WOA-NN: a decision algorithm for vertical handover in heterogeneous networks

Abstract

The heterogeneous network of a 4th generation not always support better communication and mobility between the Wireless Access Networks. Hence, the vertical handoff is highly necessitated. This paper establishes vertical handover, which is context-aware in a heterogeneous environment with WiMax and WiFi. Successful handover results with the better determination of handover points. So, an Artificial Neural Network-based network model to understand the network characteristics is firstly developed. Under simulated environment, the Received Signal Strength (RSS) of the heterogeneous network is observed to construct the training library. The trained network predicts RSS for resolving the handover points in the heterogeneous network. To ensure precise learning of the neural network about the RSS network characteristics, a renowned Whale Optimization Algorithm (WOA) is developed. The performance of WOA-NN model is compared with the conventional Levenberg–Marquardt-Neural Network, Fire Fly-Neural Network, Particle Swarm Optimization-Neural Network and Grey Wolf Optimization-Neural Network through throughput, handover, predicted RSS and Mean Absolute Error analyses. The predicted RSS of the proposed WOA-NN-based network model seems nearly closer to the actual model, attaining effective handoff.