Underwater Acoustic Sensors Data Collection in the Robotic Vessels as-a-Service Project

Signori, Alberto; Campagnaro, Filippo; Zordan, Davide; Favaro, Federico; Zorzi, Michele

doi:10.1109/oceanse.2019.8867100

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…To enable basic communication and localization for μAUVs as part of our ongoing research [33,64,66], we implemented a reference communication stack, ranging from modulation to encoding. We discuss the stack subsequently and provide a detailed evaluation in Section 5 and Section 6.…”

Section: Acoustic Communication Stackmentioning

confidence: 99%

ahoi

Renner

Heitmann

Steinmetz

2020

ACM Trans. Sen. Netw.

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The recent development of small, cheap AUVs enables a plethora of underwater near-and inshore applications. Among these are monitoring of wind parks, detection of pollution sources, water-quality inspection, and the support of divers during disaster management. These tasks profit from online reporting, control, and AUV swarm interaction; yet they require underwater communication. Unfortunately, commercial devices are prohibitively expensive and typically closed-source, hampering their application in affordable products and research. Therefore, we developed the open-source 1 ahoi acoustic modem. It is (i) small enough to be carried by micro AUVs, (ii) consumes little enough energy to not diminish operation times of its host, (iii) comes at an attractive unit cost below $600, (iv) can reliably communicate at distances of 150 m and more, and (v) supports ranging without additional hardware. Due to its modular build, the modem can be customized and is suitable as research platform to analyze, e.g., MAC and routing protocols. We conducted extensive real-world studies and present results of communication range, packet reception rate, ranging accuracy, and efficient and reliable self-localization. Finally, we draw conclusions regarding acoustic communication, ranging, and localization with inexpensive and low-power devices that go beyond a particular device. Our study, hence, encompasses general insights, observations, and recommendations.

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Section: Acoustic Communication Stackmentioning

confidence: 99%

ahoi

Renner

Heitmann

Steinmetz

2020

ACM Trans. Sen. Netw.

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“…Instead, a long-term constraint is common to ensure that the cumulative amount of QoS metric is no larger than the threshold at the edge node over time, i.e., i . The long-term constraint can be expressed by (5), where g i t (x t ) is the constraint of i. Given the total resources of applications at an edge node, the choice of resource allocation of different applications will affect with each other.…”

Section: Problem Formulationmentioning

confidence: 99%

“…A S the increase of human marine activities, more underwater devices are deployed such as various kinds of sensors and autonomous underwater vehicles (AUVs) for different applications such as fish monitoring, detection and safeguard [1]- [3]. The detected information of underwater devices is usually collected by devices at the edge-layer of the marine Internet such as buoys, ships, marine observation platforms and cellphones [4], [5]. These devices feed collected data back to remote data centers via wireless Internet access layer devices such as base stations (BSs), access points (APs) and satellites [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Resource Management of Maritime Edge Nodes for Collected Data Feedback

2020

IEEE Access

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With the development of marine economy, more marine applications emerge leading to more deployed sensors and more generated data. However, current marine Internet only provides low transmission rate constrained by limited communication resources and bad transmission environment. How to efficiently use these resources and guarantee quality-of-service (QoS) of applications need to be addressed. This paper proposes an online optimization resource management algorithm to improve the communication resource efficiency with guaranteed QoSs for different applications. This algorithm needs neither of the resource cost function nor QoS constraint function at the resource scheduling node. Furthermore, the gradient information, which is usually needed in learning strategies, is not required either. Instead, this algorithm performs resource management of both computation and communication resources in each time slot only based on the observation of the last time slot. With slot-by-slot resource allocation, the communication cost can be minimized to be suitable for maritime scenarios. In the meantime, a long-term delay constraint can be satisfied. Results show that the proposed algorithm achieves the goal of reducing communication cost while guaranteeing the delay-constraint of different applications. Although more computation resources will improve its performance, this algorithm still can obtain the minimized cost given a low computation resource. INDEX TERMS Online resource management, data feedback, edge computing, maritime communications.

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“…This is the case of the Port of Hamburg, considered as the target scenario of this paper. Although, in Reference [13] the authors demonstrated, through a simulation study, that at least one off-the-shelf acoustic modem satisfies the performance requirements of the data-muling application in a river port environment, commercial modems are typically very expensive (the price for one unit can easily exceed e8000), thus, the deployment of a dense USN composed by such devices is typically limited to critical areas and military applications, where these costs are justified. The HF smartPORT acoustic modem (AHOI) [7] developed by the Technical University of Hamburg, instead, may become the enabling technology for dense USN to be employed in civil applications, as its overall cost, including the transducer, is about e600.…”

Section: Introductionmentioning

confidence: 99%

“…The reason is that, in order to upload the whole collected data to an external sink, the AUV would have to remain close to it for a significant amount of time due to the low bitrate of the modem (less than 200 bit/s). This task, instead, can be performed with a broadband acoustic modem (which will only be needed in the AUV, and not in the sensor nodes), capable of a higher transmission rate [13].…”

Section: Introductionmentioning

confidence: 99%

Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios

Signori

Campagnaro

Steinmetz

et al. 2019

JSAN

Self Cite

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The Robotic Vessels as-a-Service (RoboVaaS) project intends to exploit the most advanced communication and marine vehicle technologies to revolutionize shipping and near-shore operations, offering on-demand and cost-effective robotic-aided services. In particular, the RoboVaaS vision includes a ship hull inspection service, a quay walls inspection service, an antigrounding service, and an environmental and bathymetry data collection service. In this paper, we present a study of the underwater environmental data collection service, performed by a low-cost autonomous vehicle equipped with both a commercial modem and a very low-cost acoustic modem prototype, the smartPORT Acoustic Underwater Modem (AHOI). The vehicle mules the data from a network of low cost submerged acoustic sensor nodes to a surface sink. To this end, an underwater acoustic network composed by both static and moving nodes has been implemented and simulated with the DESERT Underwater Framework, where the performance of the AHOI modem has been mapped in the form of lookup tables. The performance of the AHOI modem has been measured near the Port of Hamburg, where the RoboVaaS concept will be demonstrated with a real field evaluation. The transmission with the commercial modem, instead, has been simulated with the Bellhop ray tracer thanks to the World Ocean Simulation System (WOSS), by considering both the bathymetry and the sound speed profile of the Port of Hamburg. The set up of the polling-based MAC protocol parameters, such as the maximum backoff time of the sensor nodes, appears to be crucial for the network performance, in particular for the low-cost low-rate modems. In this work, to tune the maximum backoff time during the data collection mission, an adaptive mechanism has been implemented. Specifically, the maximum backoff time is updated based on the network density. This adaptive mechanism results in an 8% improvement of the network throughput.

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Underwater Acoustic Sensors Data Collection in the Robotic Vessels as-a-Service Project

Cited by 10 publications

References 13 publications

ahoi

ahoi

Resource Management of Maritime Edge Nodes for Collected Data Feedback

Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios

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