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Christian Wartenburger

dblp:58/8069 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › wireless sensor network › event detection
distributed event detection
0.112010
A system for distributed event detection in wireless sensor networks · IPSN 2010
Internet of things and sensor networks
wireless sensor network
0.112010
A system for distributed event detection in wireless sensor networks · IPSN 2010
Internet of things and sensor networks › wireless sensor network
energy-efficient computing
0.012010
A system for distributed event detection in wireless sensor networks · IPSN 2010

Methods — techniques the papers use, named apart from their topics

on-node processing · 0.1collaborative event detection · 0.1
YearPublicationVenuePosition
2010 A system for distributed event detection in wireless sensor networks
abstract
Event detection is a major issue for applications of wireless sensor networks. In order to detect an event, a sensor network has to identify which application-specific incident has occurred based on the raw data gathered by individual sensor nodes. In this context, an event may be anything from a malfunction of monitored machinery to an intrusion into a restricted area. The goal is to provide high-accuracy event detection at minimal energy cost in order to maximize network lifetime. In this paper, we present a system for collaborative event detection directly on the sensor nodes. The system does not require a base station for centralized coordination or processing, and is fully trainable to recognize different classes of application-specific events. Communication overhead is reduced to a minimum by processing raw data directly on the sensor nodes and only reporting which events have been detected. The detection accuracy is evaluated using a 100node sensor network deployed as a wireless alarm system on the fence of a real-world construction site.
Georg Wittenburg, Norman Dziengel, Christian Wartenburger, Jochen H. Schiller
IPSN3