最新刊期
卷 10 ， 期 2 ， 2026
- 摘要：As the 6G era approaches, the number of Internet of things (IoT) terminal devices has rapidly increased, leading to a significant increase in battery consumption and maintenance costs. As a result, achieving net-zero energy consumption has become one of the key technical requirements for promoting the widespread adoption of IoT terminals. Visible light communication (VLC), as an emerging green communication technology in recent years, can effectively utilize existing lighting sources to carry downlink information, while terminal devices can use solar panels to simultaneously acquire both information and energy. However, traditional VLC uplinks typically rely on infrared communication technology, which imposes higher energy demands on terminal devices, making it difficult to achieve the goal of net-zero energy consumption. Accordingly, a novel net-zero energy IoT technology solution was proposed, which integrated solar-panel-based visible light communication with retroreflector-based passive visible light communication, and a prototype was constructed. This solution aimed to achieve net-zero energy consumption for IoT terminal devices by enhancing energy harvesting efficiency and reducing wireless transmission energy consumption. Experimental results showed that the prototype could achieve a transmission distance of over 50 cm under indoor natural lighting conditions, with downlink and uplink transmission rates reaching 4 300 bit/s and 50 bit/s, respectively, making it suitable for short-range, low-rate IoT application scenarios such as smart locks. Furthermore, due to the adoption of passive uplink transmission, the required solar panel area for achieving net-zero energy consumption in terminal devices was reduced from 615 mm² to 125 mm², significantly enhancing the deployment flexibility of the terminal devices.关键词：IoT;NZE;VLC;VLBC;solar energy harvesting78|25|0更新时间：2026-10-10
- 摘要：Unmanned aerial vehicle (UAV) is recognized as the core component of the low-altitude economy network. It is crucial to achieve efficient image transmission in air-to-ground communication. During mission execution, rapid channel fluctuations are induced by changes in the flight area, including variations in signal-to-noise ratio (SNR) and transmission rate. To adapt to these changes, a channel elastic encoding and decoding method for UAV image transmission was proposed. This method included a lightweight feature extraction module, a channel feature enhancement module, a transmission rate adaptive module, and symmetric decoding modules. The lightweight feature extraction module contained residual blocks and the mobile Mamba, which could quickly extract local detail features and global context. It could effectively obtain rich semantic features from aerial images. The feature enhancement module enhanced key semantic features according to the channel state SNR. The transmission rate adaptive module adjusted the size of transmitted features based on the channel transmission rate. Extensive experiments on the aerial image dataset (AID) demonstrate that the proposed method achieves higher transmission accuracy than the state-of-the-art methods.关键词：UAV aerial image transmission;SNR adaptation;rate adaptation;semantic communication129|60|0更新时间：2026-10-10
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Wireless radio frequency backscatter communication: current status, issues, and development trends AI导读
摘要：Wireless radio frequency backscatter is characterized by shared spectrum, low power consumption, and low cost, and it has important application value in the terminal devices and repeater markets of communication and sensing. Mobile communication technology has always been a hotspot of development. The transition from 5G to 6G is not only about speed but also about evolving from the Internet of things to the Internet of intelligent things. Both mobile communication technology and Internet of things technology face the problem of spectrum scarcity, there is also demand for low-power energy-saving design, creating intersections with wireless radio frequency backscatter communication. Firstly, by exploring research hotspots such as mobile communication and Internet of things technologies, the wireless radio frequency backscatter communication was introduced in this study. Secondly, through reviewing seminal papers on the environmental backscatter communication, recent research, and fundamental principles, a clear developmental trajectory was mapped out and the current state was categorized across different dimensions. Then, based on this developmental trajectory and classification, existing issues and partial solutions were identified. Finally, combining the analysis of the full text, the next phase of development trends were predicted in this study.