Embedded intelligence for fast QoS-based vertical handoff in heterogeneous wireless access networks
One of the most important aspects of the modern communications deals with the access to wireless networks by mobile devices, looking for a good quality of service under the users preferences. Nevertheless, a mobile terminal can discover more than one network of different technology along its trajectory in heterogeneous scenarios, being capable of connecting to other wireless access points according to their quality of service values. This is the case of the vertical handoff decision phase, present in many sceneries such as 3G-LTE access networks. In this context, an efficient resource management of different networks (a good selection of weights for their quality of service parameters) constitutes an optimization problem, where several heuristic methods using simple rules try to find the best available network. Nevertheless, the characteristics of the current mobile devices advise to use fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid the slow and massive computations associated with direct search techniques, so reducing the computation time. In this paper we propose an evolutionary algorithm capable of computing rapidly in embedded processors, improving the performance of other algorithms designed in order to solve this optimization problem.
wireless networks evolutionary algorithms embedded processors qos