ASUNA: A Topology Data Set for Underwater Network Emulation

Casari, Paolo; Campagnaro, Filippo; Dubrovinskaya, Elizaveta; Francescon, Roberto; Dagan, Amir; Dahan, Shlomo; Zorzi, Michele; Diamant, Roee

doi:10.1109/joe.2020.2968104

Cited by 16 publications

(9 citation statements)

References 60 publications

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“…Here, we employed a portion of the publicly available ASUNA dataset [30], which consists of 11,000 sample groups, including 6561 positive and 4439 negative samples. The partial training samples are listed in Table 1.…”

Section: The Framework Of Svmmentioning

confidence: 99%

ASVMR: Adaptive Support-Vector-Machine-Based Routing Protocol in the Underwater Acoustic Sensor Network for Smart Ocean

Zhang,

Chen,

Xie

2023

JMSE

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The underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, the deployment of UASN poses significant challenges due to the demanding environmental conditions and the considerable expenses associated with its implementation. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of packet delivery delay, routing void, and energy consumption. In this paper, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN to minimize end-to-end delay and prolong the network lifetime. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real time by considering four types of node state information. Moreover, the ASVMR protocol establishes a “routing vector” spanning from the current node to the sink node and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments.

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Section: The Framework Of Svmmentioning

confidence: 99%

ASVMR: Adaptive Support-Vector-Machine-Based Routing Protocol in the Underwater Acoustic Sensor Network for Smart Ocean

Zhang,

Chen,

Xie

2023

JMSE

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“…To demonstrate the performance of our approach in realistic scenarios, we examined playback results from a sea experiment performed in May 2009 in the Haifa harbor, Israel, for an underwater acoustic network formed by four boats creating five different sparse network topologies, as illustrated in Figure 8 . As described in [ 37 ], the playback is performed by sending the SOS packet and its relays through the channels recorded in the experiment, and by considering the recorded delays in the nodes and in the communication links. Using this type of data-augmented approach, we take into account both real delays in the channel and distortions caused by the ambient noise and the multipath channel.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Graph Localization Approach for Underwater Sensor Networks to Assist a Diver in Distress

Diamant

Francescon

2021

Sensors

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In this paper, we focus on the problem of locating a scuba diver in distress using a sensor network. Without GPS reception, submerged divers in distress will transmit SOS messages using underwater acoustic communication. The study goal is to enable the quick and reliable location of a diver in distress by his fellow scuba divers. To this purpose, we propose a distributed scheme that relies on the propagation delay information of these acoustic SOS messages in the scuba divers’ network to yield a range and bearing evaluation to the diver in distress by any neighboring diver. We formalize the task as a non-convex, multi-objective graph localization constraint optimization problem. The solution finds the best configuration of the nodes’ graph under constraints in the form of upper and lower bounds derived from the inter-connections between the graph nodes/divers. Considering the need to rapidly propagate the SOS information, we flood the network with the SOS packet, while also using rateless coding to leverage information from colliding packets, and to utilize time instances when collisions occur for propagation delay evaluation. Numerical results show a localization accuracy on the order of a few meters, which contributes to quickly locating the diver in distress. Similar results were demonstrated in a controlled experiment in a water tank, and by playback data from a sea experiment for five network topologies.

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“…Given the large number of sea experiments performed by scientists in the last 15 years [6][7][8][9][10], a wide dataset of time-varying links has been collected, and some measurements are publicly available. Data-driven models have gradually been used to predict the trend of channel performance; for example, in [11] the authors, considering features for the model different environmental characteristics, build a logistic regression network whose Packet Success Rate (PSR) estimates are quite accurate if restricted to the short-term variability of only one of the acoustic link features used to build the regression network.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Modeling Acoustic Channel Variability in Underwater Network Simulators from Real Field Experiment Data

2022

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The underwater acoustic channel is remarkably dependent on the considered scenario and the environmental conditions. In fact, channel impairments differ significantly in shallow water with respect to deep water, and the presence of external factors such as snapping shrimps, bubbles, rain, or ships passing nearby, changes of temperature, and wind strength can change drastically the link quality in different seasons and even during the same day. Legacy mathematical models that consider these factors exist, but are either not very accurate, like the Urick model, or very computationally demanding, like the Bellhop ray tracer. Deterministic models based on lookup tables (LUTs) of sea trial measurements are widely used by the research community to simulate the acoustic channel in order to verify the functionalities of a network in certain water conditions before the actual deployment. These LUTs can characterize the link quality by observing, for instance, the average packet error rate or even a time varying packet error rate computed within a certain time window. While this procedure characterizes well the acoustic channel, the obtained simulation results are limited to a single channel realization, making it hard to fully evaluate the acoustic network in different conditions. In this paper, we discuss the development of a statistical channel model based on the analysis of real field experiment data, and compare its performance with the other channel models available in the DESERT Underwater network simulator.

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ASUNA: A Topology Data Set for Underwater Network Emulation

Cited by 16 publications

References 60 publications

ASVMR: Adaptive Support-Vector-Machine-Based Routing Protocol in the Underwater Acoustic Sensor Network for Smart Ocean

ASVMR: Adaptive Support-Vector-Machine-Based Routing Protocol in the Underwater Acoustic Sensor Network for Smart Ocean

A Graph Localization Approach for Underwater Sensor Networks to Assist a Diver in Distress

Modeling Acoustic Channel Variability in Underwater Network Simulators from Real Field Experiment Data

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