关键词：backscatter;wireless radio frequency;review;mobile communication technology;internet of things157|102|0更新时间：2026-10-10 - 摘要：Cell-free massive multiple-input multiple-output (CF-mMIMO) systems, which eliminate inter-cell interference and enhance spectral efficiency, are considered as a key enabling technology for 6th generation mobile communications (6G). Meanwhile, reconfigurable intelligent surface (RIS), featuring low power consumption, programmability, and flexible deployment, offers significant potential for improving channel quality and extending coverage. In this paper, a RIS-assisted CF-mMIMO system was investigated, and a joint optimization problem involving power control, precoding, and RIS phase adjustment was formulated to maximize energy efficiency (EE). To tackle this non-convex multi-variable problem, an alternating optimization framework was proposed. Specifically, the precoding and RIS phase subproblems were decoupled and solved using the Lagrangian dual transform and fractional programming, while the power control subproblem was addressed via Newton method. Simulation results demonstrate that the proposed scheme achieves faster convergence and significantly outperforms the conventional uniform power allocation methods. Moreover, it simultaneously improves both energy efficiency and communication rate, effectively enabling the joint optimization of spectrum efficiency (SE) and EE.关键词：CF-mMIMO;RIS;power control optimization;EE190|291|0更新时间：2026-10-10
Topic\: Integrated Sensing\, Communication and Computing in 6G Cross\-Domain IoT
- 摘要：Ultra-wideband (UWB) radar enables people counting and vital signs monitoring with wireless sensing. However, existing methods rely on statistical features or lightweight neural networks, which have limited ability to migrate and generalize across multiple tasks. In view of the powerful cross-modal learning capability demonstrated by large language model (LLM), a UWB radar multi-task learning framework based on LLM was proposed. This approach successfully mapped the temporal features of radar signals into the embedding space of LLM, and by integrating the mixture of experts (MoE) mechanism and low-rank adaptation (LoRA) strategy, UWB-LLM performed parameter-efficient fine-tuning (PEFT) for multi-task learning. Experiments were conducted on both self-collected and publicly available datasets for people counting task as well as respiration, electrocardiogram (ECG), and continuous blood pressure estimation tasks, respectively. Compared with the state-of-the-art algorithms, UWB-LLM achieves average accuracy improvements of 37.62% for people counting and 7.47%, 18.16%, and 14.70% for the three vital sign estimation tasks.关键词：LLM;UWB radar;people counting;vital signs monitoring;multi-task learning;MoE232|107|0更新时间：2026-10-10
- 摘要：With the deep integration of the Internet of things (IoT) and robotics, constructing an efficient and reliable communication network has become a critical foundation for supporting collaborative operations of robots. Addressing the demands of low latency, high reliability, and lightweight design for communication networks in the dynamic and complex environments of robot systems, a broadcast optimization algorithm based on neighbor information speculation was proposed, named speculative reception-based broadcasting (SRB). By introducing neighbor management and a speculative reception mechanism, issues inherent in traditional Flooding algorithms, such as "broadcast storms", redundant forwarding, and excessive energy consumption, were effectively mitigated by the SRB algorithm. Periodically exchanged neighbor information among nodes was leveraged to infer whether their neighbors had already received the current data packet, thereby making intelligent forwarding decisions to avoid unnecessary retransmissions. The performance of the proposed algorithm was evaluated through simulations and a prototype system of robots. Experimental results demonstrate that SRB outperforms traditional Flooding algorithms in key metrics, including packet delivery ratio, end-to-end latency, and forwarding rate.关键词：IoT;robot;Mesh network;Flooding algorithm39|12|0更新时间：2026-10-10
