UREAL: Underwater Reflection-Enabled Acoustic-Based Localization

Emokpae, Lloyd; DiBenedetto, Stephen; Potteiger, Bradley; Younis, Mohamed

doi:10.1109/jsen.2014.2357331

Cited by 52 publications

(23 citation statements)

References 24 publications

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“…Sensory data collected by U V are gathered and routed to SN with respect to the requirements of widespread domain applications [18], [13]. Sensor nodes can be (i) static, when attached with docks, (ii) semi-mobile, when deployed with buoys or ships, or (iii) mobile, when attached with autonomous underwater vehicles [19]. In this paper we assume that sensor nodes are semi-mobile, whose position changes with the water dynamics, and usually, sensor nodes are continuously moving from 2 to 3 m/sec (3-5 km/h) in the horizonal and/or vertical directions [20].…”

Section: Carp and Problem Definitionmentioning

confidence: 99%

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E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

et al. 2016

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With the advance of the Internet of Underwater Things, smart things are deployed under the water and form the underwater wireless sensor networks (UWSNs), to facilitate the discovery of vast unexplored ocean volume. A routing protocol, which is not expensive in packets forwarding and energy consumption, is fundamental for sensory data gathering and transmitting in UWSNs. To address this challenge, this paper proposes E-CARP, which is an enhanced version of the C hannel-Aware Routing Protocol (CARP) developed by S. Basagni et al., to achieve the location-free and greedy hop-by-hop packet forwarding strategy. Generally, CARP does not consider the reusability of previously collected sensory data to support certain domain applications afterwards, which induces data packets forwarding which may not be beneficial to applications. Besides, the PING-PONG strategy in CARP can be simplified for selecting the most appropriate relay node at each time point, when the network topology is relatively steady. These two research problems have been addressed by our E-CARP. Simulation results validate that our technique can decrease the communication cost significantly and increase the network capability to a certain extent.

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Section: Carp and Problem Definitionmentioning

confidence: 99%

“…Given a PONG control packet received by nv sn originated from uv z (line 14), g(uv z ) is computed and G nvsn is updated accordingly (lines [15][16]. If uv z is a better candidate than nv rly sn which was previously chosen as the relay node, nv rly sn is replaced by uv z (lines [17][18][19][20].…”

Section: Algorithm 2 Relaynodeselectionmentioning

confidence: 99%

E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

et al. 2016

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“…If assuming the surface of building is pure mirror, such assumption is too ideal, failing to consider the convexes or concaves existed on the surface of building, or the impact on signal propagation path by some uncertain factors. Referring to [8], we need comprehensively consider various factors on the surface of building that may cause impact on signal propagation in order to make signal propagation model in microcell closer to reality. This paper thinks that when signal is reflected on the surface of building, reflected ray will have certain derivation compare with the pure specular reflection, thus a small deflection angle F  will be added, as is shown in Fig.1.…”

Section: Signal Propagation Environment Modelmentioning

confidence: 99%

“…Ref. [8] thinks the fluctuation surface of water changes with time is not pure plain. When signal reflects under water, reflex angle is determined jointly by the tangent plane at reflection point by fluctuation surface and incident ray.…”

Section: Introductionmentioning

confidence: 99%

A GDOP-Weighted Intersection Method for Ray-Trace Based Target Localization using AOA Measurements in Quasi-Specular Environment

Kong¹,

Zheng²,

Song³

et al. 2016

Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016TOEC 2016)

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Abstract. Whereas environment modeling of current ray tracing algorithm used for locating is too ideal and the positioning accuracy is not high because AOA based weight intersection method fails to consider singularity bearing line intersection, this paper sets up AOA localization model on quasi-specular condition and proposes a GDOP-weighted intersection method (GWIAOA-RT) for ray-trace based target localization using AOA measurements. The method firstly utilizes idea of cluster to complete tracing propagation paths of multipath signal, gains virtual building surface after revised according to tracing result, constructs image base station, translates non-line-of-sight(NLOS) path into line-of-sight(LOS) path; then utilizes AOA measurements on each path and gets all intersections of bearing line; further utilizes circular error probable (CEP) to determine effective intersections, gives corresponding weight according to Geometric Dilution of Precision (GDOP) of effective intersections, and weighted merge them to estimate the position of target. The method utilizes multipath signal to eliminate the impact of NLOS noise on positioning accuracy as well as consideration of position error of image base station in GDOP calculation. It has strong robustness. Simulation result shows that compared with existing methods, GWIAOA-RT method has higher positioning accuracy and better adaptation in different application scenarios.

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“…However, the former is infeasible since RF is not applicable in aquatic environments [5], while the latter can lead to problems due to the unpredictable UWA velocities [6]. AoA relies on a direct line-of-sight (LOS) UWA transmission path which may not exist at all unlike in terrestrial radio [7], while typical multi-path components in UWA channels can also lead to large errors in AoA measurements [8]. RSS-based underwater source localization gets less attention as another alternative to measure distance, since it is difficult to achieve accurate ranging due to multipath propagations and the complicated UWA transmission loss (TL) phenomena [3].…”

Section: Introductionmentioning

confidence: 99%

RSS-based sensor localization in underwater acoustic sensor networks

Zhang

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which uses electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena. In this paper, we address this issue and propose two novel semidefinite programming (SDP) approaches which can be solved more efficiently. The numerical results validate our proposed SDP solvers in underwater environments, and indicate that the placement of the anchor nodes influences the RSS-based localization accuracy similarly as in the terrestrial counterpart. We also highlight that adopting traditional terrestrial RSS-based localization methods will fail in underwater scenarios.

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UREAL: Underwater Reflection-Enabled Acoustic-Based Localization

Cited by 52 publications

References 24 publications

E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

E-CARP: An Energy Efficient Routing Protocol for UWSNs in the Internet of Underwater Things

A GDOP-Weighted Intersection Method for Ray-Trace Based Target Localization using AOA Measurements in Quasi-Specular Environment

RSS-based sensor localization in underwater acoustic sensor networks

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