An Energy-Aware Multilayer Clustering-Based Butterfly Optimization Routing for Underwater Wireless Sensor Networks

Chenthil, T. R.; Jayarin, P. Jesu

doi:10.1007/s11277-021-09042-6

Cited by 17 publications

(7 citation statements)

References 23 publications

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“…The proposed protocol adjusts cluster size proportionally to node density. Chenthil et al [23] contribute a multilayer clustering routing algorithm for UWSN, enhancing CH selection mechanisms and overall network energy efficiency. The butterfly optimization algorithm is employed to prevent local optima and improve solution convergence, leading to an increased packet delivery ratio and reduced transmission loss.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN

Chauhan,

Sharma,

Sharma

et al. 2024

Preprint

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The utilization of Free-Space Optical (FSO) communication in underwater environments is gaining momentum, particularly in Software Defined Underwater Wireless Sensor Networks(SDUWSNs). However, challenges such as high-energy loss and limited propagation distance persist in data transmission within SDUWSNs. In addressing these issues, this study introduces an innovative approach known as Software Defined Free Space Optical Underwater Wireless Sensor Networks, where FSO communication is seamlessly integrated with SDUWSNs to enhance network longevity. To optimize the performance of SD-FSO-UWSNs, the implementation of clustering and routing is explored as an effective strategy for energy-efficient data delivery. Nevertheless, the selection of optimal control nodes (CNs) in clustering poses a significant challenge. In response, a novel self-adaptive cheetah optimization-based clustering approach (SACO-CA) is proposed by incorporating self-adaptive inertia weights to identify optimal CNs based on a devised fitness value. The fitness function considers important parameters such as energy levels and distances among network devices, aiming to balance cluster sizes effectively. Moreover, the NS3 simulator is used to run network simulation while, SDN policies are implemented through the Open Network Oper-ating System (ONOS) controller. The simulation result metrics, including stability period,alive nodes, average residual energy, packets transmitted to the control server, and averagedelay, indicate that SACO-CA outperforms existing state-of-the-art methods. The results underscore the efficacy of the nature-inspired SACO-CA approach in optimizing CNs and improving overall network performance in SD-FSO-UWSNs.

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Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN

Chauhan,

Sharma,

Sharma

et al. 2024

Preprint

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“…To address communication voids, EEUCP utilizes periodic fuzzy trust values for reliable relay forwarding, thereby achieving the goals of extending the network lifetime and improving network quality of service. In [31], an energy-aware multilayer clustering-based butterfly optimization routing (MCBOR) is proposed. MCBOR selects cluster heads based on residual energy, communication overhead, node coverage range, and processing energy consumption to reduce energy consumption and extend the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Mei,

Wang,

Shen

et al. 2024

JMSE

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Clustering protocols for underwater acoustic sensor networks (UASNs) have gained widespread attention due to their importance in reducing network complexity. Congestion occurs when the intra-cluster load is greater than the upper limit of the intra-cluster information transmission capacity, which leads to a dramatic deterioration of network performance despite the reduction of network complexity. To avoid congestion, we propose a node load and location-based clustering protocol for UASNs (LLCP). First, a node load and location-based optimization mechanism is proposed. The number of cluster members is optimized based on node load and location to maximize the number of cluster members while avoiding congestion. Then, a node degree and location-based cluster member selection mechanism is proposed to select the optimal cluster members. Finally, a priority-based clustering mechanism is proposed. The node clustering order is adjusted based on the clustering priority to maximize the reduction of network complexity by increasing the average number of cluster members. Simulation results show that our proposed LLCP minimizes the network complexity while avoiding congestion.

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“…Chenthil and Jesu Jayarin, [16] presented the multilayer clustering-based butterfly optimization routing (MCBOR) for broadcasting the data to the receiver without loss. The list of parameters such as energy, node coverage, processing power and communication cost were utilized to choose the candidate CH.…”

Section: Related Workmentioning

confidence: 99%

Multi Objective Energy Based Improved Jellyfish Swarm Optimization for Effective Cluster Head Discovery in UWSN

2023

IJIES

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Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN.

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An Energy-Aware Multilayer Clustering-Based Butterfly Optimization Routing for Underwater Wireless Sensor Networks

Cited by 17 publications

References 23 publications

Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN

Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Multi Objective Energy Based Improved Jellyfish Swarm Optimization for Effective Cluster Head Discovery in UWSN

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