Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks
Fast Node Clustering Based on an Improved Birch Algorithm for Data Collection Towards Software-Defined Underwater Acoustic Sensor Networks
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DOI：
10.1109/jsen.2021.3055948
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发表时间：
2021-11
影响因子：
4.3
通讯作者：
Chuan Lin;Guangjie Han;Ting-ting Wang;Yuanguo Bi;Jiaxin Du;Bin Zhang
中科院分区：
文献类型：
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作者：
Chuan Lin;Guangjie Han;Ting-ting Wang;Yuanguo Bi;Jiaxin Du;Bin Zhang
本文对水声传感器网络中的数据采集调度问题进行了深入研究。为了解决这一问题，我们提出使用软件定义网络(SDN)技术，并在此基础上提出了软件定义水声传感器网络(SD-UASN)的范例。结合SD-UASN，提出了一种基于节点分簇的高效数据采集方案。在考虑数据规模的基础上，提出了一种基于改进BIRCH算法的快速节点聚类方法，包括节点预聚类和节点重聚类。特别是对于节点预聚类过程，我们对BIRCH算法的簇特征树进行了改进，将每个簇中的节点限制在最优距离之内。基于提出的预聚类方法，我们提出了一种贪婪的数据收集方案，该方案能够在考虑压缩采样的情况下启发式地寻找最优解。评估结果表明，我们的方案在网络效率方面优于一些流行的方案。
In this paper, we take an in-depth study on the data collection scheduling issue in underwater acoustic sensor networks (UASNs). To address this issue, we propose the use of software-defined networking (SDN) technique, based on which the paradigm of software-defined underwater acoustic sensor networks (SD-UASNs) is proposed. With SD-UASNs, we propose an efficient data collection scheme on account of node clustering. We take the data scale into account, propose a fast node clustering approach based on an improved Birch algorithm including node pre-clustering and node re-clustering. In particular, for the node pre-clustering procedure, we improve the cluster feature tree of the Birch algorithm and limit the nodes in each cluster to be within an optimal distance. Based on the proposed pre-clustering approach, we propose a greedy data collection scheme that can heuristically seek optimal solutions when the compressed sampling is taken into account. Evaluation results demonstrate that our scheme performs better in network efficiency than some popular schemes.