Robust sensor localization algorithm in wireless ad-hoc sensor networks

Abstract

Wireless ad-hoc sensor networks are being developed to collect data across the area of deployment. To stamp the collected data and facilitate communication protocols, it is necessary to identify the location of each sensor. Most existing localization algorithms make use of trilateration or multilateration based on range measurements obtained from RSSI, TOA, TDOA and AoA. In the paper, we first study some situations that most existing sensor localization methods fail to perform well. An example of such situations is when the topology of a sensor network is anisotropic. We propose a distributed sensor localization method with estimation-comparison-correction paradigm to address these conditions. In detail, multidimensional scaling (MDS) technique is applied to recover a series of local maps for adjacent sensors in two (or three) dimensional space. The maps along the route from an anchor to another anchor are stitched together to estimate the sensors' physical locations. Then, the estimated anchor locations are compared with the anchors' physical locations for correction, and estimated sensor locations are corrected at the same time to approximate their physical locations. By iterative estimation, comparison, and correction, the method reduces sensor localization errors caused by anisotropic network topology and complex terrain, which were seldom addressed in previous research.