Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Emokpae, Lloyd; Younis, Mohamed

doi:10.1145/2537130

Cited by 13 publications

(4 citation statements)

References 32 publications

(64 reference statements)

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“…, L}, where κ b is the bottom reflection coefficient, which (presumably) can be determined based on prior physical knowledge (e.g., assuming the bottom is sand, silt, clay, rock, etc.) and the angle of incidence, and is assumed to be known within the MFP framework for a given hypothesized emitter location p. For (10), we assumed a perfectly reflecting ocean surface [8], [31], which approximately holds for calm shallow waters. Based on this knowledge, the channel responses {H } can be readily computed.…”

Section: The Matched Field Processing Solutionmentioning

confidence: 99%

A Semi-Blind Method for Localization of Underwater Acoustic Sources

Weiss,

Arikan,

Vishnu

et al. 2021

Preprint

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Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when such structure includes occlusion, which causes the loss of line-of-sight (LOS) between the acoustic source and the receivers, on which many of the existing localization algorithms rely. We develop a semi-blind passive localization method capable of accurately estimating the source's position even in the possible absence of LOS between the source and all receivers. Based on typically-available prior knowledge of the water surface and bottom, we derive a closed-form expression for the optimal estimator under a multi-ray propagation model, which is suitable for shallow-water environments and high-frequency signals. By exploiting a computationally efficient form of this estimator, our methodology makes comparatively high-resolution localization feasible. We also derive the Cramér-Rao bound for this model, which can be used to guide the placement of collections of receivers so as to optimize localization accuracy. The method improves a balance of accuracy and robustness to environmental model mismatch, relative to existing localization methods that are useful in similar settings. The method is validated with simulations and water tank experiments.

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Section: The Matched Field Processing Solutionmentioning

confidence: 99%

A Semi-Blind Method for Localization of Underwater Acoustic Sources

Weiss,

Arikan,

Vishnu

et al. 2021

Preprint

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“…A complete survey for underwater localization schemes is provided in [16], where the tradeoff between various localization methods is highlighted. Emokpae and Younis have proposed a surface-based reflection scheme to perform localization using both line-of-sight and non-line-of-sight links [17]. The authors use an average SS, i.e., a static medium model, to distinguish the different received signals, namely reflected and direct paths.…”

Section: Related Workmentioning

confidence: 99%

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Ahmed

Younis

2019

Applied Sciences

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To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process.

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“…Intuitively,

is smaller than the communication radius r of sensor nodes, which is assumed to be the same for all sensor nodes in this article. Note that when

is almost the same in value as r , the acoustic transmission loss should be high, the packet delivery ratio should be low and the bit error rate should be large [ 31 , 32 ]. This means that

may not be appropriate to serve as the parent of

, although

is still within the communication radius of

.…”

Section: Preliminaries: Routing Tree Construction and Maintenancementioning

confidence: 99%

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

Zhou

Xing

Duan

et al. 2015

Sensors

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With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

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Surface-Reflection-Based Communication and Localization in Underwater Sensor Networks

Cited by 13 publications

References 32 publications

A Semi-Blind Method for Localization of Underwater Acoustic Sources

A Semi-Blind Method for Localization of Underwater Acoustic Sources

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

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