Bio-inspired deep residual neural network learning model for QoS routing enhancement in mobile ad-hoc networks

In mobile ad-hoc networks (MANETs) always the quality-of-service (QoS) routing problem remains to exist and as a non-deterministic polynomial hard problem it is necessary to improve the QoS parameters to the most possible extent. For the considered MANET model, it is important to develop a suitable model that is capable of improving and enhancing the quality-of-service metrics. In this research study, a novel bio-inspired deep residual neural network (DResNet) architecture algorithm is developed over a MANET model for designing a most effective QoS routing protocol. The main aim of this proposed study is to locate a better routing path with satisfied QoS metrics for the MANET model and the bio-inspired deep learning neural model gets trained to meet the set fitness function. The new DResNet architecture operates with limited quantity of training data and its weights are optimized with novel IIWGSO technique. The proposed IIWGSO based DResNet model with the ad-hoc on-demand distance vector (AODV) protocol gets trained for the MANET model and its effectiveness is justified with QoS constraints being satisfied. The new IIWGSO-DResNet-AODV architecture resulted in better fitness value and QoS metrics proving its superiority compared to the existing techniques from previous literatures for the same MANET model.