- 摘要：The development of embodied intelligence and the robotic Internet of things (IoT) is characterized by increasing autonomy, collaboration, and multimodal fusion. The large-scale integration of heterogeneous embodied intelligent agents into IoT systems poses higher requirements for interoperability and security in network architectures. In this context, the introduction of a unified model context protocol (MCP) to standardize device capability descriptions and task interactions becomes essential. A comprehensive survey of MCP in the integration of embodied intelligence and robotic IoT was presented in this paper. The core concepts of MCP and its roles in multimodal perception, communication, computation, and control collaboration were introduced. The challenges in standardization, scalability, and cross-domain adaptation were summarized and future research directions were discussed. Studies indicate that MCP has the potential to significantly enhance resource utilization efficiency, task execution coordination, and system security in embodied intelligence and robotic IoT, providing a unified protocol foundation for the next-generation intelligent IoT systems.关键词：embodied intelligence;robotic IoT;MCP;semantic control plane;Context-to-QoS/Sec compiler16|15|0更新时间：2026-10-10
- 摘要：A complex terrain adaptive agricultural assistance robot based on PID control was developed, aiming to enhance agricultural production efficiency, reduce farmers' labor intensity, and achieve precise agricultural assistance in various complex terrains. The robot was controlled by the STM32F103C8T6 single-chip microcontroller, which received remote instructions from users through the HC-15 bluetooth module and controlled the stepper motor to achieve precise movement, and drove the water pump module to complete the spraying operation of the pesticide. The core operation principle was a closed-loop process of "wireless instruction reception → core processing → motor and actuator drive". The biggest innovation of this robot compared to existing agricultural robots lies in its ability to adapt to different terrains. It can operate stably on hillsides, sandy areas, or any rough terrain. Additionally, the robot can conduct health checks on crops, including the growth of leaves and the maturity of the crops. Besides, the robot has functions such as detecting soil composition, spraying pesticides, and irrigation.关键词：STM32F103C8T6 microcontroller;PID algorithm;Bluetooth communication;adaptation to complex terrain96|47|0更新时间：2026-10-10
Topic\: Embodied Intelligence and Robotic IoT
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Deep neural network-based dynamic mapping strategy for low earth orbit satellite channel estimation AI导读
摘要：In low-earth-orbit (LEO) satellite communication systems, addressing the poor performance of traditional channel estimation algorithms under dynamic delay variations and low signal-to-noise ratio (SNR) caused by high-speed satellite-terrestrial motion, a deep neural network (DNN)-based channel estimation algorithm combined with sub-block merging of channel responses under an orthogonal frequency-division multiplexing (OFDM) framework was proposed in this paper. The reliability and stability of data transmission in non-terrestrial network (NTN) were enhanced in this method. The algorithm divided the frequency-domain pilot channel response matrix into sub-blocks for averaging and employed a DNN to adaptively determine the optimal sub-block merging number under different channel conditions with varying delay and noise. The bit error rate (BER) performance of this algorithm was compared through simulations with the existing channel estimation methods under dynamically randomized delays. Simulation results demonstrate that the proposed algorithm achieves lower BER at identical SNR levels than traditional methods, exhibiting stronger robustness. Compared with the existing neural network-based channel estimation algorithms, it adapts better to larger delays and dynamic delay variations, showing superior generalization capability.关键词：LEO satellite;NTN;channel estimation;DNN;OFDM144|92|0更新时间：2026-10-10 - 摘要：Aiming at the existing satellite network key node analysis methods which are mainly oriented to the static network and employ a monolithic evaluation index and overlook the node function for the network specificity, a satellite IoT key node analysis method integrating the functional characteristics and topology structure was proposed. Firstly, the dynamic satellite network was divided into discrete time to get the topology snapshot sequence graph. Secondly, considering the geographic location and coverage time of subsatellite points, a spatio-temporal transaction traffic model was constructed to obtain the IoT transaction traffic of satellites. Next, the functional importance index of satellite nodes based on the entropy of the weight traffic and the topological importance index of satellite nodes based on the degree and betweenness of the network nodes were defined, and it was further proposed to use the gray comprehensive evaluation to fuse the indexes of these two aspects to obtain a comprehensive key node analysis method. Finally, the simulation results show that compared with the existing methods, the proposed method can effectively improve the accuracy of key node identification in satellite networks, thus providing a technical means for the analysis of key nodes in dynamic satellite networks.关键词：satellite Internet of things;key nodes;weight-flow entropy;comprehensive gray evaluation13|8|0更新时间：2026-10-10
