Error Analysis of a Distributed Node Positioning Algorithm in Underwater Acoustic Sensor Networks

Liu, Yiwen; Fang, Guocan; Chen, Huifang; Xie, Lei; Fan, Rong; Su, Xiangrui

doi:10.1109/wcsp.2018.8555657

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…• PA D represents as the packet delay • Rxt destn is the time of reception at destination • Txt source is the Time of Tranmission at source • d is the average delay • N (Rxp) Received packet count In this research AEA-QoS data hold rate time in the buffer which leads to increase the reliability of data which is calculated based on service request time [38], [39] with less packet delay as shown in the Eq (18) and graphical representation as shown in the figure .12.…”

Section: Resultsmentioning

confidence: 99%

Adaptive Energy Aware Quality of Service for Reliable Data Transfer in Under Water Acoustic Sensor Networks

et al. 2019

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Currently, reliable data transfer, and energy management have been considered as a significant research challenge in the underwater acoustic sensor networks (UWASN) owing to high packet loss, limited ratio of bandwidth with significant incur of energy, network life time with high propagation delay, less precision with high data hold time and so on. Energy saving and maintaining quality of service (QoS) is more important for UWASN owing to QoS application necessity and limited sensor nodes. To address this issue, several existing algorithms such as adaptive data forwarding algorithms, QoS-based congestion control algorithms and several methodologies were proposed with high throughput and less network lifetime as well as the less utilization of energy in UWASN by choosing sensor nodes data based on data transfer and link reliability. However, all the conventional algorithms have fixed data hold time, which incurs more end-to-end delay with less reliability of data and consumption of high energy due to high data transfer reachability. This high end research proposes adaptive energy aware quality of service (AEA-QoS) algorithm for reliable data delivery by formulating discrete times stochastic control process and deep learning techniques for UWSAN to overcome these issues. The proposed algorithm has been validated with conventional state-of-the-art methods and results show that the proposed approach exhibits its effectiveness in terms of less network overhead and propagation delay with high throughput and less energy consumption for every reliable packet transmission.INDEX TERMS Under water acoustic sensor networks, reliable data transfer, quality of service (QoS), deep learning.

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Section: Resultsmentioning

confidence: 99%

Adaptive Energy Aware Quality of Service for Reliable Data Transfer in Under Water Acoustic Sensor Networks

et al. 2019

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“…To solve this problem, the Error Propagation (EP) model [19] is utilized to derive the position error of the target trajectory and correct the traditional GDOP through error information. Hence, a Geometric Dilution of Precision based on Error-propagation (EGDOP) model is proposed to evaluate the calibrating quality of beacons given different sub-sets of target trajectory.…”

Section: A Egdop Modelmentioning

confidence: 99%

“…According to (7), the error equation can be obtained by the total differential form of (7) and the EP law [19]: ] is the direction cosine of the target relative to obs beacon. The position error noise of obs beacon (the error of GP beacons converted from UP beacons is derived from the subsequent discussion) and the ranging error noise can be acquired in advance.…”

Section: ) Position Error Of Estimated Target Trajectorymentioning

confidence: 99%

A Novel Simultaneous Calibration and Localization Algorithm Framework for Indoor Scenarios

Deng

Liu

et al. 2020

IEEE Access

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Under the navigational process of simultaneous beacon-calibrating and target-positioning, the real-time positioning and calibrating accuracy in indoor scenarios is significantly limited. To solve it, a novel Simultaneous Calibration and Localization (SCAL) algorithm framework for indoor scenarios is proposed. The proposed framework is mainly divided into the Target Localization and Beacon Calibration (TLBC) and the Global Optimization (GO) section: in the TLBC section, under the typical distributed beacon layout, a processing mechanism for synchronously locating the target and calibrating beacons is proposed; in GO section, parallel to TLBC section, a global optimization method based on Geometric Dilution of Precision based on Error-propagation (EGDOP) model is further proposed, and it can utilize the geometric trajectory of target and positioning error of target and beacons to efficiently and simultaneously optimize the positioning of target trajectory and beacons from TLBC section. The numerical simulation results show that the improvement of the GO section on the positioning performance is verified. Compared with the SCAL algorithm based on backward processing with inverse trajectory, the proposed framework has improved the accuracy of target and beacon positioning by 39.06% and 58.01%, respectively, and accuracy of positioning reaches 0.2557m and 0.2437m, respectively, in an actual indoor positioning scene.

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Reinforcement Learning-Based Asynchronous Localization of USNs

et al. 2012

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Error Analysis of a Distributed Node Positioning Algorithm in Underwater Acoustic Sensor Networks

Cited by 12 publications

References 8 publications

Adaptive Energy Aware Quality of Service for Reliable Data Transfer in Under Water Acoustic Sensor Networks

Adaptive Energy Aware Quality of Service for Reliable Data Transfer in Under Water Acoustic Sensor Networks

A Novel Simultaneous Calibration and Localization Algorithm Framework for Indoor Scenarios

Reinforcement Learning-Based Asynchronous Localization of USNs

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