Search

Smart city aims to enhance the efficacy of urban management and improve quality of citizen life. This however faces a fundamental difficulty due to the inadequate data sharing among social entities, which has become one of the key barriers to the in-depth development of smart city, making wasted information resources and application silos ubiquitous. As an emerging communication paradigm, the digital twin network (DTN) has potential to eliminate the data sharing barrier in smart city. By mapping the social physical entities (PEs) in smart city to digital virtual agents, namely data twins (DTs), valuable data can be directly shared among the DTs over DTN without physical constraints, and form decisions that feed back and guide the operation of PEs. In this article, we explore the role of DTN in supporting the smart city and promoting data sharing construction. We first introduce the basic concepts related to DTN and propose the architecture of DTN-enabled smart city. The key features of DTN-enabled smart city are then discussed with an emphasis on the security and privacy issues with fundamental solutions around data sharing in DTN-enabled smart city. As a case study, we investigate the data sharing in DTN-enabled smart city and design the secure and fair data sharing mechanism. Finally, we propose open challenges for achieving more reliable DTN-enabled smart city.

As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) has been envisioned to provide numerous intelligent applications that necessitate the cooperation of a multitude of terrestrial devices for machine learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL) offers a promising strategy for distributed training with enhanced data security and privacy. However, employing FL in SAGIN faces challenges in managing vast datasets, training complicated ML models, and ensuring security in long-distance transmission of ML models. In this article, we propose a quantum-empowered FL framework integrating variational quantum algorithms (VQA) and quantum relays in SAGIN. Our approach employs VQA-based ML for local training of FL, addressing the complexity emerging from vast datasets and ML models. Furthermore, supported by unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS), the proposed quantum relay scheme via quantum teleportation guarantees security in long-distance model transmission. We present a case study to validate the proposed VQA-based local training and quantum relaying model transmission. The numerical results demonstrate the feasibility and efficiency of the proposed VQA-based FL framework and the quantum relay-based model transmission. Our approach highlights the potential of integrating quantum techniques with FL in SAGIN, which can enable secure, efficient, and advanced edge intelligence applications.

Considering the overarching trend in mobile communication development, the extensive coverage and seamless connectivity offered by non-terrestrial networks (NTN) are becoming increasingly essential. NTN communications, characterized by extensive data volumes, hyper-connectivity, and large-scale fading, present notable challenges to traditional communication methods. This paper proposes a semantic communication-based NTN (SC-based NTN) architecture, highlighting its potential applications. However, the integration of semantic communication within NTN faces three key challenges, including multimodality, security, and resource allocation. This paper explores the opportunities presented by artificial intelligence, physical layer security, and intelligent resource management technologies, which promote multimodal, secure, and efficient communications for NTN. Finally, the paper outlines a visionary outlook for SC-based NTN, highlighting its potential impact on the future of mobile networks.

With the development of wireless communication technologies, the space-air-ocean integrated unmanned surface vehicle (USV) networks are significantly promising to provide various intelligent services, e.g., marine resource exploration, disaster rescue, maritime monitoring, etc. However, owing to the node diversity, data vulnerability and resource limitation, the USV networks face a series of security threats, e.g., eavesdropping attack and sybil attack, etc. In this article, to enhance the security of maritime communication networks, we investigate the security and privacy in space-air-ocean integrated USV networks. Specifically, we first introduce the architecture of space-air-ocean integrated USV networks and present its typical applications. Second, we discuss the security threats faced by space-air-ocean integrated USV networks in different application fields including sybil attack, collusion attack, and eavesdropping attack, etc. Third, a case study of space-air-ocean integrated USV networks regarding data backup perspective is presented to enhance the security and integrity of the stored data. Finally, we discuss the future research directions in this area.

Wireless federated learning (FL), which allows edge devices to perform local deep/machine learning (DL/ML) training and further aggregates the locally trained models from them via radio channels, establishes a promising framework for enabling various DL/ML-based services in future B5G/6G networks. Despite respecting the data privacy, periodically performing the local model training is not friendly to energy-constrained edge devices and degrades the sustainability and performance of FL services. In this article, motivated by the advanced simultaneous wireless information and power transfer (SWIPT), we propose a framework of SWIPT-empowered wireless FL that can provide over-the-air wireless power transfer in parallel with the transmission of global/local models. We present the key approaches of leveraging SWIPT for FL with their advantages illustrated. The practical challenging issues in reaping the benefits of integrating SWIPT are then discussed and we also provide the potential solutions to address these issues. A representative case study of FL via SWIPT is presented to validate the advantages of exploiting SWIPT. To this end, we present a joint design of SWIPT policy and the client-scheduling for FL, which is firstly formulated as a finite horizon dynamic optimization problem and then is solved by an actor-critic-based deep reinforcement learning algorithm. We finally articulate some potential open future directions regarding the SWIPT-empowered wireless FL.