Clustering Algorithms for MANETs: A Review on Design and Development

Pathak, Sunil; Jain, Sonal; Borah, Samarjeet

doi:10.1007/978-981-15-7394-1_51

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…Walikar and Biradar [69] proposed Energy Aware Multicast Routing Protocol (EAMRP) utilizing number of adjacent nodes and remaining energy to make stable connections. Borah et al proposed clustering algorithms dependent on IDs, degree, residual energy, movement, and weight of node [70]. A futuristic cooperation evaluation model (FUCEM) also stresses on effect of residual energy and mobility of nodes on link stability [71].…”

Section: Imparting Link Stability Using a Combination Of Factorsmentioning

confidence: 99%

A Brief Review on Parameters of Link Stability in MANETs

Pareek

Tharewal

2021

Algorithms for Intelligent Systems

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Section: Imparting Link Stability Using a Combination Of Factorsmentioning

confidence: 99%

A Brief Review on Parameters of Link Stability in MANETs

Pareek

Tharewal

2021

Algorithms for Intelligent Systems

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“…Although a large number of approaches exploit benefit of clustering for the most recent MANET contexts because of its advantages resulting in high level network managing [14] it provides several issues to be addressed; for example, hierarchical strategies could involve significant routing overhead [15] while deterministic approaches could lead to scalability troubles and use of specific cluster shapes [16,17]. Advancements in deep learning technologies, specifically Graph Neural Networks (GNN), offer a robust alternative to traditional clustering techniques.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

Inzillo¹,

Garompolo²

2023

Preprint

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The rapid advancement of wireless technologies and the increasing demand for seamless, high-speed connectivity has spurred the need for efficient routing protocols in 6G Mobile Ad Hoc Networks (MANETs). This paper proposes a modern approach, the Deep Learning Clustering Beamforming Massive MIMO Routing Protocol (DLCB), that is designed to achieve high level throughput by exploiting beamforming advantages and for enhancing communication efficiency for future next generation 6G MANETs. The DLCB algorithm establishes robust and efficient routes for data transmission by adopting a clustering strategy based on deep learning allowing to select cluster heads by taking advantage of Graph Neural Networks (GNN) features and creating effective cluster paths. Furthermore, this approach integrates IEEE 802.11ay and Massive MIMO technologies to exploit their benefits in terms of high-speed data transmission, improved network capacity, and better signal quality. DLCB aims to facilitate seamless data transmission in dynamic network environments, paving the way for enhanced network performance in 6G MANETs. Experimental evaluations and simulations are conducted to validate the performance of the proposed approach through the use of Omnet++ Network Simulator.

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“…As a result, to improve network speed and endurance, these limited resources must be used very efficiently. Otherwise, unbiased traffic and resource usage could quickly clog the network and deplete energy [4]. The majority of the inefficiency in wireless networks can be ascribed to load imbalance.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Cluster Subleader to Balance the Load of a Cluster Head to Achieve Qos in Manets

Sowmya¹,

Cauvery²,

Sharvani³

2022

INDJCSE

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In Mobile Ad hoc Networks (MANET), load balancing is considered one of the critical issues. Nowadays, load balancing is introduced to relieve the central nodes by shifting traffic away from the node. Many conventional routing protocols that have been designed, but cannot provide load balancing. Leader election is a major problem in wired networks as well as mobile ad hoc networks. Till now, several approaches are developed for the design and implementation of load balancing, but, the existing leader election techniques are unable to cope with frequent topology changes and the dynamic nature of mobile networks. In this research, Clustered ANTHOCNET Subleader, a leader election procedure is proposed, that is highly flexible to random topological changes and compatible for usage in MANET. The approach is centered on the discovery of extrema and the use of diffusing calculations to simulate the algorithm in a MANET environment. This study is carried out in MANETs by generating clusters and appointing a Cluster Leader (CL) using the Weighted Clustering Algorithm (WCA), as well as transferring various clusters utilizing Ant Colony Optimization (ACO). The numerical simulations, clearly show that the proposed Clustered ANTHOCNET Subleader is used to accomplish Quality of Service (QoS) and stability when compared to Ad Hoc On-Demand Distance Vector (AODV) in terms of Packet Delivery Ratio (95.4%), Throughput (3991.54 bits/second), Delay (0.088s) and Jitter (0.058s) under random mobility nodes.

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Clustering Algorithms for MANETs: A Review on Design and Development

Cited by 7 publications

References 46 publications

A Brief Review on Parameters of Link Stability in MANETs

A Brief Review on Parameters of Link Stability in MANETs

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

Implementation of Cluster Subleader to Balance the Load of a Cluster Head to Achieve Qos in Manets

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