Spurred by the limited availability of quantum resources, known as qubits, in recent quantum computers, quantum federated learning (QFL) is drawing attention. Due to its ability to fully utilize distributed qubits, QFL is suitable for developing quantum algorithms. QFL achieves local quantum gradients using the parameter-shift rule (P-S rule) and aggregates them, effectively coping with the limited number of qubits in each quantum computer. However, realizing QFL remains challenging due to the characteristics of the P-S rule, which requires two forward passes to compute the quantum gradients in each local quantum computer. These challenges become even more severe when the aggregation of the local quantum gradients occurs under heterogeneous channel conditions and data distributions. Motivated by this, this paper proposes the joint P-S rule, which eliminates the aggregation process in QFL and instead directly achieves the global quantum gradients. Furthermore, this paper proposes joint efficient quantum federated learning (Joint EQFL) that leverages successive interference cancellation and divergence-based clustering for achieving stability under heterogeneous channel conditions and robustness to heterogeneous data distributions. This paper analyzes the convergence and corroborates the superiority of Joint EQFL.

Recent advancements in unmanned aerial vehicle (UAV) technology have opened new avenues for dynamic data collection in challenging environments, such as sports fields during fast-paced sports action. For the purposes of monitoring sport events for dangerous injuries, we envision a coordinated UAV fleet designed to capture high-quality, multi-view video footage of collision events in real-time. The extracted video data is crucial for analyzing athletes’ motions and investigating the probability of sports-related traumatic brain injuries (TBI) during impacts. This research implemented a UAV fleet system on the NetLogo platform, utilizing custom collision observation algorithms to compare against traditional TV-coverage strategies. Our system supports decentralised data capture and autonomous processing, providing resilience in the rapidly evolving dynamics of sports collisions. The collaboration algorithm integrates both shared and local data to generate multi-step analyses aimed at determining the efficacy of custom methods in enhancing the accuracy of TBI prediction models. Missions are simulated in real-time within a two-dimensional model, focusing on the strategic capture of collision events that could lead to TBI, while considering operational constraints such as rapid UAV maneuvering and optimal positioning. Preliminary results from the NetLogo simulations suggest that custom collision record methods offer superior performance over standard TV-coverage strategies by enabling more precise and timely data capture. This comparative analysis highlights the advantages of tailored algorithmic approaches in critical sports safety applications.

This paper investigates the sum spectral efficiency (SE) and total energy efficiency (EE) of the active reconfigurable intelligent surface (aRIS)-assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) over temporal and spatially correlated channels. Multiple aRISs are deployed between numerous access points (APs) and mobile users to enhance the signals. The active reflective elements (REs) amplify and vary the signal phase. The continuously evolving channel creates a situation, where the channel differs during training and data transmission. We characterize the joint impact of multi-user interference (MUI), pilot contamination (PC), channel aging (CA), and active RIS noise amplification (RNA). The desired signal is enhanced as the reflected signal amplitude and REs per aRIS increase. However, the PC, MUI, CA, and RNA also grow– constraining the SE. Also, the network power consumption increases. An alternating optimization framework based on the weighted minimum mean square error and fractional programming is proposed to optimize the transmit power and reflection coefficients (RCs) with the objective of maximizing the sum SE. It is demonstrated that the number of APs and REs can be reduced by deploying aRISs. The proposed power and RC optimization algorithms considerably improves the sum SE. The trade-off analysis between the total EE and sum SE shows that the envelope of the operating region of the CF mMIMO is expanded by deploying multiple aRISs.

Sensor networks in IoT play a crucial role in harsh and complex environments, such as pipeline monitoring and irregular terrains. Traditional geographic OR (GOR) schemes that rely on Euclidean distance for packet forwarding are often unsuitable for irregular network topologies, frequently resulting in incorrect forwarding directions and routing holes. To address the limitations, this paper proposes a Manifold Learning-based Geographic Opportunistic Routing (MLGOR) scheme for 3D strip sensor networks. Inspired by the Isomap algorithm, MLGOR first maps the irregular 3D network onto a regular 2D strip network, enabling the construction of effective forwarding candidates using Euclidean distance and thus mitigating topological issues. A novel forwarding node selection scheme is then introduced that combines both node and network-based metrics. This hybrid approach calculates forwarding priorities and leverages connectivity to minimize duplicate transmissions, supplemented by a time-based coordination mechanism. The applicability of MLGOR is discussed, and simulation results demonstrate its energy efficiency in low-reliability and irregular 3D network environments, thereby extending network lifetime.

Spectrum deception has found broad utility across multiple domains, including electronic warfare, tactical counter-measures, and adversarial sensing suppression. However, generating complex time–frequency signatures relies on sophisticated signal processing pipelines, which poses significant challenges for UAV and other IoT platforms with severely constrained onboard computational resources. Moreover, the limited onboard capability further restricts rapid signal synthesis and adaptation, failing to meet the strict rapid-response requirements of the battlefield. To address this dilemma, we propose the Fast-Tactical Signal Deception Framework (FT-SDF), a specialized generative architecture optimized for real-time signal synthesis. We formulate a novel spectro-temporal diffusion dynamics mechanism that innovatively incorporates additional spectral blurring and reverse process variance, jointly optimizing noise prediction and variance, which is necessary to preserve fine-grained spectral structures and key time–frequency signatures across different modulation schemes. Notably, to ensure strict adherence to communication protocols, we introduce a lightweight spectrum-context encoder that employs a dual-domain embedding strategy for physics-aware conditioning. Furthermore, to enable rapid inference, we develop a variance-aware acceleration mechanism that exploits learned spectral uncertainty to guide a dynamic warm-start schedule, thereby drastically compressing the sampling trajectory. Extensive evaluations on a systematically reconstructed RadioML benchmark demonstrate that FT-SDF outperforms state-of-the-art baselines. Specifically, it achieves a 59.3% reduction in sampling iterations (more than 2-fold inference speedup), while maintaining both high statistical fidelity (FID < 15) and industrial-grade precision (EVM ≤ 14%), demonstrating the feasibility of rapid, controllable generative AI in complex electromagnetic environments.

