The fog computing paradigm allows for the distribution of computing resources and services at the edge of the network, close to end users, complementing cloud computing. Due to the dynamicity of fog computing environments, resource discovery is a key process that aims to find new computational resources that are available to integrate into it. These resources compose fog nodes, devices considering computational capabilities (such as CPU, memory, and disk) and behavioral characteristics (such as availability, scalability, and mobility). Performing an optimized resource discovery with all those attributes is still a challenge. This article proposes an efficient approach to resource discovery in fog computing that considers the computational capability and behavioral characteristics to select fog nodes. The results show that it is at least 33% more efficient than a similar solution found in the literature.