Hybrid Mobility Prediction of 802.11 Infrastructure Nodes by Location Tracking and Data Mining

TL;DR

This paper proposes a hybrid mobility prediction scheme combining location tracking and data mining to improve resource allocation and handoff performance in IEEE 802.11 networks, with a focus on accuracy and security.

Contribution

It introduces a novel hybrid prediction method for mobile nodes in wireless networks, integrating location tracking and data mining, along with a secure version.

Findings

High prediction accuracy demonstrated through simulation

Enhanced resource allocation efficiency

Improved handoff performance

Abstract

In an IEEE 802.11 Infrastructure network, as the mobile node is moving from one access point to another, the resource allocation and smooth hand off may be a problem. If some reliable prediction is done on mobile nodes next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput of wireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict the future path of the mobile node. We also propose a secure version of the same scheme. Through simulation and analysis, we present the prediction accuracy of our proposal.