Lucid dreaming
Sasha Jevtic, Mathew Kotowsky, Robert P. Dick, Peter A. Dinda, Charles H. Dowding
January 1, 2007 DOI: 10.1145/1236360.1236405 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractStandard sensor networks waste battery power because they are designed to continuously poll for data, making them unsuitable for long-term use in applications that must detect and respond to unpredictable events. To solve this, a hardware-software technique called lucid dreaming was designed, implemented, and evaluated for a structural autonomous crack monitoring application in civil engineering. The technique dramatically reduces sensor node power consumption by enabling high-resolution sampling only in response to aperiodic vibrations in buildings and bridges, rather than running continuously.
Study at a glance
| Characteristics | Evaluation |
|---|---|
| Keywords | Computer science Cognitive science Psychology |
| Citations | 26 |
| Key finding | The lucid dreaming technique dramatically decreases sensor node power consumption in event-driven sensing applications such as autonomous crack monitoring. |
Abstract
Existing sensor network architectures are based on the assumption that data will be polled. Therefore, they are not adequate for long-term battery-powered use in applications that must sense or react to events that occur at unpredictable times. In response, and motivated by a structural autonomous crack monitoring (ACM) application from civil engineering that requires bursts of high resolution sampling in response to aperiodic vibrations in buildings and bridges, we have designed, implemented, and evaluated lucid dreaming, a hardware--software technique to dramatically decrease sensor node power consumption in this and other event-driven sensing applications.