Login | Register

Vertical Handover Decision Making Using QoS Reputation and GM(1,1) Prediction


Vertical Handover Decision Making Using QoS Reputation and GM(1,1) Prediction

Giacomini, David (2012) Vertical Handover Decision Making Using QoS Reputation and GM(1,1) Prediction. Masters thesis, Concordia University.

Text (application/pdf)
Giacomini_MASc_F2012.pdf - Accepted Version


Telecommunication consumers are fueling a demand for mobile devices that are rapidly increasing in their capability to provide a wider range of services. These services in turn are consuming more bandwidth and require richer quality of service (QoS) in order to ensure a good end user experience when performing activities such as streaming video content or facilitating voice over IP (VoIP). As a result, network providers are expanding and improving their coverage area while technology to establish Wi-Fi hotspots is becoming more accessible to every day users. This combination of increase in demand and accessibility, coupled with users’ ever increasing expectations for high quality service presents a growing need to seamlessly optimize the use of the overlaid heterogeneous networks in urban areas to maximize the end user experience via the use of a vertical handover mechanism (VHO). Grey systems theory has been used in a wide range of systems including economic, financial, transportation, and military to accurately forecast time series based on limited information. In this thesis we build on a novel reputation based VHO decision rating system by proposing the use of the grey model first order one variable, GM(1,1), in the handover decision making progress. The low complexity of the GM(1,1) model allows for a quick and efficient prediction of the future reputation score for a given network, providing deeper insight into the current state of the target network. Furthermore, we analyze how this model helps balance the load across the heterogeneous networks employing its strategy.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Giacomini, David
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:14 September 2012
Thesis Supervisor(s):Agarwal, Anjali
ID Code:974727
Deposited On:24 Oct 2012 15:14
Last Modified:18 Jan 2018 17:38
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top