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Improving Collection Dynamics by Monotonic Filtering
Conference proceeding

Improving Collection Dynamics by Monotonic Filtering

Hunza Zainab, Giorgio Audrito, Soura Dasgupta and Jacob Beal
2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), pp.127-132
08/2020
DOI: 10.1109/ACSOS-C51401.2020.00043

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Abstract

A key coordination problems in distributed open systems is distributed sensing, as achieved by cooperation and interaction among individual devices. An archetypal operation of distributed sensing is data summarization over a region of space, by which many higher level problems can be addressed, including counting items, measuring space, averaging environmental values, etc. A typical coordination strategy to perform data summarization in a peer-to-peer scenario, where devices can communicate only with a neighborhood, is to progressively accumulate information towards one or more collector devices, though this typically exhibits problems of reactivity and fragility. In this paper, we present a monotonic filtering strategy for improving the dynamics of single path collection algorithms. The strategy consists of inhibiting communication across devices whose distance towards the collector device is not decreasing. We prove that single path collection in a line graph results in quadratic overestimates after a source change and that these overestimates disappear with the application of monotonic filtering. These preliminary results suggest that monotonic filtering is likely to improve the dynamics of singlepath collection algorithms, by preventing excessive overestimates.
 data aggregation Distributed databases edge computing Electronic mail Heuristic algorithms Network topology Performance evaluation Robot sensing systems selfstabilisation

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