A neural network controller for congestion control in ATM multiplexers
Computer Networks and ISDN Systems
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
| Hi-index | 0.00 |
In this paper, we propose an adaptability enhancement mechanism to be integrated with OLSR, and potentially any Mobile Ad Hoc Network (MANET) proactive routing protocol. The key of this mechanism is prediction and evaluation of the mean queuing delay as a routing metric. Neural network methods are used to predict delays. We investigated the pros and cons of using two types of neural networks, namely Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF), in predicting nonstationary time series (e.g., mean queuing delay time series). We present TierUp -- our novel node-state routing table calculation algorithm, which is developed for the integration of delay prediction and OLSR. We name the extended version of OLSR as OLSR_NN. We show through ns2 simulation that compared to OLSR, OLSR_NN is able to increase data packet delivery ratio and reduce average end-to-end delay in scenarios with complex traffic patterns and various node mobility. Our simulation also shows the advantage of using neural network for delay prediction compared to moving average and exponential smoothing. The enhanced adaptability of OLSR_NN is further verified by the more balanced traffic observed in our simulation.