Effective Collective Summarisation of Distributed Data in Mobile Multi-Agent Systems


Giorgio Audrito, Sergio Bergamini, Ferruccio Damiani, Mirko Viroli

18th International Conference on Autonomous Agents and MultiAgent Systems, pages 1618-–626
 2019
International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC

One of the key applications of physically-deployed multi-agent systems, such as mobile robots, drones, or personal agents in human mobility scenarios, is to promote a pervasive notion of distributed sensing achieved by strict agent cooperation. A quintessential operation of distributed sensing is data summarisation over a region of space, which finds many applications in variations of counting problems: counting items, measuring space, averaging environmental values, and so on. A typical strategy to perform peer-to-peer data summarisation with local interactions is to progressively accumulate information towards one or more collector agents, though this typically exhibits several sources of fragility, especially in scenarios featuring high mobility.

In this paper, we introduce a new multi-agent algorithm for dynamic summarisation of distributed data, called "parametric weighted multi-path", based on a local strategy to break, send, and then recombine sensed data across neighbours based on their estimated distance, ultimately resulting in the formation of multiple, dynamic and emergent paths of information flow towards collectors. By empirical evaluation via simulation in synthetic and realistic case studies, accounting for various sources of volatility, using different state-of-the-art distance estimations, and comparing to other existing implementations of aggregation algorithms, we show that parametric weighted multi-path is able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations.

(keywords) adaptive algorithm, aggregate programming, computational field, data aggregation, gradient
 @inproceedings{ABDV-AAMAS2019,
author = {Audrito, Giorgio and Bergamini, Sergio and Damiani, Ferruccio and Viroli, Mirko},
title = {Effective Collective Summarisation of Distributed Data in Mobile Multi-Agent Systems},
booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
series = {AAMAS '19},
year = {2019},
isbn = {978-1-4503-6309-9},
location = {Montreal QC, Canada},
pages = {1618--1626},
numpages = {9},
url = {http://dl.acm.org/citation.cfm?id=3306127.3331882},
acmid = {3331882},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
keywords = {adaptive algorithm, aggregate programming, computational field, data aggregation, gradient},
}  

Publication

— authors

Giorgio Audrito, Sergio Bergamini, Ferruccio Damiani, Mirko Viroli

— status

published

— sort

paper in proceedings

Venue

— volume

18th International Conference on Autonomous Agents and MultiAgent Systems

— pages

1618-–626

— publication date

2019

URLs

original page  |  original PDF

Identifiers

— print ISBN

978-1-4503-6309-9

BibTeX

— BibTeX ID
ABDV-AAMAS2019
— BibTeX category
inproceedings

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