Publications (4)0 Total impact
Conference Proceeding: Um Algoritmo Coevolutivo Cooperativo para Configuração de uma Rede de Sensores Sem FioAnais do XLIII Simpósio Brasileiro de Pesquisa Operacional; 01/2011
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ABSTRACT: This work proposes a cooperative coevolutionary algorithm for the design of a wireless sensor network considering complex network metrics. It is proposed an heuristic based on cooperative coevolution to find a network configuration such that its communication structure presents a small value for the average shortest path length and a high cluster coefficient. This configuration considers a cluster based network, where the cluster heads have two communication radii. The mathematical model of the cluster head location problem was developed to determine the nodes which will be configured as cluster heads. This model was adopted within the coevolutionary algorithm. We describe how the problem can be partitioned and how the fitness computation can be divided such that the cooperative coevolution model is feasible. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with two specifics complex network measurements allowing the reduction of energy consumption and the data transmission delay.13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Companion Material Proceedings, Dublin, Ireland, July 12-16, 2011; 01/2011
Conference Proceeding: Evolutionary design of wireless sensor networks based on complex networks[show abstract] [hide abstract]
ABSTRACT: This work proposes a genetic algorithm for designing a wireless sensor network based on complex network theory. We develop an heuristic approach based on genetic algorithms for finding a network configuration such that its communication structure presents complex network characteristics, e.g. a small value for the average shortest path length and high cluster coefficient. The work begins with the mathematical model of the hub location problem, developed to determine the nodes which will be configured as hubs. This model was adopted within the genetic algorithm. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with complex network characteristics, thus reducing the energy consumption and the data transmission delay.Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2009 5th International Conference on; 01/2010
- IJNCR. 01/2010; 1:33-53.
Universidade Federal de AlagoasMaçayó, Alagoas, Brazil