Enhancing the performance of MANET using EESCP

Kumar, Gulshan; Kaliappan, M.; Julus, L. Jerart

doi:10.1109/icprime.2012.6208348

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…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

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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.

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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

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“…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

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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.

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“…The protocol chooses a threshold below which a node is not permitted to relay data/control packets. This paper [8] proposed a method that is Energy Efficiency and Secure Communication Protocol (EESCP). It uses two algorithms cluster election and diffie Hellman key exchange algorithm that divides network into 2-hop clusters.…”

Section: Related Workmentioning

confidence: 99%

Power Saving Routing Protocol for Ad hoc Networks based on AODV

RaniGupta¹,

Mishra²,

Shrivastava³

2014

IJCA

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An ad hoc network is a wireless and infrastructure less network. It has dynamic and impermanent network topology. Each of nodes contains limited power in ad hoc networks. Power constraint is a major challenge to ad hoc networks. Limited power of nodes breaks link of the networks and reduces life of the networks. This proposed method minimizes the problem of link breakage and reduces the energy use of the network using a threshold (cutoff) based technique. This method takes all the paths whose nodes have energy greater than threshold value. This method selects the shortest path from the various paths based on AODV (Ad hoc on demand Distance Vector) protocol for routing. This proposed method is an extension of AODV protocol on the basis of energy factor and it shows better performance than existing AODV protocol. The performance parameters are throughput, packet delivery fraction and normalized routing load. It reduces network overhead and increases network lifetime. We use NS-2 network simulator for performance analysis. General TermsPower, Performance analysis, power aware routing.

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Enhancing the performance of MANET using EESCP

Cited by 10 publications

References 7 publications

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

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

Power Saving Routing Protocol for Ad hoc Networks based on AODV

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