Underwater sensor networks: ‘Comparative analysis on applications, deployment and routing techniques’

Gola, Kamal Kumar; Gupta, Bhumika

doi:10.1049/iet-com.2019.1171

Cited by 25 publications

(13 citation statements)

References 71 publications

(61 reference statements)

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“…Similarly, energy optimization of underwater sensor network is explored recently through depth-based routing [ 35 ], clustering scheme [ 36 ], machine learning technique [ 37 ], and reinforcement learning [ 38 ]. A comparative study on the applications, deployment, and routing techniques in UWSNs is also carried out in [ 39 ].…”

Section: Related Workmentioning

confidence: 99%

Investigating Master–Slave Architecture for Underwater Wireless Sensor Network

Jan

Yafi

Hafeez

et al. 2021

Sensors

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A significant increase has been observed in the use of Underwater Wireless Sensor Networks (UWSNs) over the last few decades. However, there exist several associated challenges with UWSNs, mainly due to the nodes’ mobility, increased propagation delay, limited bandwidth, packet duplication, void holes, and Doppler/multi-path effects. To address these challenges, we propose a protocol named “An Efficient Routing Protocol based on Master–Slave Architecture for Underwater Wireless Sensor Network (ERPMSA-UWSN)” that significantly contributes to optimizing energy consumption and data packet’s long-term survival. We adopt an innovative approach based on the master–slave architecture, which results in limiting the forwarders of the data packet by restricting the transmission through master nodes only. In this protocol, we suppress nodes from data packet reception except the master nodes. We perform extensive simulation and demonstrate that our proposed protocol is delay-tolerant and energy-efficient. We achieve an improvement of 13% on energy tax and 4.8% on Packet Delivery Ratio (PDR), over the state-of-the-art protocol.

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Section: Related Workmentioning

confidence: 99%

Investigating Master–Slave Architecture for Underwater Wireless Sensor Network

Jan

Yafi

Hafeez

et al. 2021

Sensors

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“…Let T = F i=1 N n=1 ∆t i [n] denote the mission time which defines the total travel time starting from the first sensor node S 1 and returning to it after completing the mission. [1]] denote the AUV trajectory. Accordingly, we can express the optimization problem as…”

Section: Problem Formulationmentioning

confidence: 99%

“…Underwater sensor networks (USNs) have been increasingly deployed in various maritime applications including pollution monitoring, tsunami warnings, underwater oil field detection, and valuable minerals explorations among others [1]. Autonomous underwater vehicles (AUVs) are particularly instrumental in USNs to retrieve data from sensor nodes [2].…”

Section: Introductionmentioning

confidence: 99%

AUV Trajectory Optimization for an Optical Underwater Sensor Network in the Presence of Ocean Currents

Mahmoodi

Uysal

2021

2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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Autonomous underwater vehicles (AUVs) are instrumental for data offloading in underwater sensor networks (USNs). With high data rate capacity at transmission ranges in the order of several tens of meters, visible light communication (VLC) is well-positioned to serve as a wireless link between the AUV and sensor nodes. In this paper, we consider a USN network where an AUV is used for data retrieval from the sensors through VLC link. We formulate the design of optimal AUV trajectory as an optimization problem to minimize the AUV energy consumption under data rate constraints imposed by the VLC link and in the presence of ocean currents. Our numerical results demonstrate that our proposed trajectory is reactive to ocean currents and brings significant reductions in energy consumption and mission time of the AUVs, in particular for USN scenarios with a large number of sensor nodes.

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“…In lowered condition typical resources are inspected by unmanned underwater vehicles (UUVs) and autonomous underwater vehicles (AUVs). Because of astoundingly deferred in speed, acoustic wave lowered, and huge number of multipath, there is long inspiration response and colossal time delays [4,5,6]. Acoustic correspondence is a critical advancement to exchange information lowered, since acoustic signs, instead of optic and radio signs, can spread in water for long reach detachments.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Optimal Path Routing (EEOPR) for Void Avoidance in Underwater Acoustic Sensor Networks

Singh¹,

Gupta²

2022

IJCNIS

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UASN (Underwater Acoustic Sensor Network) has intrinsic impediments, since it is utilized and utilizes acoustic signs to impart in the sea-going world. Examples include long delays in propagation, limited bandwidth, high transmitting energy costs, very high attenuation in the signal, expensive implementation and battery replacement etc. The UASN routing schemes must therefore take account of these features to achieve balance energy, prevent void hole and boost network life. One of the significant issue in routing is the presence of void node. A void node is a node that does not have any forwarder node. The presence of void may cause the bundle conveyance in the steering time which prompts information misfortune. The gap during steering influences the network execution regarding proliferation delay, vitality utilization and network lifetime, and so forth. So with the objective to remove the void node in the networks, this work presents an energy efficient optimal path routing for void avoidance in underwater acoustic sensor networks. This work uses the concept of gray wolf optimization algorithm to calculate the fitness function and that fitness function is used to select the best forwarder node in the networks. This work only consider the vertical directions which further reduces the end to end delay. The proposed work has been simulated on MATLAB and performances are evaluated in terms of broadcast copies of data, energy tax, and packet delivery ratio, number of dead nodes, network lifetime and delay.

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Underwater sensor networks: ‘Comparative analysis on applications, deployment and routing techniques’

Cited by 25 publications

References 71 publications

Investigating Master–Slave Architecture for Underwater Wireless Sensor Network

Investigating Master–Slave Architecture for Underwater Wireless Sensor Network

AUV Trajectory Optimization for an Optical Underwater Sensor Network in the Presence of Ocean Currents

An Energy Efficient Optimal Path Routing (EEOPR) for Void Avoidance in Underwater Acoustic Sensor Networks

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