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Localization is one of the challenges in wireless sensor networks, especially those without the aid of a global positioning system. Use of a dedicated positioning device incurs additional cost and reduces battery life; therefore, a range-free localization scheme is promising as a cost-effective approach. However, the main limitation of this approach is that the estimation precision can be affected by factors such as node density, sensing coverage, and topology diversity. Thus, this study investigates and proposes a method for improving a traditional range-free-based localization method (centroid) that uses soft computing approaches in a hybrid model. This model integrates a fuzzy logic system into centroid and uses an extreme learning machine (ELM) optimization technique to capitalize on the strengths of both approaches: the former is properly used with low node density and short coverage, while the latter is used for …