Energy-efficient multipath routing in networking aid of clustering with OGFSO algorithm

Rajashanthi, Mariappan; Valarmathi, K.

doi:10.1007/s00500-020-04710-4

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…Action recognition, as a basic part of behavior analysis, is a classification study of action category recognition at the action level. Human motion can be abstracted at different levels, so as to study human motion according to different levels [ 10 , 11 ]. DNN has the ability of representation learning, and it has better performance than traditional computer vision technology and expert system in the fields of target detection and recognition, natural language processing, and so on.…”

Section: Introductionmentioning

confidence: 99%

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Zhong

Dong

2022

Computational Intelligence and Neuroscience

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This paper constructs a sports action recognition model based on deep learning (DL) and clustering extraction algorithm. For the input detection image frame, athletes’ movements are detected through DL network, and then athletes’ sports movements are fused. Moreover, it expands new knowledge and improves learning ability through automatic learning training set. The neural network (NN) is applied to the sample set containing images of nonathletes, and the negative training sample set is iteratively enhanced according to the generated false positives, and the results are optimized by clustering method. Simulation experiments show that compared with other algorithms, the clustering extraction algorithm in this paper has achieved superior performance in recognition rate and false alarm rate, and the recognition speed is faster. The aim is to extract the athletes’ training postures through the analysis of sports movements, so as to assist coaches to train athletes more professionally and provide some reference for sports movement recognition.

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Section: Introductionmentioning

confidence: 99%

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Zhong

Dong

2022

Computational Intelligence and Neuroscience

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“…M. Rajashanthi and K. Valarmathi [19], have proposed a work which considers an issue of energy consumption in MANETs. In their work, nodes in the network are gathered by the K-medoid bunching calculation, which limits the expense of steering of information in populated networks.…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Stable Multipath Disjoint Routing Based on Accumulated Trust Value in MANETs

Benakappa¹,

Kiran²

2022

IJCNIS

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Conserving energy and finding the stable path are the two vital issues in Mobile Ad Hoc Networks (MANETs) as the prior increases the network lifetime and the later increases the network throughput. The nodes which are not legitimate in terms of residual energy and packet forwarding history might be a threat to the path thereby making the path unstable. Thus, it results in frequent link failure, reduced throughput, reduced network and path life time. In order to reduce these hazards, authors have proposed an energy efficient, reliable path selecting protocol referred to as Trust Based Energy Aware Multipath Disjoint Routing Protocol (TEA-MDRP) for MANETS in this article. TEA-MDRP finds the optimum route between the source and the destination nodes using two parameters namely, the Accumulated Trust Value (ATV) and the node’s residual energy (N_res). ATV is calculated based on the packet forwarding status of the node which shows how good the node is in terms of packet forwarding point of view. TEA-MDRP allows only the nodes which have a good ATV and sufficient residual energy. A good ATV shows loyalty in forwarding the packets while a sufficiently large residual energy node avoids frequent path breakups and packet drops. Thus, the TEA-MDRP not only increases the network and path lifetime but also increases the throughput of the communication. Further, with the legitimate nodes in the paths, the TEA-MDRP considerably reduces the control packet overhead which might occur because of the frequent route re-discovery process. An extensive simulation is carried out using Network Simulator-2.35 for the quantitative and qualitative analysis of TEA-MDRP. The results obtained are compared with classical AOMDV and the results are satisfactory.

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“…Rajasthani et al [11] introduced an energy-aware optimized link state routing (EA-OLSR) protocol using clustering with the OGFSO algorithm. This proposed work addressed the energy consumption issues in MANET.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Route Optimization Using Ticket-ID Based Routing Management System (T-ID BRM)

Venkatasubramanian¹,

Suhasini

Vennila³

2021

Wireless Pers Commun

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MANET -Mobile Ad Hoc Network is the connection of several remote mobile nodes. These networks are dynamic and independent to move anywhere. It does not contain any central controller, and hence it is stated as a structure less network. MANET is one of the most emerging technologies getting popular in recent days. It's significant features, and real-time issues grab the research community's attention towards it. The unstable data transmission will leverage the overall network performance, and designing an energy-efficient routing protocol is challenging. The main reason for this problem is the lack of a stable multipath routing system and resource constraints. Various research works have launched several efficient routing mechanisms, but the need for advancement still exists. In this paper, we propose a Ticket -ID Based Routing management system for enabling reliable routing for the entire network. The proposed system work under the TID principle, which executes according to the node properties and routing maintenance system. This system works under the supervision of TID-Routing manger. The TID routing manager is responsible for managing the ticketing pool and allocating unique ticket-ID based on the collected node factors such as energy, node location, speed, etc. The proposed routing system facilitates the shortest path for reliable communication. An experimental work using NS-simulator is done with the proposed method. The proposed work's efficiency is determined using a comparison work between T-ID BRM with TABRP, OGFSO, and PDMR. The observation states that the proposed Ticket-ID system achieves 94% better than other existing methods.

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Energy-efficient multipath routing in networking aid of clustering with OGFSO algorithm

Cited by 20 publications

References 24 publications

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm

Energy Aware Stable Multipath Disjoint Routing Based on Accumulated Trust Value in MANETs

An Efficient Route Optimization Using Ticket-ID Based Routing Management System (T-ID BRM)

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