Uncrewed Aerial Vehicles (UAVs) play a vital role in public safety, especially in monitoring wildfires, where early detection reduces environmental impact. In UAV-Assisted Wildfire Monitoring (UAWM) systems, jointly optimizing the data collection schedule and UAV velocity is essential to minimize the average Age of Information (AoI) for sensory data. Deep Reinforcement Learning (DRL) has been used for this optimization, but its limitations – including low sampling efficiency, discrepancies between simulation and real-world conditions, and complex training – make it unsuitable for time-critical applications such as wildfire monitoring. Recent advances in Large Language Models (LLMs) provide a promising alternative. With strong reasoning and generalization capabilities, LLMs can adapt to new tasks through In-Context Learning (ICL), which enables task adaptation using natural language prompts and example-based guidance without retraining. This paper proposes a novel online Flight Resource Allocation scheme based on LLM-Enabled In-Context Learning (FRSICL) to jointly optimize the data collection schedule and UAV velocity along the trajectory in real time, thereby asymptotically minimizing the average AoI across all ground sensors. Unlike DRL, FRSICL generates data collection schedules and velocities using natural language task descriptions and feedback from the environment, enabling dynamic decision-making without extensive retraining. Simulation results confirm the effectiveness of FRSICL compared to state-of-the-art baselines, namely Proximal Policy Optimization, Block Coordinate Descent, and Nearest Neighbor.

Delivering downlink ACKs to Class A LoRaWAN devices via LEO satellite constellations requires predicting end-to-end propagation delays of 10–500 ms to hit a 1-second receive window, a timing challenge absent from terrestrial schedulers. We present DORSAL, the first Mixed-Integer Linear Programming (MILP) scheduler for downlink delivery in direct-to-satellite LoRaWAN networks. It introduces theDownlink Arrival Receive-window Timing (DART)as the central constraint to jointly optimize gateway selection and packet injection timing across ISL-capable and bent-pipe architectures. Evaluation on a Walker 5×5 polar constellation (25 satellites, 650 km) and a commercial ground station network yields five results: (i) DART infeasibility blocks 73% of bent-pipe deliveries regardless of scheduler quality: DART-unaware approaches collapse from 93% nominal to only 20% real delivery; (ii) ISL routing raises %ACK by +67.4 pp over bent-pipe (91.6% vs. 24.2% atnGS=3 ground stations, ISM band) by eliminating the three-way simultaneous-visibility constraint, with 73% of ACKs delivered via ≥ 2-hop paths; (iii) ISL %ACK is nearly flat acrossnGS∈ {3, 6, 12} ground stations (91.6%–92.7%), showing that a minimal threestation polar network captures the full scheduling benefit of an ISL-equipped constellation; (iv) ISL hardware speed has negligible impact up to 300 ms/hop: %ACK is flat from 0 to 300 ms, then degrades in two discrete steps (−5.3 pp at 500 ms, −2 pp at 700 ms) as successive hop-count tiers lose causal injection feasibility; and (v) the MILP solves to proven optimality within seconds for up to 103devices, confirming that the sparse conflict-graph structure keeps the problem tractable at realistic IoT subscriber counts.

Unmanned Aerial Vehicles enable a wide range of applications, including search and rescue, environmental monitoring, and disaster response. These aerial platforms can form dynamic flying ad hoc networks to support both sensing and data communication. In particular, UAVs can establish a resilient communication backbone that adapts to varying propagation conditions and fluctuating traffic demands. However, maintaining reliable performance in such highly dynamic and unpredictable environments remains a critical challenge. This work investigates online link quality estimation by autonomous agents, with a focus on real-time detection of link model changes (e.g., due to mobility or interference) through model drift. Building on this, we propose and evaluate adaptive formation control strategies that adjust UAV placement to optimize the network’s Packet Delivery Ratio. Simulation results demonstrate that the proposed optimal placement strategy significantly outperforms baseline approaches for line-based UAV networks.

