Adaptive Route Selection Policy Based on Back Propagation Neural Networks

One of the key issues in the study of multiple route protocols in mobile ad hoc networks (MANETs) is how to select routes to the packet transmission destination. There are currently two route selection methods: primary routing policy and load-balancing policy. Many ad hoc routing protocols are based on primary (fastest or shortest but busiest) routing policy from the self-standpoint of traffic transmission optimization of each node. Load-balancing protocols equalize transmission load among multiple routes in the network. However, the lack of global perspective can cause congestion in primary policy and prolong delay time in load-balancing policy. So, although they are sometimes efficient, these two types of policies cannot adapt to intricately changing network conditions. We propose a new multiple route protocol with an Adaptive route selection Policy based on a Back propagation Neural network (APBN) to optimize selection policy. In our study, we used a gradient ascent algorithm to determine the relationship between different optimum route selection polices and varying conditions in the communication network and to make a neural network that learns this relationship using the Back Propagation (BP) algorithm to predict the next optimum route selection policy. We evaluated our protocol using Omnet simulator. The results show that the proposed scheme performs better than current protocols.