Underwater Acoustic Modems

Sendra, Sandra; Lloret, Jaime; Jiménez, Jose M.; Parra, Lorena

doi:10.1109/jsen.2015.2434890

Cited by 240 publications

(114 citation statements)

References 44 publications

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“…• This work does not have experimental results to report to support the simulation results presented earlier. Nevertheless, the reader interested in experimental validation of the proposed impersonation detection framework is referred to the works [33]- [35]. Specifically, [33] summarizes the state-of-the-art in commercial underwater acoustic modems, while the details pertinent to the (commercially available) arrays of hydrophones could be found in [34], [35] which report experimental results.…”

Section: Discussionmentioning

confidence: 99%

Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

et al. 2018

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This work considers a line-of-sight underwater acoustic sensor network (UWASN) consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared underwater acoustic (UWA) reporting channel in a time-division multiple-access (TDMA) fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this work first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-features based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum likelihood hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a maximum-likelihood (ML) distance estimator as well as the corresponding Cramer-Rao bound (CRB). We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with colored noise and frequency-dependent pathloss.

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Section: Discussionmentioning

confidence: 99%

Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

et al. 2018

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“…It should be noted that each of the nodes in the network and the mobile node (AUV) is equipped with acoustic modes WHOI (Micro-Modem) for making acoustic voice communication [17], [18]. For this reason, the modem features should be considered in the simulation.…”

Section: Evaluating the Performance Of The Proposed Protocolmentioning

confidence: 99%

“…As it is clear, by the increase in simulation time, the ratio of signal to the noise is decreased up to approximately 40dB. In Fig.9., the results are compared with the BGAF algorithm [18], which is a clustering algorithm in the underwater networks. Comparison of the charts shows that the proposed algorithm has a better function than the BGAF algorithm.…”

Section: Evaluating the Performance Of The Proposed Protocolmentioning

confidence: 99%

An Energy-Aware Data-Gathering Protocol Based on Clustering using AUV in Underwater Sensor Networks

MotahariNasab¹,

Bohlooli²,

Moghim³

2016

IJCNIS

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Abstract-Underwater Wireless Sensor Networks (UWSNs) consist of certain number of sensors and vehicles interacting with each other to collect data. In recent years, the use of Autonomous Underwater Vehicle (AUV) has improved the data delivery ratio and maximized the energy efficiency in UWSNs. Clustering is one of the effective techniques in energy management which increases the lifetime of these networks. One of the most important parameters in creating optimized clusters is the choice of appropriate cluster head (CH), which not only increases the lifetime of the network and the received data in the sink, but also reduces energy consumption. Clustering of networks was primary done via distributed methods in previous researches. It spends too much energy and also involves too many nodes in the clustering process and fades their main functionality, which is gathering data in sensor networks. It also causes more damping of the network. However, in the proposed protocol, instead of having them distributed by the network and the nodes, the stages of clustering and selecting the appropriate CH is the task of the AUV (Autonomous Underwater Vehicle). Since all the necessary measures to cluster in the network will be carried out by the AUV by this method, many control overheads in the process of clustering the network will be removed and energy consumption caused by nodes reduces significantly. With this method, the network scalability will also be manageable and under control. For simulating and implementing our method we mainly used the OPNET software. The results show that energy consumption of nodes in the proposed algorithm has been significantly improved compared to previous results.

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“…The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.Sensors 2020, 20, 1377 2 of 23 water-based transport applications [6,7], oil, and natural gas production applications [8,9], and developing fishing-centric industries [10,11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13].…”

mentioning

confidence: 99%

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

Rathore

Sangwan

Mazumdar

et al. 2020

Sensors

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Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.Sensors 2020, 20, 1377 2 of 23 water-based transport applications [6,7], oil, and natural gas production applications [8,9], and developing fishing-centric industries [10,11]. In underwater networking, tiny sensor nodes are deployed underwater, as well as on the upper surface layer for monitoring the specific underwater area [12]. These underwater nodes communicate with the surface nodes, acting as access points or cluster heads for reaching the sink node of the network, which accumulates the information and communicates with the cloud-enabled computing resources [13]. Underwater networking is significantly challenging compared to traditional wireless networking due to the dynamic self-mobility of the medium of communication and constraints in signal propagation in the underwater environment [14][15][16]. In this constrained networking environment, the underwater network deployment-oriented challenges further complicate scientific investigations towards the development of an energy-centric green underwater network for various application domains [17][18][19].Towards enabling green underwater networking, several service and geolocation-centric techniques of varying quality have been suggested [20,21]. A heuristic approach has been suggested in underwater networking for solving the surface gateway deployment o...

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Underwater Acoustic Modems

Cited by 240 publications

References 44 publications

Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

An Energy-Aware Data-Gathering Protocol Based on Clustering using AUV in Underwater Sensor Networks

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

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