Accurate channel modelling is essential for enhancing overwater communications in dynamic propagation environments, but it is hindered by variable and noisy measurement data that cause uncertainty in large-scale propagation models. This work examines data uncertainty in overwater shore-to-shore WiFi communications and explores strategies to reduce it. We show that local principal curves allow representing empirical data with lower error than conventional methods. Uncertainty and sensitivity analysis provided insights into regions (water vs. mud) and periods (rising tide) with high variability in model predictions, and the most influential parameters (antenna heights). Parameter calibration effectively mitigates the impact of input parameter uncertainty.

This paper analyzes the impact of specular signal reflections on the accuracy of Received Signal Strength (RSS)-based localization for Internet of Things (IoT) devices using the weighted least squares (WLS) regression algorithm within a two-ray propagation channel. Simulations with realistic WiFi/BLE settings, considering distance, antenna heights, and carrier frequency, reveal that localization accuracy is significantly influenced by deep fades caused by surface reflections, which depend on the geometry of anchor-target positions. A pseudo-outlier elimination approach based on feasible localization distances effectively mitigates this issue, significantly reducing localization error. These findings offer practical insights into the performance of WLS-based IoT localization in two-ray environments and lay the groundwork for GPS-free or GPS-denied localization systems in challenging scenarios, such as overwater environments, where two-ray propagation is predominant.

Wireless communications increasingly enable ubiquitous connectivity for a large number of nodes, applications and scenarios. One of the less explored scenarios is aquatic communications, specially when considering near-shore and short-range communications. Overwater communications are impaired by a number of distinguishing dynamic factors, such as tides, waves or node mobility, that lead to a widely fluctuating and unpredictable channel. In this work, we empirically characterize near-shore, overwater channels at 2.4 GHz under realistic conditions, including tidal variations, and relatively short TX-RX separations. To this end, we conducted experiments in a coastal estuarine region and on a harbor to characterize Shore-to-Shore (S2S) and Shore-to-Vessel (S2V) communication channels, respectively, and to identify major factors impairing communication in such scenarios. The empirical results show that constructive/destructive interference patterns, varying reflecting surface, and node mobility (i.e. travel direction and particular maneuvers) have a relevant and noticeable impact on the received signal strength. Thus, a set of parameters should be simultaneously considered for improving the performance of communication systems supporting S2S and S2V links, namely tidal variations, reflection surface changes, antenna height, TX-RX alignment and TX-RX separation. The results useful provide insights into realistic S2S and S2V link design and operation.

This article presents a novel centrality-driven gateway designation framework for the improved real-time performance of low-power wireless sensor networks (WSNs) at system design time. We target time-synchronized channel hopping (TSCH) WSNs with centralized network management and multiple gateways with the objective of enhancing traffic schedulability by design . To this aim, we propose a novel network centrality metric termed minimal-overlap centrality that characterizes the overall number of path overlaps between all the active flows in the network when a given node is selected as gateway. The metric is used as a gateway designation criterion to elect as a gateway the node leading to the minimal number of overlaps. The method is then extended to multiple gateways with the aid of the unsupervised learning method of spectral clustering . Concretely, after a given number of clusters are identified, we use the new metric at each cluster to designate as cluster gateway the node with the least overall number of overlaps. Extensive simulations with random topologies under centralized earliest-deadline-first (EDF) scheduling and shortest-path routing suggest our approach is dominant over traditional centrality metrics from social network analysis, namely, eigenvector , closeness , betweenness , and degree . Notably, our approach reduces by up to 40% the worst-case end-to-end deadline misses achieved by classical centrality-driven gateway designation methods.

The future of goods transportation will rely on increased efficiency, lower risks, and diminished delays through the use of vehicle platoons that benefit from vehicular connectivity using V2X (Vehicle to Everything) applications. This article describes a system that offers the aforementioned vehicular connectivity to platoons, based on AI-enhanced 5G for resource allocation in wireless platoon intra-communications under three scenarios (latency emergency braking, platoon wireless resource management in tunnels, V2X communications interference in a traffic congestion). Demos are described for each of the scenarios, targeting different layers, starting by the PHY (physical) layer where propagation models are implemented, then a simulation-based MAC (medium access control) layer that allows the allocation of resources to the connected User Equipments (UE) and finally a management and orchestration layer capable of monitoring and managing the radio network, offering features such as network slicing management using O-RAN (Open Radio Access Network) standards.

