The increase in wireless communication and microelectronic devices enables the development of micro sensors with monitoring capable for large areas. Consisting of thousands of sensor nodes, working collaboratively, the Wireless sensor networks have severe energy constraints, due to the limited capacity of batteries of the nodes that compose the network. The power consumption can be minimized by allowing only a few special nodes, called Cluster Head, are responsible for receiving data from its cluster nodes that form and propagate this data to a collection point called Base Station. The choice of optimum cluster head influence on increasing the period of stability of the network, maximizing their useful life. The proposal, presented in this thesis, uses Fuzzy Logic and k-means algorithm based on centralized information on Base Station for election of ideal Cluster Head for Heterogeneous Wireless Sensors Networks. The criteria used to select the ideal Cluster Head are based on the node centrality, energy level and proximity to the Base Station. This dissertation presents the disadvantages when the local information are used to the cluster leader election and the importance of discriminatory treatment on the energy discrepancies in the network. This proposal is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) and Distributed energy-efficient clustering (DEEC) algorithms. This comparison is evaluated using the end of the stability period and the lifetime of the network.
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