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.

Improving Collection Dynamics by Monotonic Filtering

Audrito G.;
2020-01-01

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.
2020
1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2020
Online
2020
Proceedings - 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2020
Institute of Electrical and Electronics Engineers Inc.
127
132
978-1-7281-8414-2
data aggregation; edge computing; selfstabilisation
Zainab H.; Audrito G.; Dasgupta S.; Beal J.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1761364
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