Reducing Unwanted Handoff Using Machine Learning

Day by day the importance of internet and wireless technologies has been tremendously increases. As the cellular wireless communication techniques grow rapidly, the cells become smaller than the traditional communication system, then the handover events are very frequent and need to support a large number of users, and handover detection has become a very active research direction in a mobile computing environment. When the user moves from the range of one wireless area network to another wireless area network handoff will get occurs. Handoff is a very important of wireless mobile communication. The handoff algorithms provide gain, quality of signal (QOS). The two vital problems are raising in handoff, not necessary occurrence of handoff and handoff failure. To avoid these issues, an algorithm needs to be implemented to decrease the unnecessary handoffs and handoff failures. It minimizes the unnecessary handoffs and handoff failures by using algorithm when the mobile terminal senses the network of any wireless technology. This algorithm minimizes the unnecessary handoff and failures of handoff whenever predictive travelling distance in Wi-Fi (Wireless-Fidelity) cell smaller than the threshold value. Theoretical and simulation analysis of the algorithm shows that, travelling distance prediction based algorithm minimizes the probability of the occurrence of unnecessary handoff.