Similarity-guided Sensor Localization without Explicit Ranging

Accurate sensor localization in wireless sensor networks (WSNs) is essential for context-aware applications, such as vehicle detection and environmental monitoring in smart cities. In densely deployed WSNs, spatial proximal sensor measurements are known to be highly correlated. Herein, we propose a sensor localization method based on the similarity of sensor measurements that simultaneously updates locations and intersensor distances by leveraging this similarity–distance relationship. The intersensor distance is modeled as a similarity-conditioned Gaussian distribution. Given an initial set of sensor locations, observed pairwise distances are used to update distribution parameters using maximum likelihood estimation and curve fitting. Subsequently, sensor locations are estimated by solving a nonlinear optimization problem. The proposed method establishes a self-consistent iterative process in which sensor locations and distance distributions are jointly updated using a statistical model. The simulation results indicate that the proposed method reduces the average localization error by up to 86% compared with that of initial locations when the sensor detection radius is 3.0–4.0 m. Even if the distance estimation contains errors, the iterative process improves localization accuracy by leveraging similarity information that does not directly represent distance. The proposed method eliminates the need for explicit ranging or threshold tuning, making it well-suited for dense sensor deployments. As a result, it expands the practical potential of densely deployed low-cost WSNs for large-scale environmental monitoring applications.