Aug 2026· Italian National Conference on Sensors· Vol 26, pp. 5180· 0 citations· 28 references
Medicine
TL;DR
A vision-assisted unmanned aerial vehicle (UAV) relay triggering framework that uses roadside multi-camera images to predict the future link state of a target vehicle and triggers a UAV decode-and-forward (DF) relay before the direct roadside-unit (RSU)–vehicle link becomes unreliable.
Abstract
Millimeter-wave (mmWave) vehicular-to-everything (V2X) links are highly vulnerable to sudden blockages in dense urban traffic. Since terrestrial roadside links can degrade rapidly, and alternative ground paths are often limited, maintaining reliable service with only ground networking resources remains challenging. To enhance link reliability by exploiting aerial relay resources in air–ground integrated networks, this paper proposes a vision-assisted unmanned aerial vehicle (UAV) relay triggering framework. The framework uses roadside multi-camera images to predict the future link state of a target vehicle and triggers a UAV decode-and-forward (DF) relay before the direct roadside-unit (RSU)–vehicle link becomes unreliable. To enable target-specific prediction, a template-guided image-matching module is developed to localize the target vehicle in multi-view images. The matched features are fused and temporally modeled to predict future LoS, NLoS, and Absent states, with the predicted NLoS probability further used to determine the UAV activation decision through a probability-based triggering policy. Simulation results on a 3D ray-tracing urban V2X dataset show that the proposed dual-view predictor achieves about 99% validation accuracy, compared with about 87% for the single-view baseline. The proposed relay triggering scheme reduces the outage probability from 15.08% for RSU-only transmission and 4.49% for reactive relaying to 0.76%, and improves the 5th-percentile rate from 11.72 Mbps to 22.49 Mbps over reactive relaying.
Simulation results demonstrate that the proposed adaptive scheme demonstrates notable improvements over classical loss-based and delay-based baselines in reducing queuing delays at UAV relay nodes, enhances the transmission efficiency of multi-hop terminals, and effectively maintains end-to-end goodput stability in high-latency environments.
L. Zong, Yun Cheng, Yi Yao· Italian National Conference...· 0 citations
Simulation results demonstrate that RESCUE-ISAC improves energy efficiency, link reliability, sensing performance, mobility robustness, and runtime–performance trade-off compared with heuristic, lightweight, and optimization-based benchmark schemes.
R. Khalil, Saba Mahmood, T. Jan et al.· IEEE Open Journal of Vehicul...· 0 citations
Urban edge-camera and IoT sensing networks increasingly support time-critical monitoring, where rapid target detection is essential in emergency scenarios. In these environments, Unmanned Aerial Vehicles (UAVs) serve as mobile data ferries to disseminate high-value recognition payloads to distributed ground nodes. However, large payloads are severely constrained by short air-to-ground (A2G) contact windows and packet-level impairments such as path loss, shadowing, smallscale fading, and stochastic packet errors (PER). This paper addresses the problem of minimizing mission-level time-to-detection under realistic packet-level PHY constraints. We propose a coordinated framework that partitions recognition payloads into compact fragments. A UAV continuously broadcasts these fragments, rather than the full payload, along a coverage-aware trajectory, while ground nodes cooperatively exchange and fuse missing evidence through localized, confidencetriggered collaboration. Unlike monolithic UAV-only delivery and infrastructure-bound cooperative inference, our design explicitly couples fragment broadcasting with edge cooperation under packet-level feasibility. Simulation results show that the proposed framework significantly reduces time-to-detection compared to monolithic transmission and UAV-only baselines, particularly under stringent A2G conditions with short contact windows and high packet error rates. The results highlight the value of jointly optimizing fine-grained dissemination, edge cooperation, and PHY-aware communication in UAV-assisted urban sensing systems.
An D. Vu, Khanh-Van Nguyen· IEEE International Conferenc...· 0 citations
Post-disaster environments involve severe blockage, strong scattering, deep shadowing, and heavy-tailed fading, making reliable emergency communication and life-sign sensing difficult. This study develops a STAR-RIS-assisted UAV emergency rescue network and models the control-center-to-UAV link using power-law path loss and Rician fading, while the UAV-to-user and UAV-to-target links are characterized by the Fisher–Snedecor F distribution to capture debris obstruction and multipath effects. To jointly optimize communication throughput and life-detection accuracy, an improved GD-TD3 algorithm is proposed by integrating a GRU-based temporal memory module, Softmax value smoothing, and Gaussian action exploration. The method jointly controls UAV deployment, RIS phase shifts, and communication-sensing power allocation under time-varying obstruction. Simulation results show that activating only 50% activation ratio preserves approximately 81% of the sensing performance achieved by the Full-array STAR-RIS benchmark. In dynamic high-obstruction scenarios, GD-TD3 achieves about 72% communication coverage, a 68% sensing detection rate, and an 87% system detection rate while maintaining 3.4–3.8 Gbps throughput and 0.5594 Mbps/J energy efficiency. These results demonstrate robust integrated communication and sensing performance while maintaining competitive energy efficiency under severe dynamic obstruction.
Intelligent vehicular networks require reliable high-capacity links in dynamic non-stationary environments. Conventional geometry-based stochastic models (GBSMs) struggle to accurately capture the complex propagation effects in aerial-assisted links, particularly those affected by unmanned aerial vehicle (UAV) attitude fluctuations. In this paper, we propose a three-dimensional (3D) artificial intelligence (AI)-enhanced channel model for aerial double reconfigurable intelligent surface (RIS)-assisted vehicle-to-vehicle (V2V) communications. The proposed model integrates UAV mobility, random attitude fluctuations, and double-RIS configurations. We derive the complex channel impulse response (CIR), spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and frequency correlation function (FCF). A lightweight multilayer perceptron (MLP) neural network is embedded to compensate for RIS perturbation errors induced by UAV dynamics. Furthermore, the twin delayed deep deterministic policy gradient (TD3) method is employed to jointly optimize UAV trajectories and RIS parameters. Simulation results demonstrate that the proposed model effectively captures spatial-temporal-frequency non-stationary characteristics, achieving superior channel correlation and communication capacity compared to conventional single-RIS models and other deep reinforcement learning-based algorithms.
Ying Zhao, Zhi-Min Chen, Ming Li et al.· IEEE Transactions on Cogniti...· 0 citations
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