This paper investigates the outage probability of a vehicle-to-vehicle (V2V) multiple antenna communication link with respect to the density of antennas. The objective is to study the trade-off between complexity (number of antennas) and link performance considering explicit ground reflections. The antennas are assumed to be located at regularly distributed positions across the surface of contiguous vehicles. Under the assumption of symbol repetition across Tx antennas, we also focus on well-known mechanisms such as maximum-ratio and equal-gain combining (MRC and EGC, respectively). The objective is to minimize the outage probability of the V2V link with deterministic and stochastic channel components (i.e., Rice-distributed), where the line-of-sight (LOS) is affected by ground reflections (as an extension of the two-ray model). This scenario is more realistic for V2V applications due to the proximity of the antennas to the ground. The outage probability is averaged over a range of inter-vehicle distances with respect to the free-space loss solution. The results show that performance is improved even for a relatively small number of antennas, and that a point is reached beyond which improvement becomes differential. This suggests an optimum trade-off between outage probability and complexity can be reached over a range of inter-vehicle distances.

Intersections are critical elements of urban traffic management and are identified as bottlenecks prone to traffic congestion and accidents. Intelligent intersection management plays a significant role in improving traffic efficiency and safety determining, among other metrics, the waiting time that vehicles incur when crossing an intersection. This work presents a preliminary analysis of the worst-case response time of intersection management protocols that handle mixed traffic with autonomous and human-driven vehicles. We deduce theoretical bounds for such time considered as the interval between the injection of a vehicle in the road system and its departure from the intersection, considering different intersection management protocols for mixed traffic, namely the Synchronous Intersection Management Protocol (SIMP) and several configurations of the conventional Round-Robin (RR) policy. Simulation results validate the analytical bounds partially. Ongoing work addresses the queue dynamics and its reliable detection by traffic simulators.

This paper proposes network centrality as a criterion to designate a gateway or sink in real-time wireless sensor-actuator networks (WSAN). The objective is to improve network schedulability by design, particularly, by means of a centrality-driven gateway designation. To this purpose, four classical centrality metrics taken from social network analysis are explored, namely, (i) degree centrality, (ii) closeness centrality, (iii) betweenness centrality, and (iv) eigenvector centrality. We assume time-synchronized channel-hopping (TSCH) WSANs under centralized shortest-path routing and earliest-deadline-first (EDF) scheduling. Simulation results under varying configurations show that assigning the gateway role based on network centrality is, in general, an effective and promising approach to improve real-time performance in this type of networks. To the best of our knowledge, this work pioneers the use of a centrality-driven gateway designation as a mean to improve schedulability in WSANs.

In the design of shore-to-shore and shore-to-vessel wireless links, the impact of the ray reflected on the surface is often neglected. It adds that, in some coastal areas, the geometry of the reflection changes over time due to tides. When choosing an antenna height for an inshore node, often the largest possible height is used, but this approach can lead to signal degradation. The two-ray model is the most fundamental path loss model to account for the contribution of the reflected ray. We carried out experimental measurements at the shores of a freshwater body to verify that the two-ray model can predict the major trends of the path loss experienced by a 2.4 GHz over-water wireless link. We focus on short-to-medium distance links, with antennas installed a few meters above surface. We observed considerable consistency between measurements and model estimates, leading us to conclude that the two-ray model may bring benefits when applied to the network design of over-water links affected by tidal variations, which is our end-goal.

The rising adoption of wireless technologies in the In- dustrial Internet of Things has stressed the need for traffic schedulability validation at system design-time to support safety and time critical streams (e.g., process control and emergency response). In this context, the demand-based schedulability tests have recently been proposed in the literature. This work revisits two well-established techniques borrowed from the multi-processor scheduling theory, namely the demand-bound-function (DBF) and the forced-forward-demand-bound-function (FFDBF), and evaluates their performances when adapted to the field of wireless sensor-actuator networks. Simulation experiments when varying network configurations confirm the equal or better accuracy of FFDBF over DBF to estimate both network demand and schedulability. In future work, we aim at building upon these promising results in order to design novel admission control and adaptation strategies that improve network schedulability under varying workload conditions.