The Use of a Hopfield Neural Network in Solving the Mobility Management Problem
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
A Simulated Annealing Approach for Mobile Location Management
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
A Genetic Algorithm for Finding Optimal Location Area Configurations for Mobility Management
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Applying Differential Evolution to a Realistic Location Area Problem Using SUMATRA
ADVCOMP '08 Proceedings of the 2008 The Second International Conference on Advanced Engineering Computing and Applications in Sciences
Wireless Communications & Networking
Wireless Communications & Networking
Solving a Realistic Location Area Problem Using SUMATRA Networks with the Scatter Search Algorithm
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Applying scatter search to the location areas problem
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Differential evolution for solving the mobile location management
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Location management methods for third generation mobile systems
IEEE Communications Magazine
Computationally efficient algorithms for location area planning in future cellular systems
Computer Communications
Location management for next-generation personal communications networks
IEEE Network: The Magazine of Global Internetworking
| Hi-index | 0.00 |
The optimization of the management tasks in current mobile networks is an interesting research field due to the exponential increase in the number of mobile subscribers. In this paper, we study two of the most important management tasks of the Public Land Mobile Networks: the location update and the paging, since these two procedures are used by the mobile network to locate and track the Mobile Stations. There are several strategies to manage the location update and the paging, but we focus on the Location Areas scheme with a two-cycle sequential paging, a strategy widely applied in current mobile networks. This scheme can be formulated as a multi-objective optimization problem with two objective functions: minimize the number of location updates and minimize the number of paging messages. In previous works, this multi-objective problem was solved with single-objective optimization algorithms by means of the linear aggregation of the objective functions. In order to avoid the drawbacks related to the linear aggregation, we propose an adaptation of the Non-dominated Sorting Genetic Algorithm II to solve the Location Areas Planning Problem. Furthermore, with the aim of studying a realistic mobile network, we apply our algorithm to a scenario located in the San Francisco Bay (USA). Results show that our algorithm outperforms the algorithms proposed by other authors, as well as the advantages of a multi-objective approach.