Interval-based localization for mobile sensors in low-anchors density networks

In this article, we propose an original approach for self-localization in mobile sensor networks. The proposed approach is developed for low-anchors density networks. Based on intervals theory, the presented method is an online technique yielding a bounded-cumulative error. The estimation of the positions of mobile sensors is performed using multi-hop observation model added to an a priori mobility model. One of the contributions of this paper is that it uses the measurements of all types of sensors, including those that do not have GPS, denoted non-anchor nodes. Compared to the existing localization techniques, this method leads to a higher accuracy with a low computational cost.