As the Internet of Things (IoT) landscape continues to expand, a diverse range of devices with various functionalities is being integrated into the IoT ecosystem. When traditional systems, which involve human interaction, are replaced by devices, it becomes crucial to upgrade the conventional authorization and authentication mechanisms. Traditional approaches for device identification and anomaly detection often fail to address the dynamic behaviors of IoT devices due to the highly heterogeneous nature of the IoT environment. To address these challenges, this article proposes a novel and lightweight integrated model for simultaneous IoT device identification and anomaly detection. The proposed approach leverages both packet-based and flow-based feature extraction techniques to extract a diverse and significant set of features, which are crucial for robust anomaly detection and device classification. This novel combined feature set incorporates a wide range of attributes from various domains, including HTTPS-related features, handshake information, and user agent strings, specifically extracted for IoT device identification. In addition, the feature set includes specialized attributes for anomaly detection, such as stream, channel, and jitter metrics, which are calculated over different time intervals to enhance the model’s anomaly detection capabilities. Experimental analysis, conducted using real network traffic data from state-of-the-art datasets, demonstrates the model’s efficiency and scalability, which makes the model well-suited for real-time IoT threat detection and device management in resource-constrained environments.

Localized meteorological forecasting holds significant practical value for human socio-economic activities. Current deep learning-based localized meteorological prediction methods trained with structured reanalysis data have made great progress. However, the assimilation process from observational data to reanalysis data may introduce uncertainty errors. Meanwhile, the inherent sparsity and unstructured nature of observational data fail to reflect localized sparse meteorological conditions accurately. Therefore, these methods struggle to deliver precise localized meteorological forecasts. To address these issues, we propose a Heterogeneous Spatio-Temporal Graph Neural Network (HSTGNN) that integrates observational and reanalysis data for in-situ prediction of meteorological variables at localized sparse stations. Specifically, we first design a Hybrid Graph Constructor (HGC) to map sparse observational data and reanalysis data into heterogeneous nodes, establishing node association topologies through a dual-channel mechanism combining geographical graphs and adaptive learning graphs. We then develop a Hierarchical Heterogeneous Message Passing Module (H2MPM) to facilitate differentiated information interaction between heterogeneous data, effectively capturing spatial dependencies across data sources. Finally, we introduce a temporal linear unit based on single-layer linear regression to extract temporal dependencies and perform sequence prediction of meteorological variables at each station. Experimental results on two datasets demonstrate that HSTGNN comprehensively models localized meteorological pattern and achieves outstanding performance in in-situ meteorological variables prediction tasks.

Undoubtedly, smart elderly care systems can leverage Internet of Things (IoT) technology to enhance senior living services by integrating connected devices and sensors. Nevertheless, it is still a challenging issue to achieve secure and efficient multicast group communication among these IoT devices in smart elderly care systems, as IoT devices can dynamically become targets during multicast communication. To address this challenge, in this paper, we propose a new efficient multicast authenticated encryption scheme, which is characterized by integrating Merkle Tree, prefix encoding, XOR filters, and ASCON techniques to provide a robust solution for secure and personalized eldercare services in IoT-enabled environments. Security analysis demonstrates that our proposed scheme can satisfy the confidentiality and integrity requirements. In addition, the performance evaluation confirms that our proposed scheme is computationally efficient.

An extended GIHS (EGIHS) fusion approach is presented for merging the panchromatic (PAN) image and the multi-spectral (MS) image with model-based optimization (MBO). The pan-sharpened MS image is posed as the optimization solution of the proposed functional, which consists of two energy terms. The first energy term injects the details of the PAN image into the MS image with the generalized IHS (GIHS) fusion model and the second energy term preserves the spectral contents of the MS image by enforcing the low-pass version of the pan-sharpened image should be close to the original up-sampled MS image, in which the low-pass filter is developed based on the modulation transfer function (MTF) of the different MS band. The pan-sharpened MS images are decoupled in the proposed method, which allows an efficient implementation. Experiments on IKONOS and QuickBird datasets demonstrate that the proposed approach outperforms the state-of-the-art fusion models including IHS-based GIHS, GIHSA, IHS-BT, MBO-based Adaptive IHS and MRA-based AWLP.

A novel urban land-use/land-cover classification framework for very high resolution SAR images which combines scene description and scene-based mapping is presented in this paper. We use textural features for scene region description, including the traditional GLCM feature and the new scene scattering descriptor. The scene scattering descriptor is proposed in this paper to model the scattering behavior and context appearance of urban scenes. This work intends to implement an efficient description of urban land covers from the urban backscattering behavior perspective and apply it to scene-based mapping of urban areas. The framework demonstrates good performance in both accuracy and efficiency for urban scene interpretation of very high resolution SAR images.

A new pan-sharpening approach is proposed for merging the panchromatic image and the multi-spectral image by jointly using IHS and variational method. The substitute image of the intensity component of IHS transform is the optimization solution of the proposed energy functional. The functional consists of three energy terms. The first term extracts the spatial details from the Pan image and injects the details into the MS image. The second term preserves the spectral signature of the MS image by requiring that the low-resolution version of the substitute image should be close to the intensity component. The third term guarantees the smoothness of the optimization solution. The proposed method is validated on the IKONOS and QuickBird dataset. In comparison with the fusion models including extended fast IHS (eFIHS), AWLP and IHS-BT, the prospective fusion results are presented by the proposed model.