- 摘要：Emerging low earth orbit (LEO) mega-constellations demonstrate significant potential in global communications. However, the vast network scale and frequent disruptions result in poor scalability and robustness of existing deep reinforcement learning (DRL)-based routing schemes. GraphSR, a robust and scalable routing framework based on graph reinforcement learning was proposed. First, GraphSR adopts a new topology model, called virtual traffic topology to shield dynamic topology disruptions. The physical topology changes caused by network failures are converted into the diversity of traffic demands to facilitate robust routing calculations. Second, to achieve routing scalability, GraphSR divides the constellation into multiple software-defined networking (SDN) control domains and designs a graph neural network (GNN)-based DRL algorithm to get rid of the input dependence on fixed topology. Lastly, by quickly evaluating critical failure scenarios and selecting some critical links that significantly impact network performance, GraphSR focuses on controlling the weights of critical links to reduce the action space dimension of DRL. Simulations on the Starlink mega-constellation topology show that compared with other baseline routing schemes, GraphSR improves path reliability by more than 7.04% under various link failure scenarios, while the end-to-end delay remains within an acceptable range.关键词：low earth orbit mega-constellation;resilient routing;software-defined networking;deep reinforcement learning;graph neural network97|71|0更新时间：2026-10-10
- 摘要：In space scenarios, the communications between the flying transmitters and receivers generally face two difficulties: a long-distance transmission and a high-dynamic movement. The long-distance transmission decreases the signal-to-noise ratio at the receiver and the high-speed movement imposes a dynamic Doppler-shift on the transmitter's carrier frequency, which is also referred to as the low SNR and high dynamic. To address the energy dispersion caused by low SNR and high dynamic Doppler-shift, a max-min-acquisition (MMA) scheme was proposed. The max-elements and min-elements provide us the cues of possible ranges and impossible ranges, respectively. The MMA scheme first selects some large elements (also called max-elements) and small elements (also called min-elements) to construct a narrow max-min-range, and then searches for the largest element in this range to obtain the acquisition result. Moreover, the theoretical analysis and simulation results have verified our MMA scheme's significant advantages over the existing schemes.关键词：space communications;Doppler-shift acquisition;low SNR;high-dynamic;energy dispersion12|8|0更新时间：2026-10-10
- 摘要：To address the challenges of user mobility, task dependency, and restricted aerial trajectory planning in maritime mobile edge computing, a user mobility-aware air-sea collaborative task offloading framework was proposed. A three-tier computing architecture comprising user equipment, aerial auxiliary nodes (AAN), and the edge layer was established, formulating a multi-objective optimization model that minimizes system costs (latency and energy consumption) while considering task dependencies, resource allocation constraints, and AAN 3D spatial safety requirements. A dynamic K-means-based user clustering mechanism that periodically updates mobile device (MD)-AAN associations according to real-time mobility patterns was proposed; a twin-delayed deep deterministic policy gradient based heterogeneous agent offloading (TD3-HAO) algorithm was designed,enabling joint optimization of dependent sub-task offloading, resource allocation, and 3D AAN trajectory planning through heterogeneous multi-agent coordination. Simulation results demonstrate superior performance over baseline methods,such as the local algorithm and the deep deterministic policy gradient (DDPG) algorithm,maintaining less than 3% deviation from optimal solutions when scaling to 25 MDs, with 16.94%~38.34% latency reduction. The proposed solution effectively resolves the resource underutilization caused by ignoring task dependencies and agent homogeneity in existing approaches, providing theoretical guidance for air-sea integrated edge computing systems.关键词：Maritime mobile edge computing;Aerial auxiliary nodes;dependent task offloading;3D trajectory planning;Heterogeneous multi-agent deep reinforcement learning87|72|0更新时间：2026-10-10
