This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

Industrial Internet of Things (IIoT) is regarded as the basis of the future industrial system. The interaction between nodes in IIoT shows strong regularity or sociality, which has the potential to improve the efficiency of IIoT. This article focuses on the investigation of the relationship of social activity and data transmission, and proposes a novel routing protocol to enhance QoS of social and cognitive IIoT networks. In accordance with practical requirements of an application, the routing protocol owns two purposes: one is to meet the requirement of delay-sensitive information transmission, and the other focuses on the load balance of the social and cognitive IIoT network. As a special feature of social and cognitive IIoT, the mechanism for protection of social activities is proposed while data transmission is adopted in the proposed protocol. System-level evaluation shows that the proposed protocol can achieve better performances compared with existing routing protocols for social and cognitive IIoT networks.

Current remote sensing image captioning (RSIC) methods often struggle to provide accurate and comprehensive descriptions due to their reliance on networks designed for natural images. Due to limited domain-specific knowledge in remote sensing, these networks often fail to accurately reflect the intrinsic semantic information of remote sensing categories. This paper proposes a novel Knowledge-Embedded Remote Sensing Image Captioning model (KE-RSIC). We first define two types of remote sensing knowledge: general knowledge within the field of remote sensing, and specific knowledge that is relevant to the input image. To acquire general knowledge, we construct a remote sensing knowledge graph and propose a general knowledge embedding method, enabling semantic correlations between entities and relationships in remote sensing knowledge graphs. The generated entity embeddings and relationship embeddings can effectively capture the intrinsic semantic information of remote sensing categories. To acquire specific knowledge, we also propose a specific knowledge embedding method. We retrieve reports with similar label distributions to the input and then extract entities and relationships from the retrieved reports using a relation extractor. Embedding specific knowledge can alleviate to some extent the issue of poor matching between visual features and semantic features due to the lack of relevant knowledge. Subsequently, to integrate entity embeddings, relationship embeddings, and visual features, we propose a Visual Feature and Knowledge Information Dynamic Fusion module (VFKII). This module can efficiently combine the visual features of remote sensing images with structural information on embedded knowledge. Numerous experimental findings attest to the superiority and effectiveness of the proposed method.

In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data.

Remote sensing image captioning (RSIC) is an important task in environmental monitoring and disaster assessment. However, existing methods are constrained by redundant feature interference, insufficient multi-scale feature integration, and cross-modal semantic gaps, leading to limited performance in scenarios requiring fine-grained descriptions and semantic integrity, such as disaster assessment and emergency response. In this letter, we propose a Cross-Modal Semantic Alignment Model for Remote Sensing Image Captioning (CSA-RSIC), addressing these challenges with three innovations. First, we designed an Adaptive Feature Selection Module (AFSM) that generates channel weights through dual pooling. The AFSM dynamically weights the most informative features at each scale to improve caption accuracy. Second, we propose a Cross-Scale Feature Aggregation Module (CFAM) that constructs a hierarchical feature pyramid by aligning multi-scale resolutions and performs attention-guided fusion with enhanced weighting via AFSM, ensuring the effective integration of fine-grained and global semantic information. Finally, a novel loss function that combines contrastive learning and consistency loss is proposed to enhance the semantic alignment between visual and textual features. Experiments on three datasets show the advancement of CSA-RSIC over strong baselines, indicating its effectiveness in enhancing both semantic completeness and accuracy.