An improved mobility management technique for IEEE 802.11 based WLAN by predicting the direction of the mobile node

Abstract

In wireless local area network (WLAN), mobility management becomes a sophisticated issue of research due to the limited coverage of the Access Points (AP). For the successful communication between the mobile nodes (MN), every node should be connected with an access point (AP) during their operating mode, irrespective of their mobility. When a MN travels into the coverage area of its current AP, no special technique is required to maintain the connectivity. But when a MN travels out of the coverage area of its current AP and tries to associate with another AP, a delay occurs during the handover of connection which leads to a failure of connection. Throughout the last few years plenty of researches had been done to reduce this handover delay by reducing the scanning delay as the scanning process causes 90% of the total handover delay. In this paper, we propose a new scanning technique where the most potential AP is chosen according to the direction of the MN. First, we fit a polynomial equation along the trajectory of its motion and then to predict the direction of motion, we analyze the slope of the trajectory. Our simulation results show the effectiveness and accuracy of our proposed scheme in practical field.

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

“…However, when a user comes to a new place, there is no sufficient historical data to mine its mobility pattern, and its movement shows irregularity. Though the prediction of random movements is relatively more difficult due to irregularity, next cell can also be determined to some extent by various strategies, such as real-time monitoring [18]. Approaches for predicting random movements will be introduced in later sections as well.…”

“…The position reported by a user is a series of discrete coordinates stored in the cache, then Lagrange polynomials are established by fitting these co-ordinates to represent the trajectory. The moving direction can be predicted by analyzing the slope of the corresponding trajectory [18].…”

“…Whereas, the authors in [18] explicitly propose a scheme that predicts mobile direction by fitting a polynomial equation along the trajectory based on Lagrange's interpolation. Similarly, the paper considers hexagonal cells within tworegion structures.…”

“…Considering Ultra-Dense Networks (UDNs) have small diameters and the network environment is more complex, which makes the conventional prediction schemes cannot play an effective role, the authors of [34] present a combined approach to predict Transmission Points (TPs) in UDNs. Similar to [18], the paper exploits Lagrange's interpolation to estimate moving direction. But it considers not only angle but also the distance between a user and adjacent TPs.…”