Year: 2021
Type: conference-paper
Abstract: 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 of aggregates 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$2N$.
Institutions: University of Iowa, University of Turin, RTX (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G1627371218
OpenAlex ID: W3217097328