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.

Internet of Things (IoT) technology enables enhanced connectivity and information sharing among various devices and platforms. In the context of vehicular crowds ensing, this connectivity has opened up new way to collect environmental data via Vehicle-based Visual Crowdsensing. However, the heterogeneity of data sources and the presence of vehicle outliers pose challenges of ensuring the reliability and accuracy of the machine learning (ML) models. We propose GeoFed, a geography-aware federated learning (FL) approach for vehicular visual crowdsensing. Here, geographically similar vehicular fog nodes (VFNs) collaborate to train a cluster model unlike the traditional FL approaches where vehicles participate to train a model. To further improve GeoFed's performance, we employ the deep Q-Network (DQN) algorithm to intelligently determine the participation of vehicles in the FL process. Through extensive experiments on our own collected real-world dataset, we find that our proposed GeoFed not only outperforms the state-of-art FedAvg with higher F1 score (1.18 x) and mAP (1.14 x), but also achieves a faster convergence rate with less loss (80%).

The emergence of generative pre-trained transform-ers (GPTs) will thoroughly change the application of sixth generation mobile communications (6G) networks. Therefore, it is necessary to design new network architectures to support ubiq-uitous deployment and real-time applications of GPTs. Aiming to integrate GPTs and the 6G network, this paper investigates the typical application scenarios of 6G+GPTs and summarizes the requirements of network key performance indicators (KPIs). Then, to address the complex and dynamically changing commu-nication environment, a self-adaptive 6G network architecture is proposed based on autonomous learning and self-optimization. Additionally, a novel mechanism based on attack samples is studied to improve the security of applying GPTs in 6G networks. Finally, we demonstrate that the proposed network architecture and security mechanism can satisfy the KPIs and improve robustness effectively. Overall, this paper provides a theoretical basis for the support of native GPTs with a novel 6G network architecture.

With the advancements in sensing technologies, maritime sensing has become indispensable in various domains, including logistics, weather forecasting, and marine ranching. However, transmitting large volumes of sensing data faces many challenges in the maritime environment. First, the transmissions purely depend on satellite links often costly and suffer from long propagation latency. On the other hand, traditional unicast transmission results in data duplication, wasting valuable marine communication resources. With the increasing density of sensing devices, the communication distance between maritime sensors has become closer, enabling the deployment of maritime opportunistic networks consisting of device-to-device links. Rather than using unicast transmission over satellite links, employing multicast with opportunistic routing enables simultaneous data transmission to multiple destinations and saves communication resources. Even though the multicast method can avoid redundancy, conducting multicast without considering the maritime characteristics (i.e., the dynamics and the distribution of sensors) may lead to inefficient data delivery. Through real-world experiments, we observe that devices located on the edges of the network have a relatively low receiving rate compared with internal ones and tend to be the bottleneck of the overall multicast progress. Based on this observation, we propose BOOM, a bottleneck-aware opportunistic multicast strategy aiming at reducing multicast latency, taking into account the influence of the bottleneck node and broadcasting rate. Prominently, within maritime scenarios challenged by extreme conditions, such as storms, typhoons, and tsunamis, BOOM’s emphasis encompasses the adaptability of multicast strategies, which necessitates dynamic adjustments in response to equipment failures and shifts in network topology. Through mathematical analysis, we prove the formation of opportunistic multicast is an NP-hard problem and further design a heuristic algorithm based on the convex-hull method to reduce the computational cost in strategy generation. We compare BOOM with four other algorithms using real-world maritime vessel trajectories in various scenarios. The simulation result illustrates that the BOOM achieves a significant reduction in transmission latency, which reduces 36% when sensors are sparsely located in water areas, and the reduction could reach up to 59% when sensors are more dense. Furthermore, in extreme environmental testing conditions, BOOM continues to outperform other algorithms in terms of completion time, with performance improvements of up to 39% and 49% in sparse and dense topology environments, respectively.

