  This paper addresses the problem of distributed event localization using
noisy range measurements with respect to sensors with known positions. Event
localization is fundamental in many wireless sensor network applications such
as homeland security, law enforcement, and environmental studies. However, most
existing distributed algorithms require the target event to be within the
convex hull of the deployed sensors. Based on the alternating direction method
of multipliers (ADMM), we propose two scalable distributed algorithms named
GS-ADMM and J-ADMM which do not require the target event to be within the
convex hull of the deployed sensors. More specifically, the two algorithms can
be implemented in a scenario in which the entire sensor network is divided into
several clusters with cluster heads collecting measurements within each cluster
and exchanging intermediate computation information to achieve localization
consistency (consensus) across all clusters. This scenario is important in many
applications such as homeland security and law enforcement. Simulation results
confirm effectiveness of the proposed algorithms.
