  Distributed data collection is a fundamental task in open systems. In such
networks, data is aggregated across a network to produce a single aggregated
result at a source device. Though self-stabilizing, algorithms performing data
collection can produce large overestimates in the transient phase. For example,
in [1] we demonstrated that in a line graph, a switch of sources after initial
stabilization may produce overestimates that are quadratic in the network
diameter. We also proposed monotonic filtering as a strategy for removing such
large overestimates. Monotonic filtering prevents the transfer of data from
device A to device B unless the distance estimate at A is more than that at B
at the previous iteration. For a line graph, [1] shows that monotonic filtering
prevents quadratic overestimates. This paper analyzes monotonic filtering for
an arbitrary graph topology, showing that for an N device network, the largest
overestimate after switching sources is at most 2N.
