Petrika Gjanci

Maximizing the Value of Sensed Information in Underwater Wireless Sensor Networks via an Autonomous Underwater Vehicle.

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.

Clock synchronization and ranging estimation for control and cooperation of multiple UUVs

This paper presents the initial implementation of an acoustic synchronization and ranging system to enable the control and cooperation of multiple Unmanned Underwater Vehicles (UUVs). Our solution is based on acoustic clock synchronization and one-way ranging. It requires minimum overhead while providing accurate and quick estimation of the relative distances among underwater nodes. The use of one-way ranging allows to scale up to large teams of UUVs and reduces the energy consumption of localization techniques. Our solution has been implemented in SUNSET, leveraging on the accurate timing information and scheduled transmissions provided by SeaModem acoustic modems. Chip Scale Atomic Clocks have been integrated in the SeaModem to overcome the typical drift of real-time clocks thus enabling accurate one-way ranging estimation during long term missions. The performance of the proposed system have been extensively evaluated in two at-sea campaigns considering different testing scenarios. We have shown that our scheme is able to maintain high ranging accuracy over time without requiring the high overhead and energy consumption of two way ranging techniques. We have also shown that the proposed scheme for acoustic synchronization is very effective in synchronizing real-time and atomic clocks of underwater nodes, whenever needed. Our results confirm that the proposed solution for synchronization and one-way ranging allows to enable the control of multiple UUVs keeping at the bay the overhead in the network and the time needed to estimate relative distances.

Finding MARLIN: Exploiting multi-modal communications for reliable and low-latency underwater networking

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.

Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks

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.

MARLIN-Q: Multi-modal communications for reliable and low-latency underwater data delivery

Abstract This paper explores the smart exploitation of multi-modal communication capabilities of underwater nodes to enable reliable and swift underwater networking. Following a model-based reinforcement learning approach, we define a framework allowing senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. The choice is also driven by the quality of the communication to neighboring nodes over time, thus allowing nodes to adapt to the highly adverse and swiftly varying conditions of the underwater channel. The resulting forwarding method allows applications to choose among different classes of soft Quality of Service (QoS), favoring, for instance, reliable routes to the destination, or seeking faster packet delivery. We name our forwarding method MARLIN-Q, for Multi-modAl Reinforcement Learning-based RoutINg with soft QoS. We evaluate the performance of MARLIN-Q in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN-Q is compared to state-of-the-art forwarding protocols, including a channel-aware solution, and a machine learning-based solution. 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 latency and energy consumption.

Harnessing HyDRO: Harvesting-aware Data ROuting for Underwater Wireless Sensor Networks

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.

EVERUN: Enabling Power Consumption Monitoring in Underwater Networking Platforms

The energy restricted nature of underwater sensor networks directly affects the expected lifetime of autonomous deployments and limits the capabilities for long term underwater monitoring. Towards the goal of developing energy-efficient protocols and algorithms, researchers and equipment vendors require in-depth understanding of the power consumption characteristics of underwater hardware when deployed in-field. In this work, we introduce the EVERUN power monitoring framework, consisting of hardware and software components that were integrated with real equipment of the SUNRISE testbed facilities. Through the execution of a wide set of experiments under realistic conditions, we highlighted the limitations of model-based energy evaluation tools and characterized the energy efficiency performance of key protocols and mechanisms. The accuracy of the collected power data, along with the interesting derived findings, verified the applicability of our approach in evaluating the energy efficiency performance of proposed solutions.

OptoCOMM and SUNSET to enable large data offloading in Underwater Wireless Sensor Networks

In this paper we present the initial implementation of an integrated optical and acoustic system that can enable large data transfer between mobile and static nodes in Underwater Wireless Sensor Networks (UWSNs). The proposed system is based on the OptoCOMM optical modem and on the SUNSET Software Defined Communication Stack (S-SDCS) framework. The OptoCOMM modem allows to overcome the limits of maximum data rate and bandwidth imposed by the use of acoustic communication by providing a data rate of 10Mbps. SUNSET SDCS instead has been used to provide networking and fragmentation capabilities to efficiently offload large data in UWSNs. The performance of the proposed approach has been evaluated through in lab experiments where large files with arbitrary sizes have been optically transferred. The results achieved show that our system is able to transfer up to 1.5 GBytes of data in short time.