Energy-Efficient Cooperative MIMO Formation for Underwater MI-Assisted Acoustic Wireless Sensor Networks

The energy problem has become one of the critical factors limiting the development of underwater wireless sensor networks (UWSNs), and cooperative multiple-input–multiple-output (MIMO) technology has shown advantages in energy saving. However, the design of energy-efficient cooperative MIMO techniqu...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 15; p. 3641
Main Authors Ren, Qingyan, Sun, Yanjing, Wang, Tingting, Zhang, Beibei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
Acoustics
Algorithms
Communication
Connectivity
cooperative MIMO
data aggregation
Data compression
Data management
Data transmission
Design
Energy conservation
Energy consumption
Energy efficiency
heterogeneous underwater wireless sensor networks
Joint use
Magnetic induction
Mathematical models
MIMO communication
Nodes
Optimization
Receivers & amplifiers
Sensors
Signal to noise ratio
Transmitters
Underwater
Underwater acoustics
Wireless networks
Wireless sensor networks
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Summary:The energy problem has become one of the critical factors limiting the development of underwater wireless sensor networks (UWSNs), and cooperative multiple-input–multiple-output (MIMO) technology has shown advantages in energy saving. However, the design of energy-efficient cooperative MIMO techniques does not consider the actual underwater environment, such as the distribution of nodes. Underwater magnetic induction (MI)-assisted acoustic cooperative MIMO WSNs as a promising scheme in throughput, signal-to-noise ratio (SNR), and connectivity have been demonstrated. In this paper, the potential of the networks to reduce energy consumption is further explored through the joint use of cooperative MIMO and data aggregation, and a cooperative MIMO formation scheme is presented to make the network more energy efficient. For this purpose, we first derive a mathematical model to analyze the energy consumption during data transmission, considering the correlation between data generated by nodes. Based on this model, we proposed a cooperative MIMO size optimization algorithm, which considers the expected transmission distance and transmission power constraints. Moreover, a competitive cooperative MIMO formation algorithm that jointly designs master node (MN) selection and cooperative MIMO size can improve energy efficiency and guarantee the connectivity of underwater nodes and surface base station (BS). Our simulation results confirm that the proposed scheme achieves significant energy savings and prolongs the network lifetime considerably.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14153641