Wireless Sensor Networks (WSNs) are at the forefront of emerging technologies due to the recent advances in Microelectromechanical Systems (MEMSs). The inherent multidisciplinary nature of WSN attracted scientists coming from different areas stemming from networking to robotics. WSNs are considered to be unattended systems with applications ranging from environmental sensing, structural monitoring, and industrial process control to emergency response and mobile target tracking. Most of these applications require basic services such as self-localization or time synchronization. The distributed nature and the limited hardware capabilities of WSN challenge the development of effective applications. In this paper, the self-localization problem for sensor networks is addressed. A distributed formulation based on the Information version of the Kalman Filter is provided. Distribution is achieved by neglecting any coupling factor in the system and assuming an independent reduced-order filter running onboard each node. The formulation is extended by an interlacement technique. It aims to alleviate the error introduced by neglecting the cross-correlation terms by "suitably” increasing the noise covariance matrices. Real experiments involving MICAz Mote platforms produced by Crossbows along with simulations have been carried out to validate the effectiveness of the proposed self-localization technique.