Multilink and AUV-Assisted Energy-Efficient Underwater Emergency Communications

Huang, Zhengrui; Wang, Shujie

doi:10.1109/jiot.2022.3230322

Cited by 5 publications

(4 citation statements)

References 44 publications

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“…These energy-related limitations impact the lifetime and operational capabilities of the sensors. Moreover, signal-to-interference-plus-noise ratios (SINRs) and outage probabilities, as highlighted in the work [34], play a critical role in maintaining reliable communication within the sensor network. AMVs' physical system poses distinct constraints to the path-planning problem.…”

Section: Constraintsmentioning

confidence: 99%

“…Communication distance is a vital constraint that dictates the range over which AMVs can effectively communicate with other nodes or the central control station [45]. Also, the constraint on maximum diving depth and the number of permissible turning points was introduced in [34,35], respectively. The constraint of energy consumption during data packet transmission or reception is also considered [41].…”

Section: Constraintsmentioning

confidence: 99%

“…Data collection time/Collection delay [26,33,34]: Collection delay is one of the most important optimization terms. In contrast to electromagnetic waves, the conveyance of underwater signals predominantly relies upon acoustic waves.…”

mentioning

confidence: 99%

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Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Zhao,

Bai

2024

JMSE

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Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems.

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Section: Constraintsmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

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Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Zhao,

Bai

2024

JMSE

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“…The researchers choose the best emergency response mode (ERM) for each underwater sensor node employing greedy searching and reinforcement learning to identify the ''isolated'' USNs (IUSN) after which IUSNs may be identified. Multiobjective optimization achieves the best tradeoff between response efficiency and energy consumption [51].…”

Section: Indoor Thingsmentioning

confidence: 99%

Indexing in WoT to Locate Indoor Things

et al. 2023

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The Web of Things (WoT) is an enhanced form of the Internet of Things (IoT) that has changed the trend of life nowadays. Due to IoT, life is transformed into smart life, such as smart buildings, smart vehicles, smart agriculture, smart businesses, etc., by connecting a certain number of things to the internet. Many people are now working on ways to locate indoor things to interact and exchange data between smart things and web services and apps, which is called ''WoT,'' or ''Web of Things.'' To interact and exchange the data, researchers need a search engine on WoT. However, locating indoor things in the Web of Things (WoT) remains a challenge due to the lack of a unified indexing system. In this research, we propose a novel approach to index indoor things in the WoT by leveraging machine learning and web technologies. Our approach includes a data preprocessing step, where we extract relevant features from the sensor data, followed by a clustering algorithm to group similar devices. We then use a semantic model to assign meaning to the clusters and develop a search engine to enable efficient searching of indoor things. Our proposed approach improves the accuracy and efficiency of locating indoor things in the WoT, paving the way for new applications in smart homes, healthcare, and industrial automation.

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