With the advancement of assisted and autonomous driving technologies, vehicles are being outfitted with an ever-increasing number of sensors. Among these, visible light sensors, or dash-cameras, produce visual data rich in information. Analyzing this visual data through crowdsensing allows for low-cost and timely perception of urban road conditions, such as identifying dangerous driving behaviors and locating parking spaces. However, uploading such massive visual data to the cloud for centralized processing can lead to significant bandwidth challenges and also raise privacy concerns among vehicle owners. Federated learning (FL), in which vehicles serve as both data generators and computing nodes, presents a promising solution to address these challenges. Nevertheless, urban roads are complex and vehicles in different locations encounter completely different scenes, resulting in non-independently and identically distributed (non-i.i.d.) characteristics. Additionally, the diversity in dash-camera and onboard computation resources may lead to differences in the performance of locally trained models. Indiscriminate aggregating of local models from all vehicles can potentially degrade the global model’s performance. To overcome these challenges, we introduce FedVisual, a model aggregation approach for FL in vehicular visual crowdsensing. FedVisual leverages deep Q-network (DQN) to select appropriate local models, considering the heterogeneities in visual data contents and vehicles’ specifications. By leveraging the historical training experience, an effective model selection strategy can be obtained without complex mathematical modeling. Through the extensive simulations of our self-collected driving videos, FedVisual reduces model aggregation latency by up to 3.8% while improving the model’s performance by up to 3.2% compared to reference works.

Prompted by the significant advancements in image processing technologies and their diverse range of applications, remote sensing satellites are poised for rapid expansion. Nonetheless, offloading the vast amount of remote sensing satellite images to the ground gateway station is inefficient due to the exorbitant costs induced by satellite links, while the limited resources of individual satellites hinder local task processing. With the advancement of the network function virtualization (NFV) technology, a new paradigm for service function chain (SFC) has emerged, which can significantly improve the flexibility and resource utilization of network services and alleviate resource conflicts by dividing large services into smaller ones organized in the form of SFCs. As mega-constellations (e.g., Starlink) developed, the number of low earth orbit (LEO) satellites is increasing. By dividing services into small sub-services and organizing them into SFCs throughout the LEO network, services that cannot be completed by a single satellite can be accomplished through multi-satellite cooperation. However, the quality of the remote sensing service is positively correlated with its latency, and the rapidly changing topology of LEO networks also adds complexity to the SFC placement. Hence, how to select appropriate satellites to place the SFC and modulate service levels, in order to obtain better remote sensing results within an acceptable latency, remains a question. To address these issues, this paper proposes the FloodSFCP, an SFC placement method that aims to increase service quality and decrease latency through offline training and online optimization via deep reinforcement learning, taking into account the variation in LEO network topology. By introducing NoisyNet, Dueling, and N-step learning, we improve the model’s generalization ability and reduce the state space, thus enhancing convergence speed while reducing decision and training time. Experimental results demonstrate that FloodSFCP significantly improves service quality while reducing total decision costs.

With the emergence of compute-intensive and latency-sensitive vehicular applications, vehicular fog computing (VFC) has been proposed for catering to the thriving demands for computing and communication resources close to vehicles. In VFC scenarios where multiple tasks need to be offloaded simultaneously, the data, often coming from multiple sources, must be transmitted at a high data-rate in parallel. An antenna array system, a set of multiple connected antennas which work together as a single antenna, could achieve a significantly higher data-rate than a traditional single antenna. However, data-rate of the antenna array system may decrease due to the presence of interference. On the other hand, an antenna array system consumes more energy than a single antenna, which is antagonistic to vehicles powered by limited electricity. To address these challenges, we propose EAAV, a multi-task allocation strategy that enables multiple tasks to be offloaded concurrently in antenna array empowered VFC. EAAV aims at reducing the transmission power consumption while maintaining a high transmission data-rate, taking into account the mobility of vehicles and communication interference. We transform the multi-task allocation problem into a convex solvable one and evaluate the effectiveness of EAAV based on real-world vehicle trajectories. Compared with the existing task allocation strategy, EAAV improves the average transmission data-rate by up to 8.2% and reduces the average power consumption by up to 38.3%.

