Load Balanced Clustering Technique in MANET using Genetic Algorithms

Kaliappan, M.; E, Mariappan; Prakash, M.; Paramasivan, B.

doi:10.14429/dsj.66.9205

Cited by 12 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is up to some extent and small modifications of singular value will retain the quality of the original image. The stability of the singular values is naturally high [17]. Hence, the singular values will not be affected by the image processing attacks.…”

Section: The Proposed Schemementioning

confidence: 99%

Cost-Effective Watermarking Scheme for Authentication of Digital Fundus Images in Healthcare Data Management

Klington¹,

Ramesh

Kadry

2021

ITC

View full text Add to dashboard Cite

This paper presents a cost-effective watermarking scheme for the authentication of healthcare data management. The digital fundus images are one particular class of medical images and it is widely used for screening mass population, identifying early symptoms of various diseases in healthcare. The mass volume of such data and its management requires an effective authentication scheme, while it is exchanged on an open network. The proposed scheme uses a watermarking technique to authenticate the digital fundus images. The watermark is generated concerning the portions of the original image using Singular value decomposition (SVD) and the remaining portions are used for embedding. The embedding process uses interleaving concepts across the red and blue planes of the original images to make the number of embedding as constant. The constant number of embedding is fixed for the original size of the given image to make the scheme as computationally cost-effective. The experiment showed the maximum capacity of the proposed scheme is 329960 bits for an image of size 565x584x3. It modifies 43% of the total number of embedded pixels against jittering attacks at an average. Comparative analysis showed that the proposed scheme uses only 1/3 of the original image size for embedding by retaining good imperceptibility of 54 dB. The net performance of the proposed scheme is found to be constant and it makes a scheme as cost-effective.

show abstract

Section: The Proposed Schemementioning

confidence: 99%

Cost-Effective Watermarking Scheme for Authentication of Digital Fundus Images in Healthcare Data Management

Klington¹,

Ramesh

Kadry

2021

ITC

View full text Add to dashboard Cite

show abstract

“…In this algorithm, a Markov process model has been formed to reduce the energy consumption in a network. Load-balanced clustering technique [21,33] enhances energy-efficiency and security in ad hoc and cognitive networks. The cluster formation makes the energy-efficiency and accuracy of the channel increased using the proposed algorithm [49].…”

Section: Information Technology and Controlmentioning

confidence: 99%

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

Vimal¹,

Kalaivani²,

Kadry

et al. 2021

ITC

View full text Add to dashboard Cite

The Cognitive radio network (CR) is a widespread technology in which the Secondary users are assumed to be of the winning users to acquire the spectrum by reducing the false alarm possibilities and the false detection of the user assumed to be original user in nature is restricted with the usage of Spectrum monitoring agents. The collaborative spectrum sensing (CSS) is an approach that will identify the false intruder in the CR networks, here it is proposed with the Enhanced Q-Learning model with Coalition Game approach (EQLCG) to outline the energy enhancement. Besides an approach on Greedy Bidding is used to allocate the spectrum to the winning secondary user (SU) based on the idle primary user to strengthen the spectrum sensing. The winning secondary user forms a communication establishment with the neighbouring SU to eradicate the miss detection probability based on group level cooperation. The simulation experiment analyses the cluster level security with energy monitoring that has been performed using the analysis of interference by applying the coalition game theory modelling and the information obscured by the attacker is reduced with the usage of enhanced Q-learning, and the results prove that overhead is substantially monitored. The proposed paper enhances the security in physical layer with energy conservation and maintains the spectrum usage for application purpose. The proposed simulation approach reduces the miss detection and false alarm probabilistic approach while compared with Stackelberg and Bayesian game models.

show abstract

“…A hybrid model 60 combined DNN with SARIMA developed for making the short-term traffic prediction better. Load Balanced Clustering Technique using Genetic Algorithms, 61 Secure and fair cluster head selection protocol, 62 and Energy Efficient clustering protocol 63 can be applied to cluster the traffic data efficiently. Table 1 shows the various methodologies used to predict traffic flow and speed along with the data source information and prediction interval values.…”

Section: Related Workmentioning

confidence: 99%

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Vijayalakshmi¹,

Ramar²,

Jhanjhi

et al. 2020

Int J Communication

101

View full text Add to dashboard Cite

Summary In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work.

show abstract

Load Balanced Clustering Technique in MANET using Genetic Algorithms

Cited by 12 publications

References 14 publications

Cost-Effective Watermarking Scheme for Authentication of Digital Fundus Images in Healthcare Data Management

Cost-Effective Watermarking Scheme for Authentication of Digital Fundus Images in Healthcare Data Management

EECCRN: Energy Enhancement with CSS Approach Using Q-Learning and Coalition Game Modelling in CRN

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Contact Info

Product

Resources

About