Learning in the recurrent random neural network
Neural Computation
A neural network controller for congestion control in ATM multiplexers
Computer Networks and ISDN Systems
Applied Mathematics and Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bi-directional computing architecture for time series prediction
Neural Networks
Design and performance of cognitive packet networks
Performance Evaluation
A high-throughput path metric for multi-hop wireless routing
Proceedings of the 9th annual international conference on Mobile computing and networking
Architecture and evaluation of an unplanned 802.11b mesh network
Proceedings of the 11th annual international conference on Mobile computing and networking
QoS support and OLSR routing in a mobile ad hoc network
ICNICONSMCL '06 Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies
Finding the embedding dimension and variable dependencies in time series
Neural Computation
Function approximation with spiked random networks
IEEE Transactions on Neural Networks
Learning in the multiple class random neural network
IEEE Transactions on Neural Networks
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Existing MANET routing protocols rely heavily on hop count evaluation. Although this is simple and efficient, it sacrifices the potential performance gains obtainable by considering other dynamic routing metrics. In this paper, we propose a delay prediction mechanism and its integration with a MANET proactive routing protocol. We demonstrate our approach of predicting mean queuing delay as a nonstationary time series using appropriate neural network models: Multi-Layer Perceptron or Radial Basis Function. To support MANET proactive routing, our delay prediction mechanism is devised as a distributed, independent, and continuous neural network training and prediction process conducted on individual nodes. We integrated our delay prediction mechanism with a well-known MANET proactive routing protocol--OLSR. The essential part of this integration is our TierUp algorithm, which is a novel node-state routing table computation algorithm. The structure and the key parameters of the resulting extended OLSR, called OLSR_NN, are also discussed. Our simulation shows that because of its capability of balancing the traffic, OLSR_NN is able to increase data packet delivery ratio and reduce average end-to-end delay in scenarios with complex traffic patterns and wide range of node mobility, compared to OLSR.