Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks

Lin, Chuan; Han, Guangjie; Wang, Tingting; Bi, Yuanguo; Du, Jian Hua; Zhang, Bin

doi:10.1109/jsen.2021.3055948

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Data Acquisition Focused [51], [52] Develop Efficient sensor node placement strategies [53] Develop Optimization strategy of computing processes [54], [55], [56], [57], [58], [59], [60], [61] Develop Output from specific sensors and interpreting process [62] Develop Routing strategies for power consumption efficiency [63], [64] Develop Data transmission speed enhancement for efficient data acquisition [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75] Develop Optimization strategies for ML, DL, RL, and specific algorithms [76], [77], [78], [79], [80], [81] Develop Optimal network resource management [82], [83], [84] Develop Integration of diverse sensors (Fusion), interpretation, and optimization strategies [85], [86], [87], [88], [89], [90], [91], [92] Develop RL model for optimizing interpretation Passive/Active Sound-navigation-and-ranging (SONAR) [93] Develop Efficient routing design for data acquisition optimization [94] Developing strategies for detecting...…”

Section: Researchmentioning

confidence: 99%

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Wibisono,

Piran,

Song

et al. 2024

IEEE Access

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This article introduces an innovative approach to the navigation of autonomous underwater vehicles (AUVs) in inspection and data acquisition missions within underwater wireless sensor networks (UWSNs). We combine at least five underwater navigation techniques to accomplish this mission, adapted from related works. These five techniques are employed to address at least four main challenges identified in inspection and data acquisition missions in UWSNs involving the use of AUVs, namely: communication constraints, energy usage optimization, precise navigation, and effective data acquisition. Limited simulations conducted demonstrate the reliability of the proposed model. The model successfully navigates to the target point in 3D coordinates X Y Z, assuming the launch point as d0 (10 40 40), and reaches the q-goal target point (45 45 0) within 21 seconds, with the addition of uattractive and urepulsive (magnetic beacon attraction force and repulsion force as simulation of underwater current disturbance factor). Furthermore, in the inspection and data acquisition mission in UWSN simulated as node points (o) in pink, AUV (*) in blue effectively follows the predetermined points while acquiring data, as indicated by green lines (-) within just 5 seconds, achievable by increasing the value of α (angle of attack) of the target node to reduce delay time. The evaluation of the experimental simulations has raised issues and future research challenges, including the development of environmental simulation challenges that can closely resemble real conditions, the measurement of energy usage effectiveness to reach each target point, and the potential development of underwater recharging techniques. Furthermore, there is a need for advanced precise navigation and the advancement of effective data acquisition techniques.

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Section: Researchmentioning

confidence: 99%

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Wibisono,

Piran,

Song

et al. 2024

IEEE Access

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“…There are many clustering algorithms, which can be roughly divided into several categories according to their working mechanisms: density-based, such as DPC algorithm [2], DBSCAN algorithm [3]; model-based, such as Gaussian mixture model (GMM) algorithm [4]; Fuzzy-based clustering, such as FCM algorithm [5]; hierarchy-based, such as BIRCH algorithm [6], CURE algorithm [7]; grid-based, such as STING algorithm, CLIQUE algorithm; partition-based, such as K-means Algorithms [8]; graph-based, such as spectral clustering algorithms [9]. Each clustering algorithm has its advantages and disadvantages, and has its own applicable scenarios.…”

Section: Selection Of Clustering Algorithmsmentioning

confidence: 99%

Visualization-based clustering algorithm experimental teaching design

He¹,

He²,

Hu³

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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Clustering is an important part of artificial intelligence and is widely used in data mining, pattern recognition, natural language processing, computer vision and other aspects. Clustering algorithms are complex and difficult to master. In order to make learning easier and more interesting, a visual clustering experiment is designed. The first is the introduction of clustering algorithms and clustering indicators; then basic experiments are designed to show the working principle and clustering characteristics of each algorithm through the visualization of the clustering process and the visualization of the clustering results, and introduce the self-generated artificial data to the method to verify the characteristics of the algorithm; the last is the application experiment, through the face clustering and image segmentation experiments to improve the students' interest in learning.

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“…Data communication is responsible for data collection and transmission, which is divided into three phases: intra-cluster communication, inter-cluster communication, and communication between the switch node and the controller. The clustering process is triggered by the control communication before the intra-cluster communication by adopting the method in [18].…”

Section: The Communication Process Of Sm-uasnmentioning

confidence: 99%

A Multi-Controllers Architecture for Software-Defined Underwater Acoustic Sensor Networks

Shi

Huang

Jiang

et al. 2022

2022 IEEE International Performance, Computing, and Communications Conference (IPCCC)

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The advancement of communication, computing, and the Internet of Underwater Things (IoUT) led to the blooming of marine applications. However, due to the constrained resources of underwater nodes and highly dynamic underwater environment, traditional Underwater Acoustic Sensor Networks (UASNs) become incompetent for such enormous demands. Software-Defined N etworking ( SDN) i s a p romising approach to improve the flexibility a nd r eliability o f U ASN. T his paper introduces an SDN-enabled UASN with multi-controllers called SM-UASN. Firstly, a hierarchical framework of SM-UASN is offered. Then the energy consumption model and communication process of SM-UASN are established. Finally, we implement a simulation platform for SM-UASN by integrating Mininet and the NS-3 underwater acoustic network (UAN) module. SM-UASN is compared with the traditional single-sink-based UASN (TS-UASN), the traditional multi-sink-based UASN (TM-UASN), and the SDN-based UASN with a single controller (SS-UASN). The result reveals that our proposed architecture significantly prolongs the network's lifetime and increases the throughput.

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Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks

Cited by 10 publications

References 28 publications

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Visualization-based clustering algorithm experimental teaching design

A Multi-Controllers Architecture for Software-Defined Underwater Acoustic Sensor Networks

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