Achieving reliable underwater acoustic communications is a challenging task due to the time-varying channels in the underwater acoustic environment. Scalable Video Coding (SVC) has been widely used in video transmissions. However, inappropriate SVC structures can lead to poorer received video quality than user requirements or resource waste, especially in underwater time-varying channels. In this work, an adaptive cross-layering solution is proposed and validated for video transmissions in underwater acoustic multicast networks, namely Receiver-Driven Adaptive Scalable Video Transmission (RD-ASVTuw). In RD-ASVTuw, the decision-making about transmission schemes takes place at the receiver through Machine Learning (ML). The transmitter collects over time the selected transmission scheme indexes and the user’s video quality requirements to transmit the SVC video adaptively. At-sea experiments were conducted to collect the required acoustic data. The data collected were then used in MATLAB simulations to validate RD-ASVTuw.

Autonomy, cooperation and data fusion can increase the performance of robotic networks in many underwater applications. In this paper, we describe a novel occupancy grid (OG) based perception layer, and its use for controlling a network of autonomous underwater vehicles (AUVs), sensorised with passive sonars. Data fusion between the robots' bearing-only measurements (typical of passive sonars) enables the estimate of target position. The developed OG framework exploits networking and the spatial diversity provided by the multi-robot system. The perception layer was integrated in the intelligent Cooperative Autonomous Decision Making Engine (iCADME) control architecture and validated for the first time in the Robotics Experimentation and Prototyping MUS (REPMUS) Exercise, held in Portugal in September 2021. Our robotic network participated in a technical demonstration, whose main objective was to localise a bottomed submarine which emitted a periodic help request acoustically during a simulated distress situation. We report results which are one of the first examples to demonstrate how cooperative robotics, supported by data fusion, can be effective in a passive sonar scenario. They also confirm the viability of adopting such solutions in real-world applications, characterised by poor communications and challenging environments. What was achieved at REPMUS21 clearly demonstrates how a network of cooperative robots can improve search & rescue operations of a submarine.

Security is a critical consideration in Underwater Acoustic Networks (UANs) due to the importance of the applications in which these types of networks are often employed, from military applications to marine natural disaster prevention. Furthermore, even simple Denial of Service (DoS) attacks such as jamming can be very effective in disrupting the communication, with significant negative consequences for these critical applications. While jamming has been widely studied in the context of terrestrial networks, the peculiarities of propagation in UANs, such as the low propagation speed, the multipath, and the high delay spread, need to be considered: the relative positions of the jammer, transmitter, and receiver can have a huge impact on the feasibility and impact of reactive jamming, opening the way for the exploitation of other jamming models. In this paper, we analyze the effectiveness of a reactive and a blind jammer through a game theoretical framework, comparing them for different geometries of the scenario. We assess the impact of the different jammers, employing different active and evasive strategies, where the first type of countermeasure implies the use of additional energy to protect the communication, while the second tries to avoid the jamming signals by randomizing the transmission pattern.

This poster describes the main features provided by the Sapienza University Networking framework for underwater Simulation Emulation and real-life Testing (SUNSET) [1]. Particularly, we focus on the innovations and improvements introduced in SUNSET v2 after several years of in field experimentation. All the results collected during the different sea trials (considering sea, fjord, lake and river scenarios) have shown how SUNSET represents a robust, reliable and flexible solutions for in field tests where a network of heterogeneous devices is considered, with different acoustic modems, sensing devices and underwater vehicles.

We demonstrate the use of a novel cross layer protocol for routing in Underwater Acoustic Sensor Networks (UASNs). Our protocol, named CARP [1] for Channel-Aware Routing Protocol, is able to dynamically adapt to channel dynamics and to the changes occurring in the network topology and link quality over time. CARP is implemented and tested using the Sapienza University Networking framework for underwater Simulation Emulation and real-life Testing (SUNSET) [2] in different network scenarios. Our demonstration shows that CARP is able to find the suitable routes and to deliver data to the sink node independently of prevailing packet error rates and of nodes/links addition and removal.

Underwater sensor networks have become an important area of research with many potential practical applications. Given impairments of optical and radio propagation, acoustic communication is used for underwater networking, which translates into variable and long propagation delays, low data rates, long interference ranges and significant fluctuations in terms of link quality over time. A complete characterization of the unique features of the acoustic channel introduces significant complexity both in analytical models and in simulators but is needed for correct characterization of underwater protocols performance. Our objective has been that of designing scalable analytical techniques which are able to derive optimum traffic scheduling and routing for underwater sensor networks while accurately capturing underwater channels features. Specifically the paper presents an analytical model for joint MAC and routing optimization which produces the optimum solution for small to medium scale underwater networks. Scalable, centralized heuristics are then designed, which combine approximate analytical models and scheduling heuristics, and are able to generate solutions close to the optimum. The overall result is a powerful tool to derive benchmark results (upper bounds) for underwater protocol performance and to understand the tradeoffs and performance limits of such systems.

In this paper we present ROME, a geographic routing protocol for wireless sensor networks (WSNs) with mobile nodes. ROME design is suited to deal with communication problems in WSN scenarios with high network dynamics, such as nodal addition, nodal removal and node mobility. In addition, it retains desirable properties of protocols for static WSNs such as using cross-layer techniques for performance optimization, dealing with asynchronous nodal duty cycles, and being able to deal with connectivity dead ends. We define the protocol in details and provide detailed simulation-based performance evaluation of ROME. In scenarios with static and mobile nodes together, our ns2-based experiments show that ROME performs remarkably well with respect to metrics such as packet delivery ratio, energy consumption and end-to-end packet latency.