Six low-cost air quality sensors were installed on the front roof area of three buses to enable real-time monitoring of air quality across the city of Valladolid (Spain) over a 7-month period, capturing variability in meteorological conditions and emission sources. Prior to deployment, the sensors were placed at a reference station in Barcelona (Spain) for validation and calibration. Measurements deviating by more than 30% from reference values were discarded, and correlation coefficients (R²) were calculated. After the monitoring campaign, the calibration procedure was repeated. The results (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.85) suggest that bus-mounted sensors can effectively support real-time detection of urban air quality changes and contribute to detailed air quality mapping. Integrating data from reference air quality monitoring networks (AQMNs) with low-cost sensor (LCS) systems can strengthen evidence-based policymaking and help refine regulatory frameworks aimed at reducing urban air pollution.

Accurate and efficient traffic flow prediction is crucial for the increasingly prevalent autonomous driving, enabling more advanced intelligent transportation systems. For this purpose, we propose a novel model termed Meta Guided-Graph Lightweight TimesNet (MGGLTN) to accurately capture the spatio-temporal correlations within traffic flow data, thereby providing precise traffic flow predictions for Connected Vehicles (CVs). Our spatio-temporal information learning architecture features an encoder-decoder backbone, wherein both the encoder and decoder comprise graph convolutional networks coupled with lightweight Times modules. More importantly, we propose a meta guided-graph library, aimed at providing memory queries for time-varying traffic patterns based on real-world physical spatial information. It efficiently guides the initialization direction of meta guided-graph prototypes, thereby accelerating the convergence speed of model training. Moreover, we introduce depthwise separable convolutions to replace the computationally intensive multi-kernel convolutions in the Times modules, thus significantly reducing computational costs and model parameters while maintaining accuracy. We perform extensive experiments on three public benchmark datasets (i.e., METR-LA, PEMS-BAY, and EXPY-TKY) and conduct comprehensive performance evaluations compared to both baseline models and state-of-the-art models. The findings demonstrate the superior performance of our model across all three datasets of varying spatial scales, highlighting the potential of this model to provide precise traffic guidance for CVs.

In the context of the Internet of Things (IoT) and Connected and Automated Vehicles (CAVs), eco-driving has become a key strategy for promoting sustainable and intelligent transportation by leveraging vehicle-to-everything (V2X) communication to optimize driving behavior, reduce energy consumption, and minimize environmental impact. However, achieving a dynamic balance among energy efficiency, traffic flow, safety, and driving comfort remains challenging in complex and congested urban environments such as roundabouts. This paper proposes an IoT-enabled multi-objective eco-driving framework, termed DDPG-KAN, which integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with Kolmogorov–Arnold Networks (KANs) to enhance nonlinear feature representation and policy learning capability under mixed-traffic conditions. A multi-objective reward function is designed to jointly optimize safety, energy efficiency, traffic smoothness, and driving comfort, while an Action Inspector ensures collision avoidance and a Model Predictive Controller (MPC) guarantees smooth and stable control execution. Leveraging IoT-based connectivity, the framework allows cooperative perception and adaptive decision-making among CAVs in real time. Simulation results show that the DDPG-KAN approach reduces energy consumption by 31.87%, improves traffic efficiency by 10.93%, decreases carbon emissions by 31.86%, lowers collision warnings by 38.34%, and enhances driving comfort by 53.46% compared to rule-based methods. These findings demonstrate the potential of DDPG-KAN as an effective IoT-driven solution for achieving low-carbon, safe, and comfortable mobility in intelligent connected vehicle systems, contributing to the realization of sustainable and green transportation networks.

With the rapid evolution of smart manufacturing, achieving high-precision and real-time tool condition monitoring has become increasingly crucial for maintaining production efficiency and system reliability. To address this challenge, this work proposes a tool wear fault diagnosis framework that integrates deep learning with collaborative edge computing. At the core of this framework, a physically motivated and hierarchically coupled spatial-temporal modeling network, namely Improved Multiscale Networks (IMSNet), is developed. Unlike conventional CNN-LSTM-attention architectures, IMSNet is designed according to the intrinsic degradation characteristics of tool wear vibration signals. Specifically, a structured multiscale convolutional module is employed to extract spatial representations that capture heterogeneous frequency-axis interactions, while an LSTM network simultaneously models the temporal evolution embedded in raw vibration signals to characterize cumulative degradation dynamics. The complementary spatial and temporal features are then fused through a multi-head attention mechanism for adaptive reliability-aware feature reweighting, enabling robust representation learning under non-stationary machining conditions. To meet stringent real-time requirements in Industrial IoT systems, we further design a cloud-edge-device collaboration (CEDC) framework for adaptive task offloading and low-latency inference. The framework decomposes diagnostic workloads across device, edge, and cloud layers and dynamically coordinates computation within scheduling windows, thereby improving system responsiveness. Experiments demonstrate that IMSNet achieves 97.81% accuracy on the self-collected industrial dataset and 98.93% on the public PHM benchmark, and showing stable and generalizable performance under varying operating conditions. Meanwhile, the CEDC framework reduces task off-loading latency by up to 17.4% compared with the advanced offloading strategies, while maintaining real-time responsiveness under varying workload conditions. To facilitate reproducibility and further research, the source code is publicly available at: https://github.com/lidongyang1/tool-fault-diagnosis-IIoT.