MLP network for optimal MR decision in a large-scale nesting mobile networks
The MR (Mobile Router) by existing top-down or bottom-up methods may not be the optimal MR if the numbers of mobile nodes and routers are substantially increased, and the scale of the network is increased drastically. Since an inappropriate MR decision causes handover or binding renewal to mobile nodes, determining of the optimal MR is important for efficiency. In this paper, we propose an algorithm that decides on the optimal MR using MR QoS information, and we describe how to understand the various structured MLP (Multi-Layered Perceptron) based on the algorithm. In conclusion, we prove the ability of the suggested neural network for a nesting mobile network through the performance analysis of each learned MLP.
mobile router perceptron hierarchical mobile ipv6 quality of service back-propagation