Power Efficient Wireless Sensor Node through Edge Intelligence

Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Damle, Abhishek Priyadarshan

Chair dc:contributor.committeechair

• Ha, Dong S.

Committee members dc:contributor.committeemember

• Yi, Yang
• Jones, Creed F. III

• Edge intelligence
• LoRaWAN
• smart farm
• cattle behavior
• decision tree classifier

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35384

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111469