This work concerns underwater networking with mobile assets, like Autonomous Underwater Vehicles (AUVs), for advanced monitoring and exploration of submerged environments. Particularly, we are interested in enabling an AUV to localize itself while moving underwater by acoustically polling beacon nodes statically deployed at well-known location. Our method only relies on a model of the AUV dynamics, on an on-board depth sensor and on long baseline ranging information. The AUV applies an Extended Kalman Filter to estimate its position, without needing any further local measurements but those of depths. We have evaluated the accuracy of the proposed method via experiments at sea in the shallow waters around the Italian island of Ponza, computing the average distance between the estimated locations of the AUV and its positions as measured by GPS along its trajectory (localization error). In deployments with up to four beacons, our simple method enables AUVs to swiftly self localize with errors never exceeding 3.62m (using only two beacons), 2.65m (three beacons) and 2.45m (four beacons).

The Internet of Underwater Things (IoUT) is a novel paradigm for the Blue Economy sector bringing the ability to sense, actuate, exchange information, through low cost technologies, typical of the terrestrial IoT and personal devices, to marine and ocean environments. It requires novel underwater networking concepts that are at the core of the described developed system. While IoUT has already been proposed by the authors for sake of environmental monitoring and marine exploration, in this demo we focus on how IoUT can bring novel opportunities in the field of underwater sport, leisure and tourism. In particular, the demo demonstrates FunDive, a novel wearable system for divers, developed in EC EASME ARCHEOSUb, that supports real-time monitoring of the divers positions and health conditions, at the same time allowing unprecedented enhanced visits of the sites. The system is composed of the following elements: a miniaturized underwater sensor node and an underwater tablet. The miniaturized underwater sensor node hosts an acoustics modem, the underwater networking and information compression logic, as well as a battery pack. The tablet is connected to the node and runs a dedicated APP. The system allows divers to communicate underwater, localize themselves/be localized, navigate to points of interest, and get displayed contents based on their position. A dedicated network protocol provides secure and reliable communication among divers and supports their localization. Our system has been developed by leveraging on the SUNSET Software Defined Communication Stack (S-SDCS) framework and its modular architecture and networking capabilities. In our demonstration we show, through an emulated underwater network environment, the flexibility and effectiveness of our system in allowing divers and surface operators to securely and reliably communicate using underwater tablets and acoustic networking. The navigation and acoustic tracking of divers will be also shown through a novel application interface.

This paper concerns the design and implementation of a novel system for diving operations based on an efficient underwater acoustic network. The proposed system is composed of both static underwater nodes and divers that are equipped with acoustic modems and underwater tablets. The SUNSET Software Defined Communication Stack (S-SDCS) has been used to provide multi-hop networking capabilities. Novel image compression and transmission modules have been designed and implemented in S-SDCS. In addition, the localization module has been enhanced to increase the safety of the divers whose positions can be locally and remotely monitored. These modules interact with an innovative mobile application running on the underwater tablet thus enabling divers communication and remote position estimation and monitoring. In addition, divers can acquire and share pictures with other operators in real-time. The user interface of the application has been designed according to diver needs and feedback thus making it perfectly suitable for several diving scenarios. The system has been tested in several at-sea campaigns considering different application scenarios. The achieved results confirm that the proposed system is suitable for several diving experiences, enabling novel application scenarios while increasing divers safety.

We demonstrate the feasibility of long lasting underwater networking by proposing the smart exploitation of the energy harvesting capabilities of underwater sensor nodes. We define a data routing framework that allows senders to select the best forwarding relay taking into account both residual energy and foreseeable harvestable energy. Our forwarding method, named HyDRO, for Harvesting-aware Data ROuting, is also configured to consider channel conditions and route-wide residual energy, performing network wide optimization via local information sharing. The performance of our protocol is evaluated via simulations in scenarios modeled to include realistic underwater settings as well as energy harvesting based on recorded traces. HyDRO is compared to state-of-the-art forwarding protocols for underwater networks. Our results show that jointly considering residual and predicted energy availability is key to achieve lower energy consumption and latency, while obtaining much higher packet delivery ratio.

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

This paper concerns the smart exploitation of multimodal communication capabilities of underwater nodes to enable reliable and swift underwater networking. To contrast adverse and highly varying channel conditions we define a smart framework enabling nodes to acquire knowledge on the quality of the communication to neighboring nodes over time. Following a model-based reinforcement learning approach, our framework allows senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. We name the resulting forwarding method MARLIN, for MultimodAl Reinforcement Learning-based RoutINg. Applications can choose whether to seek reliable routes to the destination, or whether faster packet delivery is more desirable. We evaluate the performance of MARLIN in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN is compared to state-of-the-art forwarding protocols, including a channel-aware solution, a machine learning-based solution and to a flooding protocol extended to use multiple modems. Our results show that a smartly learned selection of relay and modem is key to obtain a packet delivery ratio that is twice as much that of other protocols, while maintaining low latencies and energy consumption.

This paper considers underwater wireless sensor networks (UWSNs) for submarine surveillance and monitoring. Nodes produce data with an associated value, decaying in time. An autonomous underwater vehicle (AUV) is sent to retrieve information from the nodes, through optical communication, and periodically emerges to deliver the collected data to a sink, located on the surface or onshore. Our objective is to determine a collection path for the AUV so that the Value of Information (VoI) of the data delivered to the sink is maximized. To this purpose, we first define an Integer Linear Programming (ILP) model for path planning that considers realistic data communication rates, distances, and surfacing constraints. We then define the first heuristic for path finding that is adaptive to the occurrence of new events, relying only on acoustic communication for exchanging short control messages. Our Greedy and Adaptive AUV Path-finding (GAAP) heuristic drives the AUV to collect packets from nodes to maximize the VoI of the delivered data. We compare the VoI of data obtained by running the optimum solution derived by the ILP model to that obtained from running GAAP over UWSNs with realistic and desirable size. In our experiments GAAP consistently delivers more than 80% of the theoretical maximum VoI determined by the ILP model.