Abstract
Autonomous Underwater Vehicles (AUVs) are seen as a safe and cost-effective platforms for performing a myriad of underwater missions. These vehicles are equipped with multiple sensors which, combined with their long endurance, can produce large amounts of data, especially when used for video capturing. These data need to be transferred to the surface to be processed and analyzed. When considering deep sea operations, where surfacing before the end of the mission may be unpractical, the communication is limited to low bitrate acoustic communications, which make unfeasible the timely transmission of large amounts of data unfeasible. The usage of AUVs as data mules is an alternative communications solution. Data mules can be used to establish a broadband data link by combining short-range, high bitrate communications (e.g., RF and wireless optical) with a Delay Tolerant Network approach. This paper presents an enhanced version of UDMSim, a novel simulation platform for data muling communications. UDMSim is built upon a new realistic AUV Motion and Localization (AML) simulator and Network Simulator 3 (ns-3). It can simulate the position of the data mules, including localization errors, realistic position control adjustments, the received signal, the realistic throughput adjustments, and connection losses due to the fast SNR change observed underwater. The enhanced version includes a more realistic AML simulator and the antenna radiation patterns to help evaluating the design and relative placement of underwater antennas. The results obtained using UDMSim show a good match with the experimental results achieved using an underwater testbed. UDMSim is made available to the community to support easy and faster evaluation of underwater data muling oriented communications solutions and to enable offline replication of real world experiments.

Abstract
The demand for cost-effective broadband wireless underwater communications has increased in the past few years, motivated by the video collection performed by Autonomous Underwater Vehicles (AUVs) in areas such as environmental monitoring and oil and gas industries. However, the current technological limitations make it hard to implement a viable broadband wireless communications system for transferring the large amounts of data collected. Existing underwater communications solutions, using wireless optical or Radio Frequency (RF), limit high definition wireless video transfer to distances up to tens of meters. In case of underwater acoustic communications, long ranges can be achieved, but the low bandwidth makes them unsuitable for video streaming, even for standard definition video.In this paper we propose a solution, named Underwater Adaptive and Reliable Video Streaming (UARVS), that offers a video streaming service built upon the GROW data muling approach. UARVS exploits the use of data mules - small and agile AUVs - that travel between two physical nodes, bringing the data from an underwater survey unit to a central station at the surface. To validate the solution, an experimental testbed was built using airtight PVC cylinders, on a freshwater tank. The experimental results obtained show that UARVS enables an adaptive and continuous flow of video, avoids butter underruns, and reacts to data mule losses and delays.

Abstract
In the last few decades, there has been a growing interest in exploring the sea. The activities of the so-called blue economy can go from applications such as offshore maritime wind farms to ocean environment monitoring, which are supported by sensed platforms such Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs) that require the use of reliable underwater communications. Currently, there is no suitable solution that is able to combine long-range and broadband underwater communications. The integration of different technologies, namely acoustics, RF, and optical on a multimodal approach, has been considered a suitable solution to overcome the limitations caused by the water propagation medium. Since missions at the ocean are usually expensive and demand large human and technological resources, it is important to have accurate simulation platforms for these multimodal underwater wireless networks. This paper presents the first version of a novel simulation framework - MultiUWSim (Beta) -, built upon ns-3, which integrates multiple communications technologies (RF, acoustics and optical). The current version of the simulation platform offers the possibility of simulating acoustic-based and radio-based physical wireless interfaces in a single node in a ns-3 simulation environment, enabling fully-customizable underwater network simulations.

Abstract
The growing demands of the Blue Economy are increasingly supported by sensing platforms, including as Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). Multimodal Underwater Wireless Networks (MUWNs), which may combine acoustic, radio-frequency, and optical wireless technologies, enhance underwater data transmission capabilities. Although Delay-Tolerant Networks (DTNs) address connectivity intermittency in such environments, not all data streams are delay-tolerant, and transmitting high-bandwidth DTN traffic over narrowband links can lead to significant inefficiencies. This paper presents QoS-MUWCom, a Quality of Service (QoS)-aware communication solution designed to manage both real-time and delay-tolerant traffic across dynamically selected multimodal interfaces. Experimental evaluations conducted in a freshwater tank demonstrate that QoS-MUWCom achieves near-zero packet loss for low-demand traffic even under link saturation, improves throughput for prioritized flows up to three times in mobility scenarios, and adapts to link availability and node mobility. The results confirm that QoS-MUWCom outperforms conventional multimodal strategies, contributing to more robust, resilient and efficient underwater communications.

Abstract
Underwater wireless communications face significant challenges due to propagation constraints, limiting the effectiveness of traditional radio and optical technologies. Long-range acoustic communications support distances up to a few kilometers, but suffer from low bandwidth, high error ratios, and multipath interference. Semantic communications, which focus on transmitting extracted semantic features rather than raw data, present a promising solution by significantly reducing the volume of data transmitted over the wireless link. This paper evaluates the resilience of SAGE, a semantic-oriented communications framework that combines semantic processing with Generative Artificial Intelligence (GenAI) to compress and transmit image data as textual descriptions over acoustic links. To assess robustness, we use a custom-tailored simulator that introduces character errors observed in underwater acoustic channels. Evaluation results show that SAGE can successfully reconstruct meaningful image content even under varying error conditions, highlighting its potential for robust and efficient underwater wireless communication in harsh environments.

Abstract
Optical Wireless Communications (OWC) has recently emerged as a viable alternative to radio-frequency technology, especially for the Internet of Things (IoT) domain. However, current simulation tools primarily focus on physical layer modelling, ignoring network-level issues and energy-constrained environments. This paper presents an energy-aware OWC module for ns-3 that addresses these limitations. The module includes specific PHY and MAC layers and integrates an energy model, a mobility model, and models of monochromatic transceivers and photodetectors, supporting both visible light and infrared (IR) communications. Verification against MATLAB simulations confirms the accuracy of our implementation. Additionally, mobility tests demonstrate that an energy-restricted end device transmitting via IR can maintain a stable connection with a gateway at distances up to 2.5 m, provided the SNR is above 10 dB. These results confirm the capabilities of our module and its potential to facilitate the development of energy-efficient OWC-based IoT systems.