A smart handover prediction system based on curve fitting model for Fast Mobile IPv6 in wireless networks

Abstract

Seamless handover process is essential in order to provide efficient communication between mobile nodes in wireless local area networks. Despite the importance of a signal strength prediction model to provide seamless handovers, it is not embedded in standard mobility management protocols. In this article, we propose a smart handover prediction system based on curve fitting model to perform the handover (CHP) algorithm. The received signal strength indicator parameter, from scanning phase, is considered as an input to the CHP in order to provide a prediction technique for a mobile node to estimate the received signal strength value for the access points in the neighborhood and to select the best candidate access point from them in an intelligent way. We implemented the proposed approach and compared it with standard protocols and linear regression-based handover prediction approach. Simulation results in complex wireless environments show that our CHP approach performs the best by predicting the received signal strength value with up to 800 ms in advance from real obtained value via scanning phase. Moreover, our CHP approach is the best in terms of layer 2 and overall handover latency, in comparison with standard protocols and linear regression approach, respectively.