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Energy-efficient compressed data aggregation in underwater acoustic sensor networks

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Abstract

In this paper, we propose an energy-efficient compressed data aggregation framework for three-dimensional underwater acoustic sensor networks (UASNs). The proposed framework consists of two layers, where the goal is to minimize the total energy consumption of transmitting the data sensed by nodes. The lower layer is the compressed sampling layer, where nodes are divided into clusters. Nodes are randomly selected to conduct sampling, and then send the data to the cluster heads through random access channels. The upper layer is the data aggregation layer, where full sampling is adopted. We also develop methods to determine the number of clusters and the probability that a node participates in data sampling. Simulation results show that the proposed framework can effectively reduce the amount of sampling nodes, so as to reduce the total energy consumption of the UASNs.

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Notes

  1. vec(\(\cdot\)) refers to an operator that transforms a \(k\,\times \, k\) matrix into a \(kk\,\times \, 1\) vector, i.e., \({\hbox {vec}}({\mathbf {X}}) = [x_{11},\ldots ,x_{k1}, x_{12},\ldots , x_{k2}, \ldots ,x_{1k},\ldots ,x_{kk}]^{T}\), and \(\otimes\) is the Kronecker Product.

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Acknowledgments

This work was supported in part by the National High-Tech R&D Program (“863” Program) of China under Grants 2014AA01A701 and 2015AA011303; by the National Natural Science Foundation of China under Grants 61271226, 61272410, 61202460 and 61471408; by the National Natural Science Foundation of Hubei Province under Grant 2014CFA040; by the Science and Technology Plan Projects of Wuhan City under Grant 2015010101010022; and by the Fundamental Research Funds for Central Universities under Grant 2015QN073.

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Authors and Affiliations

  1. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Hongzhi Lin, Wei Wei, Ping Zhao, Xiaoqiang Ma, Wenping Liu, Tianping Deng & Kai Peng

  2. Hubei University of Economics, Wuhan, 430205, People’s Republic of China

    Wenping Liu

  3. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Rui Zhang

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  1. Hongzhi Lin
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  2. Wei Wei
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  3. Ping Zhao
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  4. Xiaoqiang Ma
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Correspondence to Xiaoqiang Ma.

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Lin, H., Wei, W., Zhao, P. et al. Energy-efficient compressed data aggregation in underwater acoustic sensor networks. Wireless Netw 22, 1985–1997 (2016). https://doi.org/10.1007/s11276-015-1076-z

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