Smart city (SC) technologies can provide appropriate services according to citizens’ demands. One of the key enablers in a SC is the Internet of Things (IoT) technology, which enables a massive number of devices to connect with each other. However, these devices usually come from different manufacturers with different product standards, which confront interactive control problems. Moreover, these devices will produce large amounts of data, and efficiently analyzing these data for intelligent services. In this paper, we propose a novel artificial intelligence-based semantic IoT (AI-SIoT) hybrid service architecture to integrate heterogeneous IoT devices to support intelligent services. In particular, the proposed architecture is empowered by semantic and AI technologies, which enable flexible connections among heterogeneous devices. The AI technology can support very implement efficient data analysis and make accurate decisions on service provisions in various kinds. Furthermore, we also present several practical use cases of the proposed AI-SIoT architecture and the opportunities and challenges to implement the proposed AI-SIoT for future SCs are also discussed.

This letter studies a reconfigurable intelligent surface (RIS)-aided integrated communication, sensing and power transfer systems. We aim to maximize the received energy from the energy harvester by jointly optimizing the (active) transmit beamforming at the BS and (passive) reflect beamforming by the phase shift of the RIS, subject to the Cramér-Rao bound for the target, the received signal-to-noise ratio (SNR) at the information decoding receiver and the transmit power constraint at the BS. To solve this non-convex problem with coupled variables, we decouple it into two subproblems and introduce an alternating optimization framework. The transmit beamforming sub-problem is recast into a convex form using the Schur complement and semidefinite relaxation techniques. Subsequently, auxiliary variables are introduced to transform the phase shift sub-problem into a convex problem. Simulation results show that the proposed algorithm can significantly improve the received energy while ensuring communication and sensing performance.

In this paper, we present a comprehensive approach that leverages the capabilities of blockchain technology to protect private data in 6G UAV networks. We propose a dual-layer structure UAV swarm model based on blockchain, aiming to enhance the security during collaborative mission execution and data sharing between UAVs, and ensure data integrity through the decentralized ledger of blockchain security and access control. In addition, we study a dynamic trust assessment method that can evaluate the trustworthiness of UAV nodes, demonstrate adaptability to network fluctuations and potential attacks by evaluating various trust parameters and dynamically adjusting trust propagation and update strategies, while combining district Blockchain technology to identify and mitigate malicious attacks. We also investigate a tailored lightweight blockchain solution designed for UAV networks with constrained resources, aiming to boost efficiency via streamlined consensus protocols, intelligent allocation of computing resources, and decentralized storage strategies. The result is a safe and efficient data sharing platform customized to meet the unique needs of UAVs. Together, these strategies provide a powerful framework to fully protect private data in 6G UAV networks, leveraging the security and decentralization provided by blockchain technology.

We consider an amplify-and-forward relay network composed of a source, two relays, and a destination. In this network, the two relays are untrusted in the sense that they may perform Byzantine attacks by forwarding altered symbols to the destination. Note that every symbol received by the destination may be altered, and hence, no clean reference observation is available to the destination. For this network, we identify a large family of Byzantine attacks that can be detected in the physical layer. We further investigate how the channel conditions impact the detection against this family of attacks. In particular, we prove that all Byzantine attacks in this family can be detected with asymptotically small miss detection and false alarm probabilities by using a sufficiently large number of channel observations if and only if the network satisfies a non-manipulability condition. No pre-shared secret or secret transmission is needed for the detection of these attacks, demonstrating the value of this physical-layer security technique for counteracting Byzantine attacks.

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.

With the rapid development of intelligent connected vehicles (ICVs), their intelligent, remote, and high-speed mobility characteristics have led to an expansion of their attack surface, and thus an increase in security requirements. Consequently, security research for ICVs has become a current research focus. However, existing security risk monitoring measures lack linkage with attack behavior, and security defense strategies cannot fully and promptly respond to security risk levels and system vulnerabilities. Therefore, this paper proposes a security evaluation framework for ICVs based on attack chains. The framework focuses on the mechanism and steps of attack generation, evaluates the current risk level and degree of harm in a targeted manner, and maps appropriate security management measures according to the degree of harm. This security evaluation model integrates various defense mechanisms and resolves the mismatch between existing ICV security defense measures and security situations. The effectiveness of this model has been validated through experiments, and potential research directions in the future are discussed and analyzed.