There is a real need in the robotics and wireless sensor network (WSN) communities for the estimation of the geolocation of wireless agents. The received signal strength indicator (RSSI), a common metric in most networking hardware, has been reputed as a very unreliable method for doing the job, due to its vulnerability to environmental factors. Nevertheless, it still remains as the most prevalent estimator of distance between agents on many research projects. Multipath fading, shadowing and other effects that the environment exerts over a signal while propagating are regarded as the main cause of such vulnerability. Although some success has been obtained using RSSI outdoors where the effects are less noticeable, indoor settings remain an unconquered territory. The main motivation of this paper is to establish whether, in real time applications, the use of preprocessing techniques over partial raw collected data helps the RSSI to be a suitable estimator of distance. We propose one such technique and the results suggest that its use may indeed assist the obtainment of more accurate distance estimations while using RSSI.