Fault Diagnosis Method for Mobile Ad-hoc Network by Using Smart Neural Networks

Ghiasi, Maryam; Karimi, Abbas

doi:10.1016/j.procs.2014.11.055

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…It is important to handle fault and congestion in Mobile AD -HOC Network to improve performance by increasing packet delivery ratio and decreasing end -to -end delay and routing overhead. Therefore, for fault tolerance and congestion control DCDR is better than EDAODV, EDCSCAODV, AODV and EDOCR [12].…”

Section: Discussionmentioning

confidence: 99%

“…In [12] Back propagation Algorithm: Error back propagation training algorithm, which is an iterative gradient descent algorithm and is a simple way to train the multilayer feed forward neural networks. Neural network equipped with learning automata: The learning automata is based on the observation of the random response of the neural network and it adapted one of BP parameters.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fault Tolerance and Congestion Control in Mobile Ad – Hoc Networks: A Survey

2015

IJAERD

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fault Tolerance and Congestion Control in Mobile Ad – Hoc Networks: A Survey

2015

IJAERD

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“…In order to reduce the end-to-end delay of data forwarding, each node utilizes the information provided by the MAC layer to transmit its packets to the neighboring node that wakes up earlier. Ghiasi and Karimi [ 36 ] proposed an algorithm of learning automata adjusting learning rate on neural network. It is a combination of the back-propagation algorithm, a local search algorithm, and learning automata to provide efficient global search.…”

Section: Related Workmentioning

confidence: 99%

Energy‐Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

Robinson

Rajaram

2015

The Scientific World Journal

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Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.

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Fault Diagnosis Method for Mobile Ad-hoc Network by Using Smart Neural Networks

Cited by 2 publications

References 13 publications

Fault Tolerance and Congestion Control in Mobile Ad – Hoc Networks: A Survey

Fault Tolerance and Congestion Control in Mobile Ad – Hoc Networks: A Survey

Energy‐Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

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