Abstract
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Wireless sensor networks are based on the synergy of small nodes. These nodes are mainly characterized by low energy consumption, low cost and wireless connectivity, and are capable of being used to measure temperature, pressure, humidity, as well as light. High-speed networks increase demands for high-performance computing. Sensor networks are one of the key technologies for the future, while could be considered as the most pivotal technology in the 21st century as well. In the present study, using a less-used method, the Fourier transform is specified on the location of the nodes within the second layer as well as their energy would determine the amount and the location of the cluster heads optimally in the wireless sensor network, hereafter, by using the optimal genetic algorithm we determine the state of Fourier transform output, which is a novel method. The fitness criterion is based on the minimum energy consumption of the network nodes during each data transfer operation. Generating balance and uniformity in nodal energy consumption and prolonging network lifetime, stands for an achievement the use of fast Fourier and genetic algorithm in this study that leads to improved network lifetime.
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Keywords
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Wireless sensor networks, energy consumption, wireless communications, second layer, genetic algorithm
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