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Alexandre Linares

dblp:24/925 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Internet of things and sensor networks · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
wireless sensor network
0.112006
Sdlib: a sensor network data and communications library for rapid and robust application development · IPSN 2006
Distributed systems › distributed data processing
dataflow systems
0.012006
Sdlib: a sensor network data and communications library for rapid and robust application development · IPSN 2006
YearPublicationVenuePosition
2006 Sdlib: a sensor network data and communications library for rapid and robust application development
abstract
Sensor network applications tend to exhibit significant high-level commonalities along several major dimensions that have heretofore been underexposed, particularly in the areas of collection and dissemination. We have developed a component library, sdlib, which presents the fundamental abstractions of collection and dissemination as part of a dataflow sytem. This allows application developers to rapidly develop applications at the nesC level. This means that sdlib maintains significant expressivity while operating efficiently.We have built four applications, each faithful to a mature monolithic application, on top of sdlib to compare its performance to that of original. We find that applications implemented with sdlib are much simpler to write, just as resource efficient, and perform comparably to monolithic implementations.
David Chu, Kaisen Lin, Alexandre Linares, Joseph M. Hellerstein
IPSN3