  We consider the External Clock Synchronization problem in dynamic sensor
networks. Initially, sensors obtain inaccurate estimations of an external time
reference and subsequently collaborate in order to synchronize their internal
clocks with the external time. For simplicity, we adopt the drift-free
assumption, where internal clocks are assumed to tick at the same pace. Hence,
the problem is reduced to an estimation problem, in which the sensors need to
estimate the initial external time. This work is further relevant to the
problem of collective approximation of environmental values by biological
groups.
  Unlike most works on clock synchronization that assume static networks, this
paper focuses on an extreme case of highly dynamic networks. Specifically, we
assume a non-adaptive scheduler adversary that dictates in advance an
arbitrary, yet independent, meeting pattern. Such meeting patterns fit, for
example, with short-time scenarios in highly dynamic settings, where each
sensor interacts with only few other arbitrary sensors.
  We propose an extremely simple clock synchronization algorithm that is based
on weighted averages, and prove that its performance on any given independent
meeting pattern is highly competitive with that of the best possible algorithm,
which operates without any resource or computational restrictions, and knows
the meeting pattern in advance. In particular, when all distributions involved
are Gaussian, the performances of our scheme coincide with the optimal
performances. Our proofs rely on an extensive use of the concept of Fisher
information. We use the Cramer-Rao bound and our definition of a Fisher Channel
Capacity to quantify information flows and to obtain lower bounds on collective
performance. This opens the door for further rigorous quantifications of
information flows within collaborative sensors.
