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In order to gather information more efficiently, wireless sensor networks are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop wireless sensor networks. Unequal clustering mechanisms, which are designed by considering the base station location, solve this problem. In this thesis, we propose a fuzzy unequal clustering algorithm (EAUCF) which aims to prolong the lifetime of wireless sensor networks. EAUCF adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. We utilize fuzzy logic for handling the uncertainties in cluster-head radius estimation. We compare our algorithm with some popular algorithms in literature, namely LEACH, CHEF and EEUC, according to First Node Dies (FND), Half of the Nodes Alive (HNA) and energy-efficiency metrics. Our simulation results show that EAUCF performs better than other algorithms in most of the cases considering FND, HNA and energy-efficiency. Therefore, our proposed algorithm is a stable and energy-efficient clustering algorithm.