architectures, Applications, and Challenges", "authors": ["Thippa Reddy Gadekallu", "Yongkang Zhao", "Zhihao Wen", "Pronaya Bhattacharya", "Yuchao Xia", "Jijing Cai", "Engin Zeydan", "Kai Fang", "Hailin Feng"], "year": 2026, "publication_date": "2026-06-15", "doi": "10.1109/jiot.2026.3703862", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "14", "pages": "30287-30302", "abstract": "The evolution of artificial intelligence has reached a critical point, where agentic AI systems demonstrate strong capabilities in goal formulation and planning but remain difficult to deploy in real-world settings due to their limited grounding in physical environments. These limitations arise from the challenges of partial observability, actuation uncertainty, and strict resource constraints that characterize the physical world. This survey argues that the Artificial Intelligence of Things (AIoT) provides the necessary foundation to embed agentic intelligence into such environments by enabling continuous interaction between sensing, reasoning, and action. We analyze the synergy between goal-driven agentic AI and distributed AIoT infrastructures and present a unified taxonomy of AIoT-enabled agentic architectures, highlighting trade-offs across centralized, edge-native, and hybrid deployment models. The survey further examines key enabling technologies, including edge intelligence, semantic communication, digital twins, and trust mechanisms, and discusses how they integrate into cognitive control loops. Through representative applications in smart cities, industrial automation, healthcare, and energy systems, we show how this convergence moves automation beyond rule-based behavior toward context-aware autonomy. Finally, we identify open challenges related to long-horizon safety, resource-aware intelligence, and ethical governance, and outline research directions toward robust, trustworthy, and socially embedded autonomous systems.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3703862", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7164815410", "open_access": {"is_oa": true, "oa_status": "gold", "oa_url": "https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.176972132.21250188", "any_repository_has_fulltext": null}, "oa_pdf_url": "", "record_type": "article", "screening_status": "excluded", "screening_priority": "medium", "screening_reason": "AIoT architecture survey outside the semantic-link/DeepJSCC scope", "matched_topic_terms": ["semantic communication"]}