Demo Data from EDBT 2026 / reviewers in the wild / expert
Brian Holben
dblp:75/8919
· DBLP profile ↗
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.
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments
smart home |
0.1 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Ubiquitous computing and smart environments › smart home
smart thermostat |
0.1 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Energy systems and smart grids › building energy management
home energy management |
0.0 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Energy systems and smart grids › building energy management
HVAC control |
0.0 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Methods — techniques the papers use, named apart from their topics
occupancy sensing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | The smart thermostat: using occupancy sensors to save energy in homesabstractHeating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households. Jiakang Lu, Tamim I. Sookoor, Vijay Srinivasan, Brian Holben, John A. Stankovic, Eric Field, Kamin Whitehouse |
SenSys | 5 |