Comparative Analysis of Coherent Routing Using Machine Learning Approach in MANET

Ayushree,

doi:10.1007/978-981-10-5544-7_72

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Under the mobile network scenario, the AODV, MEMAC have high delay but still SSMA and P-MACRON shows better performance. If we compare SSMA, MEMAC and AODV [23] as PMACRON adopts a clustering…”

Section: Environment Setup and Resultsmentioning

confidence: 99%

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Bhandari¹,

Sekhar²

2021

IJACSA

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Wireless sensor network with mobility is rapidly evolving and increasing in the recent decade. The cluster and hierarchical routing strategy demonstrates major changes in the lifespan of the network and the scalability. The latency, average energy consumption, packet distribution ratio is highly impacted due to a lack of coordination between cluster head and extreme mobile network nodes. Overall efficiency of highly mobile wireless sensor network is reduced by current techniques such as mobilityconscious media access control, sleep/wakeup scheduling and transmission of real-time services in wireless sensor network. This paper proposes a novel Priority-Mobility Aware Clustering Routing algorithm (p-MACRON) for high delivery of packets by assigning fair weightage to each and every packet of node. To automatically decide the scheduling policy, reinforcement learning approach is integrated. The mixed approach of priority and selflearning results into better utilization of energy. The experimental result shows comparisons of slotted sense multiple access protocol, AODV, MEMAC and P-MACRON, in which proposed algorithm delivered better results in terms of interval, packet size and simulation time.

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Section: Environment Setup and Resultsmentioning

confidence: 99%

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Bhandari¹,

Sekhar²

2021

IJACSA

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“…Because GPCR is the largest super family among all the receptors in cell biology and has much functionality such as cell signaling, drug targeting etc. We know that it is an emerging area of research so many researchers have implemented varieties of algorithms upon it like support vector machine, naive Bayes, neural network, fuzzy c-means [21][22][23][24][25][26][27][28][29][30] etc. Therefore we have concentrated our experiment on GPCR along with membrane cholesterol which is an innovative idea.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Plasma Membrane Cholesterol from 7-Transmembrane Receptor Using Hybrid Machine Learning Algorithm

Nayak

Tripathy²,

Mohapatra

et al. 2022

Int. j. electr. comput. eng. syst. (Online)

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The researches have been made on G-protein coupled receptors (GPCRs) over the long-ago decades. GPCR is also named as 7-transmembrane (7TM) receptor. According to biological prospective GPCRs consist of large protein family with respective subfamilies and are mediated by different physiological phenomena like taste, smell, vision etc. The main functionality of these 7TM receptors is signal transduction among various cells. In human genome, cell membrane plays significant role. All cells are made up of trillion of cells and have dissimilar functionality. Cell membrane composed of different components. GPCRs are reported to be modulated by membrane cholesterol by interacting with cholesterol recognition amino acid consensus (L/V-X (1-5)-Y-X (1-5)-R/K) (CRAC) or reverse orientation of CRAC (R/K-X (1-5)-Y-X (1-5)-L/V) (CARC) motifs present in the TM helices. Among all, cholesterol is one who is regulated by membrane proteins. Here we took GPCR as membrane proteins and this protein modulates membrane cholesterol. According to cell biology, GPCR regulates a wide diversity of vital cellular processes and are targeted by a huge fraction of approved drugs. In this paper we have concentrated our investigation on membrane protein with membrane cholesterol. A hybrid algorithm consisting of spectral clustering and support vector machine is proposed for prediction of membrane cholesterol with GPCR. Spectral clustering uses graph nodes for calculating the cluster points and also it considers other concept such as similarity matrix, low-dimensional space for projecting the data points and upon this parameter at last construct the cluster centre. Supervised learning method is used for solving regression and classification problems. From the analysis we found that our result shows better prediction accuracy in terms of time complexity when compared with two existing models such as fuzzy c-means (FCM) and rough set with FCM model

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“…Energy Efficiency or energy conservation and network life time are the key parameters for the cooperative routing algorithms. Several routing algorithms have been proposed recently to enhance the energy efficiency and lifetime of the cooperative wireless networks [14][15][16]. Authors in [17] have been adopted bio-inspired algorithms to obtain optimal cooperative routing path.…”

Section: Introductionmentioning

confidence: 99%

Large Scale Cooperative Wireless Networks: Relay Selection and Performance Analysis

Devulapalli¹

2020

IJATCSE

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This paper presents a proficient relay selection criterion for large-scale cooperative wireless networks (LSCWN) under Rayleigh fading, where each node in the network employs an Amplify and Forward (AF) relay protocol. We obtain analytic expression for Probability Density Function (pdf) and Cumulative Density Function (CDF) of end-to-end instantaneous signal-to-noise ratio and obtained minimized expression for the Moment Generating Function (MGF). The performance of the proposed relay selection criteria is analyzed and simulated. Simulation results shows an improvement in outage probability and symbol error rate by 63.88% and 84.89%, respectively, when the number of hops are increased from 1 to 3.

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Comparative Analysis of Coherent Routing Using Machine Learning Approach in MANET

Cited by 7 publications

References 9 publications

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Prediction of Plasma Membrane Cholesterol from 7-Transmembrane Receptor Using Hybrid Machine Learning Algorithm

Large Scale Cooperative Wireless Networks: Relay Selection and Performance Analysis

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