How to automatically predict people’s gaze has attracted attention in the field of computer vision and machine learning. Previous studies on this topic set many constraints, such as restricted scenarios and strict and complex inputs. To mitigate these constraints to predict the gaze of people in more general scenarios, we propose a three-pathway network (TPNet) to estimate gaze via the joint modeling of multiple cues. Specifically, we first design a human-centric relationship inference (HCRI) module to learn the object-level relationship between the target person and the surrounding persons/objects in a scene. To the best of our knowledge, this is the first time that the object-level relationship is introduced into the gaze estimation task. Then, we construct a novel deep network with three pathways to fuse multiple cues, including scene saliency, object-level relationships and head information, to predict the gaze target. In addition, to extract the multilevel features during network training, we build and embed a micropyramid module in TPNet. The performance of TPNet is evaluated on two gaze estimation datasets: GazeFollow and DLGaze. A large number of quantitative and qualitative experimental results verify that TPNet can obtain robust results and significantly outperform the existing state-of-the-art gaze estimation methods. The code of TPNet will be released later.

Vehicle-based visual crowdsourcing is an emerging paradigm where the visual data collected from dash cameras are analyzed with the aim of measuring phenomena of common interest. To ensure the efficiency in vehicle-based visual crowdsourcing, there remain at least two technical challenges. First, to maximize the Quality of Information (QoI), which measures the amount of information extracted from the collected data, the context of data collection (e.g., camera position and orientation) must be taken into account in the process of task allocation. Second, intensive data collection from dense measurement points is key to ensure timely and accurate sensing of the targets of interest, whereas there exists a trade-off between the amount and rate of data collection and the computing and communication resources required to fulfill the latency constraint. To solve these challenges, we propose gathering and processing the collected data at the edge of the network and design a context-aware task allocation scheme, called FlexSensing, to jointly optimize the QoI and processing latency. We target application scenarios where commercial vehicles are turned into vehicular fog nodes (VFNs). These nodes gather and process the visual data collected from other vehicles within their coverage areas. The key idea of FlexSensing is to determine the rate of data collection for each sensing vehicle in the targeted area and to assign processing tasks to VFNs based on the estimated QoI and the workload of the VFNs. Given the excessive computational complexity of task allocation in this context, we formulate task allocation as a Markov decision process and apply a deep Q-network (DQN) to learn the optimized task allocation strategies for increasing the QoI of collected data while reducing the processing latency. To evaluate the effectiveness of FlexSensing, we simulate the mobility of different vehicles involved in the scenario at different times of the day based on real-world traffic data collected from the city of Helsinki and select a real-time object detection application for a case study. As compared with the existing task allocation strategies, the DQN-based task allocation strategies reduce the average processing latency by up to 51% and increase the QoI of the collected data by up to 34%.

Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.

| openaire: EC/H2020/815191/EU//PriMO-5G

Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%.

The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks (DCNs). For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3-D ring reflection spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60-GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.

Energy consumption of data center has become an important challenge due to high electric cost and carbon dioxide emissions. Previous work has mainly focused on saving energy cost of servers, though the energy consumption of data center networks (DCNs), consisting of networking equipments like switches, also takes a significant part of the overall energy consumption. In this paper, we propose ProCons, an energy saving mechanism that dynamically consolidates traffic flows onto a small set of networking equipments in order to shut down idle ones for energy saving. Different from previous works that assume the traffic demands to be stable, ProCons takes into account the variance of traffic demand over time, and predicts future demand based on historical statistics. The traffic flows are then scheduled based on the predicted future demands and the capacity of each link. We evaluate ProCons with real life traces collected from data centers using a flow-level simulator. Our experimental results show that using ProCons, 40% of energy savings for DCNs can be gained while maintaining the good performance of flow transmission.