AFLCH: Self-adaptive unequal fuzzy based clustering of heterogeneous sensors in wireless sensor networks

In this study, an adaptive fuzzy logic based algorithm for clustering heterogeneous sensors is proposed (AFLCH) which considers the environmental conditions of each sensor to select the best candidates as cluster centers. The proposed method uses three different clustering algorithms, different clustering parameters and adaptive threshold in order to control the number of total messages inside the network to increase the life of the sensors as well as the life of the network. AFLCH is compared to other methods using criteria such as the first node dies (FND), total residual energy (TRE), half node die (HND), the total number of the dead sensors and the last sensor dies (LND). The results indicate that AFLCH is able to control more energy and also increase network lifetime compared to other methods by decreasing the number of received and sent messages.