AN ENERGY EFFICIENT QUALITY OF SERVICE (QoS) PARAMETERS BASED VOID AVOIDANCE ROUTING TECHNIQUE FOR UNDERWATER SENSOR NETWORKS

Gola, Kamal Kumar; Gupta, Bhumika

doi:10.5455/jjcit.71-1562930035

Cited by 4 publications

(4 citation statements)

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“…The authors suggested a void node routing technique for the acoustic networks underwater in [19]. The QoS parameters are used for this work: depth knowledge, time hold, residual energy and distance to the next node.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…These sensors are based at various water depths and have a tiny size, storage requirements, connectivity, and power. UASNs are sensor nodes capable of detecting, interpreting, and transmitting detected data to the appropriate underwater destination to enable various uses in collaborative monitoring activities 5,6 . and the Underwater Acoustic sensor network (UASN) structure are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have lately concentrated on approaches to build different submerged technologies to assist the existence of biological organisms underwater and those surviving on the sea bed via interactive, collective, and frequent observation of aquatic surroundings 7 . Numerous applications are being explored, including monitoring of the atmosphere and smog, 8,9 gathering marine data, 5 dispersed surveillance, 10 marine sampling, 11 natural hazard control, 5,8 and deep water investigation 5 . However, several demanding problems, such as excessive power utilization, lack of communication, privacy, navigation, and collaboration, cause the UASN's performance to deteriorate 8,12,13 …”

Section: Introductionmentioning

confidence: 99%

“…7 Numerous applications are being explored, including monitoring of the atmosphere and smog, 8,9 gathering marine data, 5 dispersed surveillance, 10 marine sampling, 11 natural hazard control, 5,8 and deep water investigation. 5 However, several demanding problems, such as excessive power utilization, lack of communication, privacy, navigation, and collaboration, cause the UASN's performance to deteriorate. 8,12,13 Although UWSNs have grown significantly in the universal flea market, a diverse collection of prerequisites has resulted in urgent constraints on network job briefing and sensor node abilities in the examined area.…”

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confidence: 99%

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Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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Acoustic Sensor Networks: An Energy Efficient Grey Wolf Optimization Algorithm development for underwater networks

Vanitha

Barve

Singh³

et al. 2022

2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)

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AN ENERGY EFFICIENT QUALITY OF SERVICE (QoS) PARAMETERS BASED VOID AVOIDANCE ROUTING TECHNIQUE FOR UNDERWATER SENSOR NETWORKS

Cited by 4 publications

References 0 publications

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

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

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Acoustic Sensor Networks: An Energy Efficient Grey Wolf Optimization Algorithm development for underwater networks

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