NDLI: Mobile User Tracking Using A Hybrid Neural Network

Content Provider	Springer Nature Link
Author	Majumdar, Kausik Das, Nabanita
Copyright Year	2005
Abstract	In this paper, a novel technique for location prediction of mobile users has been proposed, and a paging technique based on this predicted location is developed. As a mobile user always travels with a destination in mind, the movements of users, are, in general, preplanned, and are highly dependent on the individual characteristics. Hence, neural networks with its learning and generalization ability may act as a suitable tool to predict the location of a terminal provided it is trained appropriately by the personal mobility profile of individual user. For prediction, the performance of a multi-layer perceptron (MLP) network has been studied first. Next, to recognize the inherent clusters in the input data, and to process it accordingly, a hybrid network composed of a self-organizing feature map (SOFM) network followed by a number of MLP networks has been employed. Simulation studies show that the latter performs better for location management. This approach is free from all unrealistic assumptions about the movement of the users. It is applicable to any arbitrary cell architecture. It attempts to reduce the total location management cost and paging delay, in general.
Starting Page	275
Ending Page	284
Page Count	10
File Format	PDF
ISSN	10220038
Journal	Wireless Networks
Volume Number	11
Issue Number	3
e-ISSN	15728196
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	mobile users location management paging location prediction MLP network SOFM network Computer Communication Networks Electronic and Computer Engineering Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Electrical and Electronic Engineering

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