- 摘要：Wireless positioning based on received signal strength indication (RSSI) is widely used in IoT due to its advantages of low cost and low power consumption. However, traditional methods are difficult to take into account channel fading, multi-path effects and dynamic changes in the environment, which leads to degradation of positioning accuracy. To this end, a dynamic channel fading modeling and maximum likelihood estimation (MLE) localization method that combining the κ-µ distribution was proposed, and adaptive portrayal of channel fading characteristics was realized by analyzing the recently received RSSI data, thus improving the accuracy and stability. Experiments were carried out in the LoRa node network. The experimental results show that the κ-µ distribution can effectively fit the complex channel characteristics, significantly reduce the error fluctuations caused by environmental changes, and realize high-precision localization in complex environments. Compared with some classical localization methods, this method has an obvious advantage in accuracy, the average localization error is reduced to 34.82 m, which improves the accuracy by about 10% compared with the existing localization methods using static κ and µ parameters for channel modeling, and the localization time consumed is controlled in milliseconds.关键词：RSSI localization;κ-µ distribution;dynamic channel modeling;Maximum Likelihood Estimation39|133|0更新时间：2026-10-10
- 摘要：To address the high-frequency heterogeneous computing task requirements generated by IoT devices, an unmanned aerial vehicle (UAV) communication network and task offloading system based on diffusion reinforcement learning (DiffRL) was proposed. There were three main innovations. (1) A diffusion reinforcement learning offloading decision framework that reduced sampling steps from 50 to 15 steps through denoising diffusion implicit model (DDIM) sampling technology, accelerating the sampling process by 70% while maintaining 98% of decision quality. (2) An energy-oriented UAV trajectory planning algorithm that reduced total system energy consumption by 15.3%. (3)Achieving tight coupling between DiffRL decision-making and trajectory planning to solve multi-objective optimization problems in dynamic environments. Experiments show that this system achieves reductions in both energy consumption and task delay compared to traditional methods, reducing task delay by 30.2% and 9.2% respectively when compared to traditional deep Q-network (DQN) and deep deterministic policy gradient algorithms (DDPG) in task offloading decisions.关键词：UAV Communication Network;Diffusion Reinforcement Learning;mobile edge computing;Task Offloading Decision93|65|0更新时间：2026-10-10
- 摘要：To solve the problems of poor flexibility of ground-based edge servers and insufficient computing power of servers in high-density traffic corridors to provide reliable offloading services, a UAV-assisted edge computing system for the Internet of vehicles (IoV) was proposed, which uses multiple UAVs as mobile base stations to assist ground-based edge servers in providing offloading services for vehicles. The system models the joint optimization problem of computational offloading and resource allocation as a mixed-integer nonlinear programming problem to optimize the offloading decision of vehicles and the resource allocation of edge devices to minimize the total system cost. To solve the problem, an algorithm incorporating a genetic algorithm and a deep deterministic policy gradient was proposed, which has a better optimization finding capability in the telematics scenario by introducing a genetic algorithm to pre-train the learning rate of the Actor module and the Critic module. Simulation results show that CORA_GADDPG reduces the system cost by 18.2%, 18.8%, 30.7%, and 78.3% compared to four algorithms for computing offloading and resource allocation problems, respectively, when offloading delay and energy consumption are considered. Therefore, CORA_GADDPG has a superior reduction effect on system cost.关键词：Internet of vehicles;mobile edge computing;deep reinforcement learning;genetic algorithm17|12|0更新时间：2026-10-10
