Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

Khan, Abdul Hakim; Imran, Muhammad; Alharbi, Abdulaziz; Mohamed, Ehab Mahmoud; Fouda, Mostafa M.

doi:10.1109/access.2022.3230600

Cited by 7 publications

(7 citation statements)

References 100 publications

(145 reference statements)

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“…259 Based on these nanogenerators, underwater acoustic sensors have been designed due to the potential capabilities such as earthquake prediction and acquisition of seismic data, study of marine life, oil spill detection, finding the wreckage of crashed planes, etc. 260 Acceleration Sensors. Piezoelectric acceleration sensors are tools for detecting acceleration and are used for human activity/behavior recognition, control and monitoring of faults and deviations, robotics/soft robotics, smart buildings, etc.…”

Section: Sensorsmentioning

confidence: 99%

“…Research in the field of underwater nanogenerators, especially acoustic transducers, has continued since the mid-1900s . Based on these nanogenerators, underwater acoustic sensors have been designed due to the potential capabilities such as earthquake prediction and acquisition of seismic data, study of marine life, oil spill detection, finding the wreckage of crashed planes, etc …”

Section: Self-powered Physical/biophysical Sensorsmentioning

confidence: 99%

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Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications

Farzin,

Naghib,

Rabiee

2024

ACS Biomater. Sci. Eng.

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The rapid maturation of smart city ecosystems is intimately linked to advances in the Internet of Things (IoT) and self-powered sensing technologies. Central to this evolution are battery-less sensors that are critical for applications such as continuous health monitoring through blood metabolites and vital signs, the recognition of human activity for behavioral analysis, and the operational enhancement of humanoid robots. The focus on biosensors that exploit the human body for energy-spanning wearable, attachable, and implantable variants has intensified, driven by their broad applicability in areas from underwater exploration to biomedical assays and earthquake monitoring. The heart of these sensors lies in their diverse energy harvesting mechanisms, including biofuel cells, and piezoelectric, triboelectric, and pyroelectric nanogenerators. Notwithstanding the wealth of research, the literature still lacks a holistic review that integrates the design challenges and implementation intricacies of such sensors. Our review seeks to fill this gap by thoroughly evaluating energy harvesting strategies from both material and structural perspectives and assessing their roles in powering an array of sensors for myriad uses. This exploration offers a comprehensive outlook on the state of self-powered sensing devices, tackling the nuances of their deployment and highlighting their potential to revolutionize data gathering in autonomous systems. The intent of this review is to chart the current landscape and future prospects, providing a pivotal reference point for ongoing research and innovation in selfpowered wireless sensing technologies.

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Section: Sensorsmentioning

confidence: 99%

Section: Self-powered Physical/biophysical Sensorsmentioning

confidence: 99%

Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications

Farzin,

Naghib,

Rabiee

2024

ACS Biomater. Sci. Eng.

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“…Both articles presented C-RAN as an emerging 5G network concept emphasizing Ultra Dense Network deployment and Multi-Radio Access Technologies capable of optimizing CAPEX and OPEX for the mobile network operator. In [31][32][33][34], the authors presented an overview of the role of energy harvesting technologies in 5G networks. Energy harvesting is presented as an alternative energy source to extend the life span of devices and networks.…”

Section: Review Of Existing Literaturementioning

confidence: 99%

“…Energy harvesting is presented as an alternative energy source to extend the life span of devices and networks. In [31], Muhammad et al outlined the energy harvesting process and discussed the requirements and benefits of energy harvesting technologies in 5G networks. The harvested energy is categorized and classified based on the following attributes:…”

Section: Review Of Existing Literaturementioning

confidence: 99%

Review of 5G C-RAN Resource Allocation

Chenke,

Nche,

Djomadji

et al. 2023

ICST Transactions on Mobile Communications and Applications

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The Fifth Generation (5G) Network will bring different types of services, namely: EMBB, mMTC and URLLC and as more devices connect to the network, each user device request for data capacity will continue to grow. The increase in the number of devices and capacity request for each device will require an increase in network capacity, which will also need an increase in the number of Base Stations in the network. More Base Stations will increase the the Mobile Network Operator’s capital investment and operation costs. However, this increase in CAPEX and OPEX will not provide a corresponding increase in ARPU as users tend to be less willing to pay more as their capacity request increases. Mobile vendors and Mobile Network Operators will face the challenge of providing higher capacity for the same or less ARPU for their customers to maintain their customer base and maintain the business profitability. C-RAN was identified as a new and promising paradigm to help Mobile Network Operators reduce their CAPEX and OPEX while delivering higher capacity to their customers and maintaining business profitability. How C-RAN resources are allocated and managed within the 5G network will determine how efficient and profitable this optimisation process will be. In this article, we provide a high-level review of the crucial enabling 5G Technologies and an exhaustive review of C-RAN resource allocation algorithms for 5G networks with emphasis on resource allocation metrics/parameters. The main resource allocation metrics considered in this work include BBU computational/processing resource, capacity, Power/ energy consumption, wavelength, UE-RRH mapping and RRH-BBU mapping. Furthermore, Energy Harvesting in 5G C-RAN is discussed, including its architecture, categorised taxonomy and requirements. Lastly, future research directions and open research issues for efficient C-RAN resource allocation are highlighted.

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“…and boats [8] and target/object detection [9], to mention a few. Underwater target/object detection processes signals (image, audio, video, acoustic vibrations, radio/optical radiations) and extracts the information content that provides an ultimate insight to the underwater environment.…”

mentioning

confidence: 99%

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

Khan,

Fouda,

et al. 2024

IEEE Access

Self Cite

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This paper provides a study of the latest target (object) detection algorithms for underwater wireless sensor networks (UWSNs). To ensure selection of the latest and state-of-the-art algorithms, only algorithms developed in the last seven years are taken into account that are not entirely addressed by the existing surveys. These algorithms are classified based on their architecture and methodologies of operation and their applications are described that are helpful in their selection in a diverse set of applications. The merits and demerits of the algorithms are also addressed that are helpful to improve their performance in future investigation. Moreover, a comparative analysis of the described algorithms is also given that further provides an insight to their selection in various applications and future enhancement. A depiction of the addressed algorithms in various applications based on publication count over the latest decade (2023-2013) is also given using the IEEE database that is helpful in knowing their future application trend. Finally, the challenges associated with the underwater target detection are highlighted and the future research paradigms are identified. The conducted study is helpful in providing a thorough analysis of the underwater target detection algorithms, their feasibility in various applications with future challenges and defined strategies for further investigation. INDEX TERMSUnderwater target detection, deep learning, underwater object detection, YOLO, convolutional neural networks, ConVNNs.I. INTRODUCTION Underwater wireless sensor networks (UWSNs) is one of the latest realms of research that aims to explore the underwater environment for a number of applications. These applications include Tsunami detection and prediction [1], military surveillance [2], underwater navigation [3], secure communications [4], oil detection [5], fault monitoring in underwater cables [6], water quality monitoring [7], detection of crashed ships

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Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

Cited by 7 publications

References 100 publications

Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications

Advancements in Bio-inspired Self-Powered Wireless Sensors: Materials, Mechanisms, and Biomedical Applications

Review of 5G C-RAN Resource Allocation

Underwater Target Detection Using Deep Learning: Methodologies, Challenges, Applications, and Future Evolution

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