  In this paper, we develop a \textcolor{black}{\emph{distributed}} algorithm
to localize a network of robots moving arbitrarily in a bounded region. In the
case of such mobile networks, the main challenge is that the robots may not be
able to find nearby robots to implement a distributed algorithm. We address
this issue by providing an opportunistic algorithm that only implements a
location update when there are nearby robots and does not update otherwise. We
assume that each robot measures a noisy version of its motion and the distances
to the nearby robots. To localize a network of mobile robots in~$\mathbb{R}^m$,
we provide a simple \emph{linear} update, which is based on barycentric
coordinates and is linear-convex. We abstract the corresponding localization
algorithm as a Linear Time-Varying (LTV) system and show that it asymptotically
converges to the true locations~of~the robots.
  We first focus on the noiseless case, where the distance and motion vectors
are known (measured) perfectly, and provide sufficient conditions on the
convergence of the algorithm. We then evaluate the performance of the algorithm
in the presence of noise and provide modifications to counter the undesirable
effects of noise. \textcolor{black}{We further show that our algorithm
precisely tracks a mobile network as long as there is at least one known beacon
(a node whose location is perfectly known).
