Real-time reliable clustering and secure transmission scheme for QoS development in MANET

Muruganandam, S.; Renjit, J. Arokia

doi:10.1007/s12083-021-01175-6

Cited by 16 publications

(5 citation statements)

References 26 publications

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“…This section provides an overview of the relevant literature on the proposed GKCA-LFP. An efficient method called real-time reliable clustering and secure transmission (RRCST) was developed for MANETs [12]. First, the nodes were clustered, and the CHs were selected based on their previous forwarding nature and node information.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Link Failure Prediction in Cluster Based Routing Protocol for MANETs

2024

IJIES

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The mobile adhoc network (MANET) has attracted considerable attention from researchers due to its dynamic and versatile nature. Graph clustering algorithms can be more effective than optimization algorithms in modeling and analyzing networks because these algorithms arrange nodes into clusters based on their connectivity. According to this context, a graph kernel-based clustering algorithm (GKCA) was developed for MANETs by combining the d-hop graph kernel and clustering scheme. Additionally, it uses the shortest route to connect multiple cluster head (CH) nodes for data transfer. MANETs face challenges such as changes in network structure and disruptions in communication links, which result in an increase in route discovery requests and longer mean end-toend delays (MED) due to longer link reconnect times. Hence, this article proposes the GKCA with link failure prediction (GKCA-LFP) in MANETs to prevent path failures resulting from node mobility. The GKCA is initially used to determine the cluster size and CH nodes. The shortest route is used to connect the CHs for data transfer. Then, the LFP strategy is introduced at this stage to maintain the path. This strategy aims to predict the current link status based on mobility and position information to prevent failure conditions and minimize packet loss ratio (PLR). The GKCA-LFP algorithm can choose more stable shortest paths to connect CHs for data transfer, resulting in decreased MED and PLR. The extensive simulations show that the GKCA-LFP algorithm outperforms the GKCA, AMAC, MARP-HO, and RS-GG algorithms in MANETs. Specifically, for 100 nodes, the GKCA-LFP algorithm achieves a 1.4% control packet ratio (CPR), 0.8% PLR, and 455µs MED. Additionally, for nodes with a mobility speed of 20m/s, the GKCA-LFP algorithm achieves a 1.2% CPR, 2.3% PLR, and 120µs MED.

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Section: Literature Surveymentioning

confidence: 99%

A Novel Link Failure Prediction in Cluster Based Routing Protocol for MANETs

2024

IJIES

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“…Muruganandam et al. [38] proposed an efficient Real‐time Reliable Clustering and Secure Transmission (RRCST) mechanism, which selected the route path based on the clustering of previous transmission behavior and node statistics.…”

Section: Related Workmentioning

confidence: 99%

“…Ullah et al [37] proposed a new Energy-efficient and Reliable Routing Scheme (ERRS) based on adaptive static clustering routing, which could enhance the stability period and reliability for resource-constrained by the forwarder node selection and forwarder node rotation methods. Muruganandam et al [38] proposed an efficient Real-time Reliable Clustering and Secure Transmission (RRCST) mechanism, which selected the route path based on the clustering of previous transmission behavior and node statistics.…”

Section: Routing Reliabilitymentioning

confidence: 99%

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

Wang

et al. 2022

IET Communications

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Best-effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning-based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win-win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

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“…The LEACH protocol has seen extensive application in MANETs as a means of addressing the difficulties associated with energy efficiency in CH selection. By taking turns as CH, LEACH attempts to reduce energy usage at individual nodes and extend the lifespan of the network [13]. However, there may be barriers to obtaining maximum energy efficiency using the standard LEACH method [14].…”

Section: Introductionmentioning

confidence: 99%

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

Devarayasamudram,

Chandrashekar,

Chetla

et al. 2024

MMEP

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Quality of Service (QoS) is a crucial aspect of Mobile Ad Hoc Networks (MANET) that needs examination to demonstrate optimal performance. The scientific community is increasingly concerned with the challenge of developing an energy-aware clustering method for MANETs. This is owing to the fact that the battery-operated sensor devices that form the backbone of these wireless networks cannot be recharged. The selection of a cluster's leader is a difficult problem in MANET. Additionally, the research focuses on Optimal Route Selection (ORS) within the MANET context, acknowledging the significance of establishing efficient communication paths between cluster heads and member nodes. Through the integration of a reliability pair factor and node energy considerations, the proposed ORS algorithm generates optimal paths based on maximizing energy efficiency while minimizing the sum of hops between nodes. The study suggests a new algorithm based on the way waterwheel plants move and change their places as they explore and exploit new territory in the quest for food. The suggested method is named Binary Waterwheel Plant Algorithm (BWPA). In this method, a novel model is used to represent both the binary search space and the mapping from continuous to discrete spaces. Particularly, mathematical models of the fitness and cost functions used by the algorithm are constructed. Through the use of a reliability pair factor and node energy, the proposed research paper on Optimal Route Selection (ORS) generates the optimal path between the cluster head and member node and establishes the path based on the maximum energy and sum of hops among the nodes. By fusing the reactive power of differential evolution with the exhaustive search efficiency of the BWPA, the proposed method extends the life of networks. The recommended method increases node lifetime by compounding the dynamic capabilities of discrepancy evolution with the high effectiveness of search.

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Real-time reliable clustering and secure transmission scheme for QoS development in MANET

Cited by 16 publications

References 26 publications

A Novel Link Failure Prediction in Cluster Based Routing Protocol for MANETs

A Novel Link Failure Prediction in Cluster Based Routing Protocol for MANETs

An efficient and reliable service customized routing mechanism based on deep learning in IPv6 network

An Energy-Aware Cluster Head Selection and Optimal Route Selection Algorithm for Maximizing Network Lifetime in MANETs

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