Demo Data from EDBT 2026 / reviewers in the wild / expert
Barbara Hohlt
dblp:61/3766
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2006
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author
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 |
Energy-efficient computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › energy management
power management |
0.0 | 1 | 2004 | Flexible power scheduling for sensor networks · IPSN 2004 |
Internet of things and sensor networks
wireless sensor network |
0.0 | 1 | 2004 | Flexible power scheduling for sensor networks · IPSN 2004 |
Energy-efficient computing › power management › low-power mode management
duty cycling |
0.0 | 1 | 2004 | Flexible power scheduling for sensor networks · IPSN 2004 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.1measurement · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Network Power Scheduling for TinyOS Applications
Barbara Hohlt, Eric A. Brewer |
DCOSS | 1 |
| 2004 | Flexible power scheduling for sensor networksabstractWe propose a distributed on-demand power-management protocol for collecting data in sensor networks. The protocol aims to reduce power consumption while supporting fluctuating demand in the network and provide local routing information and synchronicity without global control. Energy savings are achieved by powering down nodes during idle times identified through dynamic scheduling. We present a real implementation on wireless sensor nodes based on a novel, two-level architecture. We evaluate our approach through measurements and simulation, and show how the protocol allows adaptive scheduling and enables a smooth trade-off between energy savings and latency. An example current measurement shows an energy savings of 83% on an intermediate node. Barbara Hohlt, Lance Doherty, Eric A. Brewer |
IPSN | 1 |