LDB: Localization with Directional Beacons for Sparse 3D Underwater Acoustic Sensor Networks

Luo, Hanjiang; Guo, Zhongwen; Dong, Wei; Hong, Feng; Zhao, Yiyang

doi:10.4304/jnw.5.1.28-38

Cited by 83 publications

(56 citation statements)

References 31 publications

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“…Range-based systems use techniques such as ultrasound to measure the distance between nodes and then triangulation to compute the positions of nodes. Range-free techniques use implicit information provided by anchor nodes to obtain positions of nodes, such as number of hops between devices or radio coverage membership [8][9][10]. Although range-free protocols do not need additional hardware for distance measurements, they can only provide rough positional estimates.…”

Section: Centralized Localization Versus Distributed Localizationmentioning

confidence: 99%

“…LDB [9,10], AAL [20,21], DNRL [22,23], and MobiL [24] focus on how anchors provide their positions to the sensor nodes and assume that the anchors are mobile and equipped with GPS to obtain their locations. Their disadvantages include high localization delays and high cost as the accuracy of the location relies on a large number of mobile beacons, especially for large-scale UWSNs.…”

Section: Distributed Localization Techniquesmentioning

confidence: 99%

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Hybrid Localization Approach for Underwater Sensor Networks

Tsai

Wang

2017

Journal of Sensors

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Underwater Wireless Sensor Networks (UWSNs) are widely used to collect data in the marine environment. Location and time are essential aspects when sensors collect data, particularly in the case of location-aware data. Many studies on terrestrial sensor networks consider sensor locations as the locations where data is collected and focus on sensor positioning when sensors are fixed. However, underwater sensors are mobile networks and the sensor locations change continuously. Localization schemes designed for static sensor networks need to run periodically to update locations and consume considerable sensor power and increase the communication overhead; hence, they cannot be applied to UWSNs. This paper presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA), which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model and conduct extensive simulations. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy.

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Section: Centralized Localization Versus Distributed Localizationmentioning

confidence: 99%

Section: Distributed Localization Techniquesmentioning

confidence: 99%

Hybrid Localization Approach for Underwater Sensor Networks

Tsai

Wang

2017

Journal of Sensors

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“…That is, they assume the radiation energy of the transducer is focused on some angle range with no leaking of radiation energy outside this range. For example, such transducers are used for localization using directional beacons in [16] and for directional packet forwarding in [17]. These solutions also use only one frequency.…”

Section: Related Workmentioning

confidence: 99%

QUO VADIS: QoS-aware underwater optimization framework for inter-vehicle communication using acoustic directional transducers

Chen

Pompili

2011

2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

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Abstract-Underwater acoustic communications consume a significant amount of energy due to the high transmission power (10 − 50 W) and long data packet transmission times (0.1 − 1 s). Mobile Autonomous Underwater Vehicles (AUVs) can conserve energy by waiting for the 'best' network topology configuration, e.g., a favorable alignment, before starting to communicate. Due to the frequency-selective underwater acoustic ambient noise and high medium power absorption -which increases exponentially with distance -a shorter distance between AUVs translates into a lower transmission loss and a higher available bandwidth. By leveraging the predictability of AUV trajectories, a novel solution is proposed that optimizes communications by delaying packet transmissions in order to wait for a favorable network topology (thus trading end-to-end delay for energy and/or throughput). In addition, the solution proposed -which is implemented and compared with other solutions using an emulator that integrates underwater acoustic WHOI Micro-Modems -exploits the frequency-dependent radiation pattern of underwater acoustic transducers to reduce communication energy consumption by adjusting the transducer directivity on-the-fly.

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“…Also, the matrix with only some static values cannot cover dynamically changed mobility patterns. Recently, some researchers have suggested AUV (autonomous underwater vehicle)-aided localization using directional antenna in [17][18][19][20]. They improved the localization accuracy because AUV provides relatively highprecision information to the nodes.…”

Section: Localizationmentioning

confidence: 99%

SLSMP: Time Synchronization and Localization Using Seawater Movement Pattern in Underwater Wireless Networks

Kim

Yoo

2014

International Journal of Distributed Sensor Networks

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Time synchronization and localization in underwater environment are challenging due to high propagation delay, time measurement error, and node mobility. Although synchronization and localization depend on each other and have the similar process, they have been usually handled separately. In this paper, we suggest time synchronization and localization based on the semiperiodic property of seawater movement, called SLSMP. Firstly, we analyze error factors in time synchronization and localization and then propose a method to handle those errors. For more accurate synchronization, SLSMP controls the transmission instant by exploiting the pattern of seawater movement and node deployment. Then SLSMP progressively decreases the localization errors by applying the Kalman filter or averaging filter. Finally, INS (inertial navigation system) is adopted to relieve localization error caused by node mobility and error propagation problem. The simulation results show that SLSMP reduces time synchronization error by 2.5 ms and 0.56 ms compared with TSHL and MU-Sync, respectively. Also localization error is lessened by 44.73% compared with the single multilateration.

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LDB: Localization with Directional Beacons for Sparse 3D Underwater Acoustic Sensor Networks

Cited by 83 publications

References 31 publications

Hybrid Localization Approach for Underwater Sensor Networks

Hybrid Localization Approach for Underwater Sensor Networks

QUO VADIS: QoS-aware underwater optimization framework for inter-vehicle communication using acoustic directional transducers

SLSMP: Time Synchronization and Localization Using Seawater Movement Pattern in Underwater Wireless Networks

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