Low-power wide-area networks are extending beyond the conventional terrestrial domain. Coastal zones, rivers, wetlands, among others, are nowadays common deployment settings for Internet-of-Things nodes where communication technologies such as LoRa are becoming popular. In this article, we investigate large-scale fading dynamics of LoRa line-of-sight links deployed over an estuary with characteristic intertidal zones, considering both shore-to-shore and shore-to-vessel communications. We propose a novel methodology for path loss prediction which captures i) spatial, ii) temporal and iii) physical features of the RF signal interaction with the environmental dynamics, integrating those features into the two-ray propagation model. To this purpose, we resort to precise hydrodynamic modeling of the estuary, including the specific terrain profile ( bathymetry ) at the reflection point. These aspects are key to accounting for a reflecting surface of varying altitude and permittivity as a function of the tide. Experimental measurements using LoRa devices operating in the 868 MHz band show major trends on the received signal power in agreement with the methodology’s predictions.

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.

In urban road transportation, intersections are traffic bottlenecks with increased waiting delays and associated adverse effects. A recently proposed intelligent intersection management (IIM) approach, the Synchronous Intersection Management Protocol (SIMP), synchronizes the vehicles access to simple single-lane isolated intersections, outperforming competing approaches in various performance metrics. In this paper, we apply SIMP to multi-lane intersections, increasing significantly the applicability of the protocol while dealing with the additional complexity emerging from the multiple crossing conflicts. Using the SUMO simulator, we compare the performance of SIMP with two conventional (Round-Robin - RR and Trivial Traffic Light Control - TTLC) and two IIM approaches (Intelligent Traffic Light Control - ITLC and Q-learning based Traffic Light Control - QTLC) under continuous and interrupted upstream traffic flows scenarios in urban settings. The results using a maximum speed of 30 km/h confirm the superiority of SIMP, improving traffic throughput (~ 14.4%) and reducing travel delays (~ 64.4%) and associated fuel consumption (~ 25.5%) when compared to the best of the other approaches.

Modern observation systems can be composed by heterogeneous entities (e.g., buoys, USVs, UAVs, on-shore sensors, etc.) that rely on dependable communications for coordination and data collection, often provided by over-water radio-frequency (RF) links. In tide-affected water bodies, RF links at a fixed height from the shore can experience the so-called tidal fading, a cyclic time-varying tide-induced interference. To mitigate it, the classical space-diversity reception technique (i.e., the use of two or more receiver antennas positioned at different heights) is often applied, commonly combined with the consideration of having one of the antennas at the largest possible height. Yet, this approach does not always ensure the best performance. In this work, we focus on static over-water links of short-to-medium-range distances that use antennas installed at a few meters above surface. We leverage the geometrical basis of the two-ray propagation model to investigate the optimal single-antenna height design that minimizes overall average path losses over a given tidal range. We then extend this analysis to incorporate a second receiver antenna and identify its optimal antenna height. Analytical results show that our method considerably outperforms the more classical approach, thus enabling superior (average) link capacities.

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 article proposes a novel online flight resource allocation scheme based on LLM-enabled ICL (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 (PPO), block coordinate descent (BCD), and nearest neighbor (NN).