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.
This work proposes the design of wireless sensor networks using evolutionary algorithms based on complex network measures. In this paper, the authors develop heuristic approaches based on genetic and memetic algorithms for finding a network configuration based on two complex network measures, the average shortest path length, and the cluster coefficient. The work begins with the mathematical model of the hub allocation problem, developed to determine the nodes that will be configured as hubs. This model was adopted within the basic and the hybrid genetic algorithm, and results reveal that the methodology allows the configuration of networks with more than a hundred nodes where some complex network measures are observed in the physical communication layer. The energy consumption and the delay could be reduced when a tree based routing is built over this physical layer.
This work proposes the design of wireless sensor networks using evolutionary algorithms based on complex network measures. In this paper, the authors develop heuristic approaches based on genetic and memetic algorithms for finding a network configuration based on two complex network measures, the average shortest path length, and the cluster coefficient. The work begins with the mathematical model of the hub allocation problem, developed to determine the nodes that will be configured as hubs. This model was adopted within the basic and the hybrid genetic algorithm, and results reveal that the methodology allows the configuration of networks with more than a hundred nodes where some complex network measures are observed in the physical communication layer. The energy consumption and the delay could be reduced when a tree based routing is built over this physical layer.
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