Authors:
Mohammad Sh. Daoud
1
;
Aladdin Ayesh
1
;
Mustafa Al-Fayoumi
2
and
Adrian A. Hopgood
3
Affiliations:
1
De Montfort University, United Kingdom
;
2
Salman bin Abdulaziz University, Saudi Arabia
;
3
Sheffield Hallam University, United Kingdom
Keyword(s):
Ant Colony Optimization, LBSs, Mobility Prediction, Cellular Network, UMTS.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Hybrid Intelligent Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Mobile Agents
;
Multi-Agent Systems
;
Self Organizing Systems
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
Cellular communication networks have become medium to provide various services. Most of the services provided are based on the users’ locations, as in location-based services (LBSs); these services include both common voice services as well as multimedia and integrated data services. Used techniques mostly suffered from complex computation, accuracy rate regression and insufficient accuracy. Nevertheless, in the cell side, reducing the complexity cost and preventing the prediction algorithm to perform in two closer time slot. That’s why using routing area should be able to avoid the cell side problems. This paper discusses An Enhanced Ant Colony Optimization for Routing Area Mobility Prediction over Cellular Communications Network (EACORA) which is based on developed ant colony Optimization.