María Dolores Jaraíz-Simón’s research while affiliated with University of Extremadura and other places

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 user’s 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 the 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.

When a mobile terminal is moving across heterogeneous wireless networks acting as access points, it must decide the best network to connect to, taking into account the values of the quality of service parameters of the networks. Selecting an optimal set of weights for these values in the terminal is an optimization problem that must be solved in real time for embedded microprocessors that manage the Vertical Handoff decision phase in highly dynamic environments. For this purpose, we have developed an adaptive heuristic inspired on the Simulated Annealing algorithm that improves the performance of a former algorithm designed to solve this optimization problem.

When a mobile wireless sensor is moving along heterogeneous wireless sensor networks, it can be under the coverage of more than one network many times. In these situations, the Vertical Handoff process can happen, where the mobile sensor decides to change its connection from a network to the best network among the available ones according to their quality of service characteristics. A fitness function is used for the handoff decision, being desirable to minimize it. This is an optimization problem which consists of the adjustment of a set of weights for the quality of service. Solving this problem efficiently is relevant to heterogeneous wireless sensor networks in many advanced applications. Numerous works can be found in the literature dealing with the vertical handoff decision, although they all suffer from the same shortfall: a non-comparable efficiency. Therefore, the aim of this work is twofold: first, to develop a fast decision algorithm that explores the entire space of possible combinations of weights, searching that one that minimizes the fitness function; and second, to design and implement a system on chip architecture based on reconfigurable hardware and embedded processors to achieve several goals necessary for competitive mobile terminals: good performance, low power consumption, low economic cost, and small area integration.

This work presents the application of a parallel cooperative optimization approach to the broadcast operation in mobile ad-hoc networks (manets). The optimization of the broadcast operation implies satisfying several objectives simultaneously, so a multi-objective approach has been designed. The optimization lies on searching the best configurations of the dfcn broadcast protocol for a given manet scenario. The cooperation of a team of multi-objective evolutionary algorithms has been performed with a novel optimization model. Such model is a hybrid parallel algorithm that combines a parallel island-based scheme with a hyperheuristic approach. Results achieved by the algorithms in different stages of the search process are analyzed in order to grant more computational resources to the most suitable algorithms. The obtained results for a manets scenario, representing a mall, demonstrate the validity of the new proposed approach.