  In this correspondence, we present an algorithm for distributed sensor
localization with noisy distance measurements (DILAND) that extends and makes
the DLRE more robust. DLRE is a distributed sensor localization algorithm in
$\mathbb{R}^m$ $(m\geq1)$ introduced in \cite{usman_loctsp:08}. DILAND operates
when (i) the communication among the sensors is noisy; (ii) the communication
links in the network may fail with a non-zero probability; and (iii) the
measurements performed to compute distances among the sensors are corrupted
with noise. The sensors (which do not know their locations) lie in the convex
hull of at least $m+1$ anchors (nodes that know their own locations.) Under
minimal assumptions on the connectivity and triangulation of each sensor in the
network, this correspondence shows that, under the broad random phenomena
described above, DILAND converges almost surely (a.s.) to the exact sensor
locations.
