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Lisa Shirachi

dblp:72/2385 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2005
—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.

Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › mobile sensing
mobile sensing platform
0.112005
Networked infomechanical systems: a mobile embedded networked sensor platform · IPSN 2005
Internet of things and sensor networks › wireless sensor network › distributed sensing
networked sensing
0.112005
Networked infomechanical systems: a mobile embedded networked sensor platform · IPSN 2005
YearPublicationVenuePosition
2005 Coordinated Static and Mobile Sensing for Environmental Monitoring
Richard Pon, Maxim A. Batalin, Victor Chen 0001, Aman Kansal, Mohammad H. Rahimi, Lisa Shirachi, Arun Somasundra, Mark H. Hansen, William J. Kaiser, Mani Srivastava 0001, Gaurav S. Sukhatme, Deborah Estrin
DCOSS7
2005 Networked infomechanical systems: a mobile embedded networked sensor platform
abstract
Networked infomechanical systems (NIMS) introduces a new actuation capability for embedded networked sensing. By exploiting a constrained actuation method based on rapidly deployable infrastructure, NIMS suspends a network of wireless mobile and fixed sensor nodes in three-dimensional space. This permits run-time adaptation with variable sensing location, perspective, and even sensor type. Discoveries in NIMS environmental investigations have raised requirements for 1) new embedded platforms integrating many diverse sensors with actuators, and 2) advances for in-network sensor data processing. This is addressed with a new and generally applicable processor-preprocessor architecture described in this paper. Also this paper describes the successful integration of R, a powerful statistical computing environment, into the embedded NIMS node platform.
Richard Pon, Maxim A. Batalin, Jason Gordon, Aman Kansal, Mohammad H. Rahimi, Lisa Shirachi, Mark H. Hansen, William J. Kaiser, Mani Srivastava 0001, Gaurav S. Sukhatme, Deborah Estrin
IPSN7