MAP BASED INTRA-CELL METHOD FOR LOCATION PREDICTION OVER UMTS NETWORK PLATFORM

Location based services (LBS) and their applications are regarded as the most
growing services applied on communication systems. According to the great
enhancement in the mobile network platform and its related database servers,
we are not only able to develop LBS applications but also to enhance the
performance of mobile networks. Future location prediction of mobile user
results in great enhancement in LBS applications and mobile network
performance. Many researches were introduced to predict location of mobile
user. In [7] User Mobility Pattern (UMP) algorithm based on sequential
mining prediction has been developed to deal with future location prediction in
mobile networks. The main limitations of this pattern are: map based intra cell
prediction cannot be extracted and the timestamps aren't associated with rules.
In [8], another method has been introduced using All frequent Movement
Pattern (ALLMOP) algorithm for extracting all frequent movement patterns
within movement Rule based Location Prediction method (RLP). RLP method
works on GPS network map based platform without manipulating cell to cell
movement. So, it can't be used to enhance mobile network's resource allocation
and mobility management. In this paper we proposed a new method, Intra Cell
Movement Prediction (ICMP), for mobile user's future location prediction on
Universal Mobile Telecommunication System (UMTS) environments to benefit
from both intra and inter cell based techniques for both network and services
enhancement. The proposed method depends on map based intra-cell
prediction and utilizes the network database and positioning techniques in
extracting user trajectories and movement rules. As a result, the user position
can be predicted for the next movement step. The performance of the proposed
algorithm is evaluated through computer simulation and compared with that of
[7] and [8]. The simulation results indicate that the proposed method is highly
efficient for both network and services' improvements as it is used to enhance
location based services with satisfactory accuracy, network's resource
utilization, reduce the latency in accessing resources and mobility
management.