Abstract
Problem statement: With numerous wireless devices increasingly connecting to the internet, WLAN infrastructure planning becomes very important to maintain desired quality of services. For maintaining desired quality of service it is desirable to know the movement pattern of users. Mobility prediction involves finding the mobile device's next access point as it moves through the wireless network. Hidden Markov models and Bayesian approach have been proposed to predict the next hop. Approach: In this study we propose a new method for feature extraction and propose a novel neural network classifier based on a hidden Genetic Algorithm layer-GA-SOFM Neural Network. We evaluate our hypotheses by using one month syslog data of Dartmouth college mobility traces available online to extract mobility features and use this features to find the classification accuracy of the proposed model. Results and Conclusion: Proposed methodology was implemented and obtained accuracy in mobility prediction was 83.68%. The output obtained is better than Naive Bayesian method by 15.56% and over CART by 11.94%.
Highlights
Mobility prediction of wireless devices (Aljadhai and Znati, 2001) helps the users in smart access and helps the service provider in planning the infrastructure and provide better QOS
In the case of GSM networks a mobile user can be traveling between the cells of a PCS or GSM network and in the case of a infrastructure based wireless network the user may be moving between various access points in the network
Using the logs obtained from all the access points with the associated time stamp for each unique mobile device, the frequently used path can be found out and is called as User Mobility Pattern (UMP)
Summary
Mobility prediction of wireless devices (Aljadhai and Znati, 2001) helps the users in smart access and helps the service provider in planning the infrastructure and provide better QOS. Using the logs obtained from all the access points with the associated time stamp for each unique mobile device, the frequently used path can be found out and is called as User Mobility Pattern (UMP). This study presents a neural network classification algorithm for the prediction of user movements in wireless campus environment.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
