Abstract
This research aims to identify stable neighbors in a mobile ad-hoc network to create a stable multi-path route for different mobility patterns. The other issue, this article deals with to schedule the data packets over those multiple paths to balance the loads across the paths and transmit the whole packets in minimum transmission time. The stable neighbors are chosen through a recurrent neural network which uses the previous neighborhood information as an input and predicts whether a node will be a neighbor in the next instance or not. We also framed a methodology to distribute the data packets across multiple paths based on their path length from source to destination. A simulation of the network model with two mobility models, Random way point and Gauss Markov mobility, shows that the accuracy of the recurrent neural-based stable node prediction is around 95%. The analytical, as well as a simulation, model shows that our proposed algorithm takes comparatively lesser time to transmit the same number of packets from a source to a destination due to better scheduling across multiple paths. Simulation results also demonstrate that compared to other similar multi-path routing protocols, our proposed algorithm yields a higher packet-delivery ratio and lower route recovery time also.
Published Version
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