A MAC Protocol of Concurrent Scheduling Based on Spatial‐Temporal Uncertainty for Underwater Sensor Networks

Liu, Xin; Du, Xiujuan; Li, Meiju; Wang, Lijuan; Li, Chong

doi:10.1155/2021/5558078

Cited by 10 publications

(4 citation statements)

References 43 publications

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“…As we already know that underwater speed of acoustical signals is 1.5 × 10 3 (m/s) and having propagation delay of 5 times more, when compared with the terrestrial radio frequency (RF) communication [53]. This high propagation delay in UWSNs is basically the combination of different factors, i.e., attenuation due to reflection/refraction of the acoustical signals from unpredicted seabed and the surface of water [55].…”

Section: Variabiity In Propogation Delaysmentioning

confidence: 99%

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A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

et al. 2022

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Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

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Section: Variabiity In Propogation Delaysmentioning

confidence: 99%

“…Another MAC energy-efficient protocol is created on a tone based booking technique [53]. The sender node communicates a small tone as well as hears the channel during a dispute cycle.…”

Section: (Xiv) Tone-lohi (T-lohi)mentioning

confidence: 99%

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

et al. 2022

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“…Although USNet is a type of wireless sensor network, it is different from the terrestrial wireless sensor networks in which the sensor nodes rely on radio waves to communicate with one another. The underwater sensor nodes use acoustic channels as the communication medium, with a speed of 1500 m/s, which is five orders of magnitude lower than that of radio signals [1]. USNet is characterized by a narrow bandwidth, long delay, high bit error, and limited energy resources and computing power [2,3], bringing about great challenges to the communication over underwater acoustic channels [4].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Computing Offloading Scheme via Artificial Neural Networks for Underwater Sensor Networks

Liu,

Du,

Zhang

et al. 2023

Applied Sciences

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Aiming at the problem of being unable to meet some high computing power, high-precision applications due to the limited capacity of underwater sensor nodes, and the difficulty of low computation power, in this paper, we introduce the edge servers, known as base stations for underwater sensor nodes, and propose a scheme to process the computational tasks based on coalition game theory. This scheme provides functions such as cooperation among different base stations within the coalition, the smart division of tasks, and efficient computational offloading. In order to reduce the complexity of the algorithm, the artificial neural network model is introduced into the method. Each task is divided into sub-parts and fed to an artificial neural network for training, testing, and validation. In addition, the scheme delivers the computed task from base stations back to sink nodes via a shortened path to enhance the service reliability. Due to the mobility of the base station in the ocean, our proposed scheme takes into account the dynamic environment at the same time. The simulation results show that, compared with the existing state-of-the-art methods, the success rate of our proposed approach improves by 30% compared with the Greedy method. The total service time of our proposed approach decreases by 12.6% compared with the Greedy method and 31.2% compared with the Always-Migrate method.

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“…This results in the establishment of a species' habitat, the expansion of the habitat's size, or the extinction of the species in that location. Threatened and endangered species are typically environment specialists and comparably extraordinary individuals; as a result, they are likely to be disproportionately harmed by climate change [10]. The preservation of species within their current ranges may prove to be a difficult task.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Kesavan¹,

Latha²

2023

Intelligent Automation &Amp; Soft Computing

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Climate change is the most serious causes and has a direct impact on biodiversity. According to the world's biodiversity conservation organization, reptile species are most affected since their biological and ecological qualities are directly linked to climate. Due to a lack of time frame in existing works, conservation adoption affects the performance of existing works. The proposed research presents a knowledge-driven Decision Support System (DSS) including the assisted translocation to adapt to future climate change to conserving from its extinction. The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species. However, the framework demonstrates the huge difference in the estimated significance of climate change, the model strategy helps to recognize the probable risk of threatened species translocation to future climate change. The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

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A MAC Protocol of Concurrent Scheduling Based on Spatial‐Temporal Uncertainty for Underwater Sensor Networks

Cited by 10 publications

References 43 publications

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

Cooperative Computing Offloading Scheme via Artificial Neural Networks for Underwater Sensor Networks

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

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