Recent Progress of Air/Water Cross-Boundary Communications for Underwater Sensor Networks: A Review

Luo, Hanjiang; Wang, Jinglong; Bu, Fanfeng; Ruby, Rukhsana; Wu, Kaishun

doi:10.1109/jsen.2022.3162600

Cited by 66 publications

(23 citation statements)

References 185 publications

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“…More specifically, UAVs can be flexibly deployed and assume the role of wireless relays, enabling multi-hop communication between ground BSs and sea vessels. In the case of underwater IoT, UAVs can greatly facilitate data collection by cooperating with USVs and UUVs, enhancing the capabilities of ocean monitoring systems [27]. Meanwhile, during SAR operations, UAVs can provide high capacity lineof-sight (LoS) links in order to facilitate the transmission of real-time video data among the participating vessels and ground stations [28].…”

Section: B Uav-aided Wireless Networkmentioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

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Section: B Uav-aided Wireless Networkmentioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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“…However, they also have certain limitations. To overcome all the limitations of .5 [72] 2021 Proposed solution 1 1 0 0 2 [73] 2019 Proposed solution 1 1 1 1 4 [74] 2022 Framework 1 1 0.5 2 4.5 [75] 2022 Proposed solution 0 0.5 0 1.5 2 [76] 2019 Proposed solution 1 0.5 1 0 2.5 [77] 2020 Survey 1 0.5 1 2 4.5 [78] 2021 Model 1 0.5 1 2 4.5 [79] 2019 Framework 0 0.5 1 2 3.5 [80] 2021 Model 0 0.5 0.5 1 2 [81] 2020 Proposed solution 1 2021 Proposed solution 1 1 0.5 2 4.5 [90] 2018 Framework 0 0.5 1 2 3.5 [91] 2022 Survey 1 1 0.5 2 4.5 [92] 2021 Survey 1 0.5 1 2 4.5 [93] 2021 Proposed solution 1 0.5 0 2 3.5 [30] 2021 Proposed solution 1 1 0.5 1.5 4 [94] 2017 Survey 1 0.5 0.5 0 2 [95] 2017 Framework 1 0.5 0 0 1.5 [96] 2020 Proposed solution 1 0. In recent years, SAGIN has been used by multiple organizations including SpaceX [134] and Global Information Grid [135].…”

Section: Space-air-ground Integratedmentioning

confidence: 99%

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Anjum

Anees

Tariq

et al. 2023

Applied Computational Intelligence and Soft Computing

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The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

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“…Accompanied by the increasing demand for ocean exploration and protection, underwater sensor networks (UWSNs) have received more attention as these play an important role in diverse marine applications, such as coastal monitoring and protection, marine resource exploration, disaster warning and military operations [ 1 , 2 , 3 , 4 ]. However, due to the harsh hydrographic and geographical environment, it is difficult to collect data from underwater sensor devices via a long-range routing path.…”

Section: Introductionmentioning

confidence: 99%

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Luo

et al. 2023

Sensors

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Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

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Recent Progress of Air/Water Cross-Boundary Communications for Underwater Sensor Networks: A Review

Cited by 66 publications

References 185 publications

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

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