Localization of wireless sensor networks with concurrently used identification sequences

In very large distributed sensor networks, transmission identifiers have to be shared among several nodes due to limited bandwidth and hardware resources. In this work, we propose and investigate an algorithm targeting this issue of nonunique identification in the framework of network localization. Our proposed method is based on a two-stage approach: The estimation of adjacency information based on the range measurements, and a maximum-likelihood (ML) estimation of the sensor that transmitted the ranging-pulse. The first stage can be formulated as a binary linear feasibility problem that is capable of significantly reducing the computational complexity and increasing the performance of the second stage. Our approach is compared to a method that does not exploit the estimated adjacency information. We show that our algorithm enables outstanding localization performance, also when using only few distinct transmission ID sequences.