Intelligent Reduction in Signaling Load of Location Management in Mobile Data Networks

Abstract

Massive increase in the mobile data traffic volume has recently resulted in a big interest towards the distributed mobility management solutions that aim to address the limitations and drawbacks of centralized mobility management. Location management is an important requirement in a distributed mobility management environment. To provide seamless Internet data services to a mobile node, the location of a mobile node is stored and periodically updated on a location server through a location update message that is sent by the mobile node. In this paper, we propose an intelligent approach of setting the period of sending location update messages on the basis of a mobile node's patterns of data sessions and IP handovers. We use a machine learning approach on the location server. The results show that our approach significantly reduces the signaling load of the location management and the overall reduction is more than 50%.

Handover authentication latency reduction using mobile edge computing and mobility patterns

“…It is evident through literature that the cellular data is not available due to the privacy issues [38]. So, we generate the residence times of MN using Exponential distribution.…”