Computing over Unreliable Communication Networks

Nicola Elia, Jing Wang, Andalam Satya Mohan Vamsi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this chapter, we take the unifying view of systems interacting over communication networks as distributed computing systems and propose to study them as networked control systems. Since averaging is central operation to much science and engineering, we first consider the problem of distributed averaging over unreliable networks. We point out that a popular and well-behaved algorithm can instead generate a collective global complex behavior when the inter-agent communication happens over unreliable links. To mitigate the effects of the unreliable information exchange, we propose a new distributed averaging algorithm robust to noise and intermittent communication. The algorithm and the control perspective are the basis for the development of new distributed optimization systems that we can analyze and design as networked control systems. In particular, we apply a newly developed networked controller design method to design an improved ad-hoc networked distributed least square algorithm. The approach applies to multi-agent cooperative applications and opens up several directions of research.

Original languageEnglish (US)
Title of host publicationInformation and Control in Networks
PublisherSpringer Verlag
Pages241-275
Number of pages35
ISBN (Print)9783319021492
DOIs
StatePublished - 2014
Externally publishedYes
EventLCCC Workshop in Information and Control in Networks - Lund, Sweden
Duration: Oct 17 2012Oct 19 2012

Publication series

NameLecture Notes in Control and Information Sciences
Volume450 LNCIS
ISSN (Print)0170-8643

Other

OtherLCCC Workshop in Information and Control in Networks
Country/TerritorySweden
CityLund
Period10/17/1210/19/12

Bibliographical note

Funding Information:
This research was supported by LCCC—Linnaeus Grant VR 2007-8646, Swedish Research Council.

Funding Information:
This research has been supported by NSF grants ECS-0093950, ECS-0524689 ECS-0901846, ECS-1239319.

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