The Satellite Internet of Things (SatIoT) enables integrated space–air–ground connectivity and information exchange through satellite communication networks and distributed sensors. Its primary advantage lies in achieving global coverage, effectively addressing communication challenges in signal-deprived regions. However, image transmission in SatIoT systems is severely constrained by limited satellite bandwidth, placing high demands on the efficiency and adaptability of compression algorithms. To address these challenges, this work adopts a co-design approach at both the algorithmic and system levels. At the algorithmic level, we implement a deep learning-based image compression framework. To improve the Rate-Distortion performance of the algorithm, 1) we propose a plug-and-play feature extraction module that integrates the strengths of Transformer and CNN to capture both global and local features, preserving critical image details while eliminating redundancy. 2) A Multi Channel Scaling Enhancement module is proposed, which can efficiently fuse features without performance loss and reduce the computational overhead through channel scaling. The experimental results show that they significantly improve the Rate-Distortion performance of mainstream compression algorithms with relatively low computational consumption. At the system level, we design a complete SatIoT communication chain comprising an edge terminal, the Tiantong satellite, and a ground receiving terminal. The optimized compression algorithm is deployed on the edge terminal to enable edge computing, supporting image acquisition, compression, and satellite-based transmission. The system achieves reliable, end-to-end image transmission from signal-deprived regions to ground networks, greatly improving the efficiency and robustness of satellite-based image delivery.

With cooperative positioning (CP) in Internet of Vehicles (IoV), the positioning performance can be improved by utilizing the positioning information provided by neighboring vehicles. However, in urban canyons, the CP performance based on global navigation satellite system (GNSS) is severely degraded by multipath and non-line-of-sight (NLOS) effects, and a robust CP algorithm is required. This article proposes a GNSS decentralized CP (DCP) algorithm based on the generalized extreme studentized deviate test (GESD) filter. Further, a new two-stage CP framework is established, consisting of two modules, the independent and connected modules. The independent module is the first filter layer detection for GNSS pseudorange residual error, operating independently within each vehicle to mitigate the impact of abnormal measurements, such as multipath bias and satellite faults. When receiving data from neighboring vehicles, the connected module is activated to detect shared GNSS pseudorange errors and relative pseudorange measurements, further reducing the impact of anomalous measurements. The outdoor experiments validate the superiority of the DCP algorithm over the single-point positioning (SPP) method based on receiver autonomous integrity monitoring fault detection and exclusion (RAIM-FDE) in both GPS-only and GPS/BDS combination strategies, especially in blocked situations. The positioning root-mean-square error (RMSE) of the DCP algorithm for horizontal positioning is about 1.00 m in static scenes and about 8.00 m in dynamic scenes. Compared with the SPP-RAIM, the DCP algorithm can improve about 25.22%–43.04% and 16.55%–40.17% on average under GPS/BDS combination strategies in the horizontal and 3-D directions, respectively.

Visual-inertial odometry (VIO) has played an important role in the field of the Internet of Things, providing a variety of devices and systems with high-precision and reliable positioning and navigation capabilities. In particular, indoor environments have become an important scenario for its application. However, the lack of robustness of point-based VIO systems in low-textured man-made environments often leads to failure. Based on this issue, this article proposes an innovative monocular VIO approach to fully utilize the available information in man-made environments. In the front end, the inertial measurement unit measurement model is defined by the preintegration method. In image data, first, the line features undergo a uniformization process, which reduces the redundant features and improves the accuracy of line feature matching. Then, the vanishing points in the image are detected using the Manhattan world assumption, and the structural line features are classified as either parallel or perpendicular to gravity based on vanishing points. In the back end, a novel residual term is defined for structural line features and gravity, deriving the corresponding Jacobian. This approach effectively addresses the issue of structural line degeneracy and continuously optimizes gravity, while also increasing the utilization of structural lines. A sliding window nonlinear optimization method is employed to minimize the sum of residuals. We tested the proposed system and the state-of-the-art VIO systems on both the public data sets and our collected data set to validate the effectiveness of the proposed system.

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.