A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Luo, Junhai; Fan, Liying

doi:10.3390/s17040726

Cited by 34 publications

(27 citation statements)

References 39 publications

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“…Without node location information, the collected data has no meaning [20][21]. However, the localization of underwater sensor nodes is affected by many factors such as underwater environment and underwater acoustic communication which makes existing WSN localization algorithms can not be applied to underwater [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…The TP-TSFLA [14] and SLA [15] algorithms were chosen because they have many similarities with the RCL algorithm. The TP-TSFLA algorithm has two phases.…”

Section: Simulationsmentioning

confidence: 99%

“…It is centered on the beacon node, and collects multiple circles with a communication distance as a radius to obtain the coordinates of the node. Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA) [14] uses the geometric relationship between nodes to locate. The Sensor Localization Algorithms (SLA) [15] uses iterative methods to extend the intersection area to the surrounding area to calculate the coordinates of the unknown node.…”

Section: Introductionmentioning

confidence: 99%

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A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

Guo¹,

Kang²,

Han³

et al. 2019

KSII TIIS

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Node localization is the basic task of underwater wireless sensor networks (UWSNs). Most of the existing underwater localization methods rely on ranging accuracy. Due to the special environment conditions in the ocean, beacon nodes are difficult to deploy accurately. The narrow bandwidth and high delay of the underwater acoustic communication channel lead to large errors. In order to reduce the ranging error and improve the positioning accuracy, we propose a localization algorithm based on ranging correction and inertial coordination. The algorithm can be divided into two parts, Range Correction based Localization algorithm (RCL) and Inertial Coordination based Localization algorithm (ICL). RCL uses the geometric relationship between the node positions to correct the ranging error and obtain the exact node position. However, when the unknown node deviates from the deployment area with the movement of the water flow, it cannot communicate with enough beacon nodes in a certain period of time. In this case, the node uses ICL algorithm to combine position data with motion information of neighbor nodes to update its position. The simulation results show that the proposed algorithm greatly improves the positioning accuracy of unknown nodes compared with the existing localization methods.

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Section: Related Workmentioning

confidence: 99%

“…The TP-TSFLA [14] and SLA [15] algorithms were chosen because they have many similarities with the RCL algorithm. The TP-TSFLA algorithm has two phases.…”

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

Guo¹,

Kang²,

Han³

et al. 2019

KSII TIIS

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show abstract

“…Global positioning systems (GPS) are widely used in many applications like navigation, control of autonomous road vehicles [ 1 ] and unmanned aerial vehicles [ 2 ]. In sonar, radar and underwater radar, it is often of interest to determine the location of an object from its emissions [ 3 , 4 ]. Indoor localization in wireless networks is addressed relying on a swarm-based approach [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Standard least squares (SLS) and hyperbolic least squares (HLS) undertake the search using the Newton–Raphson optimization technique [ 14 , 15 ]. Other iterative algorithms base the search on metaheuristic techniques such as PSO [ 4 , 16 , 17 ]. The second category of non-iterative algorithms is widely used in GPS, where the emitters are orbiting satellites and the source target is to locate a sensor receiving the emissions.…”

Section: Introductionmentioning

confidence: 99%

Survey on the Performance of Source Localization Algorithms

Fresno

Robles

Martínez-Tarifa

et al. 2017

Sensors

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The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

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Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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A Two-Phase Time Synchronization-Free Localization Algorithm for Underwater Sensor Networks

Cited by 34 publications

References 39 publications

A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

A Localization Algorithm for Underwater Wireless Sensor Networks Based on Ranging Correction and Inertial Coordination

Survey on the Performance of Source Localization Algorithms

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

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