Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Movement Prediction Oriented Adaptive Location Management

Movement Prediction Oriented Adaptive Location Management
View Sample PDF
Author(s): Tania Das (West Bengal University of Technology, India)
Copyright: 2009
Pages: 20
Source title: Handbook of Research on Mobile Multimedia, Second Edition
Source Author(s)/Editor(s): Ismail Khalil (Johannes Kepler University Linz, Austria)
DOI: 10.4018/978-1-60566-046-2.ch032


View Movement Prediction Oriented Adaptive Location Management on the publisher's website for pricing and purchasing information.


Movement prediction oriented adaptive location management provides a major role in personal communication service (PCS) system. Generally the GSM system supports two level architecture. Because it supports two kinds of databases-Home Location register and Visitor Location Register. Every time when the user crosses the location area it has to register with the HLR. This creates high cost for registration and location tracking as it involves the use of costly bandwidth between the Mobile Switching Center (MSC) and the HLR. In this paper the technique for reducing the costs during the location tracking and location update is proposed. Taking the movement prediction of the users it creates the block and the user registers with the HLR only after crossing the block instead of crossing the single cell. This movement prediction is generated using one neural network model for all the users. The block register (BR) is introduced between the block and the HLR in two level systems, thus introduces three level architecture. In this architecture some signaling cost values between the MSC-BR, BR-HLR and BR-BR are maintained to get the better performance. In this proposed system the aim is to set the value between the MSC and BR and the two BR as small as possible and the value between the BR and the HLR must be higher to get the better performance.

Related Content

K. Jairam Naik, Annukriti Soni. © 2021. 18 pages.
Randhir Kumar, Rakesh Tripathi. © 2021. 22 pages.
Yogesh Kumar Gupta. © 2021. 38 pages.
Kamel H. Rahouma, Ayman A. Ali. © 2021. 34 pages.
Muni Sekhar Velpuru. © 2021. 19 pages.
Vijayakumari B.. © 2021. 24 pages.
Neetu Faujdar, Anant Joshi. © 2021. 41 pages.
Body Bottom