An automated framework for power-efficient detection in embedded sensor systems

The availability of miniature low-cost sensors has allowed for the capture of rich, multimodal data streams in compact embedded sensor nodes. These devices have the capacity to radically improve the quality and amount of data available in such diverse applications as detecting degenerative diseases, monitoring remote regions, and tracking the state of smart assets as they traverse the supply chain. However, current implementations of these applications suffer from short lifespans due to high sensor energy use and limited battery size. By concentrating our design efforts on the sensors themselves, it is possible to construct embedded systems that achieve their goal(s) while drawing significantly less power. This will increase their lifespan, allowing many more applications to make the transition from laboratory to marketplace and thereby benefit a much wider population. This dissertation presents an automated framework for power-efficient detection in embedded sensor systems. The core of this framework is a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors, such that only the data necessary to determine the system state is collected at any given time.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Benbasat, Ari Yosef, 1975-

Advisor dc:contributor.advisor

• Joseph A. Paradiso.

• Architecture. Program In Media Arts and Sciences

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/38524

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/38524