Enabling Underwater Acoustic Cooperative MIMO Systems by Metamaterial-Enhanced Magnetic Induction

Desai, Soham; Sudev, Vaishnendr D; Tan, Xin; Wang, Pu; Sun, Zhi

doi:10.1109/wcnc.2019.8885489

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…First, the frequency error of the synchronization is analyzed herein [21]. The average relative clock drift in ∆T of the node z to the master node can be expressed as…”

Section: Synchronization Accuracy Analysismentioning

confidence: 99%

“…) is the underwater acoustic channel envelope [26], f is the signal frequency, and n(t) is the noise. Similar to the assumption in [21,29], as k nodes share the same transmitted signal, the amplitude H i can be recorded as H. Considering the surface BS deployed in the far-field, all the channel envelops are the same.…”

Section: Inter-cluster Acoustic Transmission Rangementioning

confidence: 99%

“…Such high speed signal propagation is able to significantly facilitate the synchronization of distributed nodes, which enables the long distance underwater communication and high throughput. Therefore, heterogeneous wireless underwater sensor networks adopting MI-assisted acoustic cooperative MIMO are proposed [21]. In such networks, the distributed nodes are divided into multiple clusters by region, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…The information exchange between different clusters are realized by the cooperative MIMO, while MI communication among intra-cluster nodes guarantee the accurate synchronization. The system architecture and process diagram of underwater MI-assisted acoustic cooperative MIMO networks are introduced in Section 2, and much more details can be also found in [21]. The performance of the underwater MI-assisted acoustic cooperative MIMO networks is obtained in [21], in which the performance gain of underwater MI-assisted acoustic cooperative MIMO networks is validated in terms of synchronization errors, signal-to-noise ratio (SNR), effective communication time, and the upper bound of the throughput in physical layer.…”

Section: Introductionmentioning

confidence: 99%

“…The system architecture and process diagram of underwater MI-assisted acoustic cooperative MIMO networks are introduced in Section 2, and much more details can be also found in [21]. The performance of the underwater MI-assisted acoustic cooperative MIMO networks is obtained in [21], in which the performance gain of underwater MI-assisted acoustic cooperative MIMO networks is validated in terms of synchronization errors, signal-to-noise ratio (SNR), effective communication time, and the upper bound of the throughput in physical layer. In this paper, we focus on the system level performance of underwater MI-assisted acoustic cooperative MIMO networks in terms connectivity and analyze the impact of clock synchronization on connectivity.…”

Section: Introductionmentioning

confidence: 99%

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Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks

Ren

Sun

Huo

et al. 2020

Sensors

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In traditional underwater wireless sensor networks (UWSNs), it is difficult to establish reliable communication links as the acoustic wave experiences severe multipath effect, channel fading, and ambient noise. Recently, with the assistance of magnetic induction (MI) technique, cooperative multi-input-multi-output (MIMO) is utilized in UWSNs to enable the reliable long range underwater communication. Compared with the acoustic-based UWSNs, the UWSNs adopting MI-assisted acoustic cooperative MIMO are referred to as heterogeneous UWSNs, which are able to significantly improve the effective cover space and network throughput. Due to the complex channel characteristics and the heterogeneous architecture, the connectivity of underwater MI-assisted acoustic cooperative MIMO networks is much more complicated than that of acoustic-based UWSNs. In this paper, a mathematical model is proposed to analyze the connectivity of the networks, which considers the effects of channel characteristics, system parameters, and synchronization errors. The lower and upper bounds of the connectivity probability are also derived, which provide guidelines for the design and deployment of underwater MI-assisted acoustic cooperative MIMO networks. Monte Carlo simulations were performed, and the results validate the accuracy of the proposed model.

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“…First, the frequency error of the synchronization is analyzed herein [21]. The average relative clock drift in ∆T of the node z to the master node can be expressed as…”

Section: Synchronization Accuracy Analysismentioning

confidence: 99%

Section: Inter-cluster Acoustic Transmission Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks

Ren

Sun

Huo

et al. 2020

Sensors

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Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Wei

Guo

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

117

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Reliable data collection techniques, whose aim is to ensure that sensed data are received successfully by a sink, are essential for applications in Underwater Wireless Sensor Networks (UWSNs). However, traditional data collection with Radio Frequency (RF) functions poorly in UWSNs due to peculiar features of underwater. Moreover, acoustic communication creates challenges for the reliability of data collection such as high bit error rate, packet collision and voids in routing. Furthermore, the deployment of Autonomous Underwater Vehicles (AUVs) in some scenarios changed the paradigm of data collection and introduced new issues that affect reliability such as inaccurate navigation and lengthy travel time. Consequently, numerous studies focus on the relative reliability of various currently available data collection in UWSNs. In this paper, we first review the problems specific to UWSNs and their impact on reliable data collection. It is followed by a discussion about characteristics, challenges, and features associated with the design of reliable techniques in UWSNs. Afterward, to provide readers with an overview of reliable data collection techniques in UWSNs, this paper categorizes them according to their ability to enhance reliability at all the key stages of data collection. In this categorization framework, the advantages and disadvantages of each technique have been in-depth discussed. Finally, several possible areas for further research are identified and discussed.

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Underwater Motion and Activity Recognition using Acoustic Wireless Networks

Sun

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Enabling Underwater Acoustic Cooperative MIMO Systems by Metamaterial-Enhanced Magnetic Induction

Cited by 7 publications

References 14 publications

Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks

Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Underwater Motion and Activity Recognition using Acoustic Wireless Networks

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