- 摘要：IoT-based environmental monitoring systems are crucial infrastructure for early forest fire warnings, playing a decisive role in preventing disaster spread. However, field-deployed terminals face limitations in communication bandwidth, computational power, and energy supply, hindering both real-time transmission of high-resolution images and on-device deployment of complex neural networks. An edge-cloud collaborative forest fire recognition method was proposed. Lightweight recognition and offloading models were deployed on terminals. Most images were processed locally at the edge; only images challenging for the lightweight model were offloaded via the IoT network to a cloud center for recognition. Recognition accuracy was balanced with the core IoT requirements of low latency, low bandwidth, and energy efficiency. Ghost Module and ShuffleNetv2 were integrated in the designed lightweight fire recognition model, enhanced with an efficient channel attention (ECA) module, significantly reducing computational and storage demands. To improve offloading decision effectiveness in dynamic IoT environments, a noisy double proximal policy optimization (NDPPO) algorithm was proposed to train the offloading model. Comparative experiments validate the effectiveness of this lightweight collaborative recognition approach for IoT-based forest fire monitoring.关键词：forest fire identification;lightweight model;collaborative identification;strategy optimization algorithm;IoT monitoring terminal130|159|0更新时间：2026-10-10
- 摘要：The complex environment of construction sites presents a huge challenge for detecting workers' safety helmets wearing . In order to accurately identify helmets wearing for workers in complex construction environments, and effectively reduce the incidence of safety accidents, a detection algorithm based on deformable convolution for the identification of safety helmets wearing by workers was proposed. Deformable convolutional modules were fused in the YOLOv8 backbone network part, alleviating the limitations of traditional standard convolution layers, which relied on fixed geometric structures for feature extraction. The model's ability was improved to analyze and identify complex construction environments by this enhancement. By employing image geometric transformations and pixel-level processing, the number of training samples was enriched by the algorithm, thereby enhancing the generalization capability of the detection model across various site scenarios. The algorithm was deployed on the Jetson TX2, and the target detection software was developed to facilitate on-site detection of the actual construction site environment. The results indicate that the model can effectively detect workers' safety helmets wearing in complex construction environments, achieving a recognition accuracy of 95.2% on a standard dataset. Furthermore, the model demonstrates robustness and can be applied across various construction sites.关键词：YOLOv8;construction site;unsafe behavior detection;deformable convolution;object recognition124|182|0更新时间：2026-10-10
- 摘要：Accurate segmentation of pulmonary nodules is a crucial prerequisite for early lung cancer screening and clinical diagnosis. However, pulmonary nodules in CT images exhibit challenges such as irregular shapes, blurred boundaries, and significant size variations (ranging from a few millimeters to tens of millimeters). Additionally, they are easily interfered with by surrounding tissues like blood vessels and pleura, which leads to limitations in traditional deep learning segmentation methods (e.g., U-Net, baseline V-Net), including large boundary localization errors, high missed diagnosis rates of small lesions, and poor segmentation consistency. Furthermore, the fixed setting of the region of interest (ROI) tends to cause redundancy of target information or loss of key details, further restricting segmentation accuracy.To address the above challenges, a two-stage pulmonary nodule segmentation method was proposed that combined an adaptive ROI algorithm with a multi-view 3D segmentation strategy. In the first stage, the V-Net architecture was employed to perform initial segmentation along the axial axis. The position and size of the ROI were dynamically adjusted by an innovative adaptive ROI (A-ROI) algorithm, maintaining the area ratio of the nodule to the ROI below a threshold RT to reduce interference from irrelevant tissues. In the second stage, patch-based analysis was conducted along the coronal and sagittal axes, and finally, the multi-plane prediction results were integrated by a consensus module to enhance segmentation stability. Experiments on the public LUNA16 and LNDb datasets show that this method achieves Dice coefficients of 92.6% and 92.3%, respectively, representing an improvement of 6.2% and 6.1% compared to the baseline V-Net, while the Hausdorff distance is reduced to 2.92±1.89 mm. Compared with the traditional U-Net, the segmentation accuracy is also significantly improved. Multi-plane collaborative analysis improves shape similarity by 29.8%. This method effectively addresses the core challenges in pulmonary nodule segmentation in CT images, providing reliable technical support for clinical accurate diagnosis of early lung cancer and efficacy evaluation.关键词：lung nodule;Adaptive ROI algorithm;Multi-view segmentation;V-Net architecture;segmentation stability83|76|0更新时间：2026-10-10
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