  Many challenging tasks in sensor networks, including sensor calibration,
ranking of nodes, monitoring, event region detection, collaborative filtering,
collaborative signal processing, {\em etc.}, can be formulated as a problem of
solving a linear system of equations. Several recent works propose different
distributed algorithms for solving these problems, usually by using linear
iterative numerical methods.
  The main problem with previous approaches is that once the problem inputs
change during the process of computation, the computation may output unexpected
results. In real life settings, sensor measurements are subject to varying
environmental conditions and to measurement noise.
  We present a simple iterative scheme called SS-Iterative for solving systems
of linear equations, and examine its properties in the self-stabilizing
perspective. We analyze the behavior of the proposed scheme under changing
input sequences using two different assumptions on the input: a box bound, and
a probabilistic distribution.
  As a case study, we discuss the sensor calibration problem and provide
simulation results to support the applicability of our approach.
