Demo Data from EDBT 2026 / reviewers in the wild / expert
Lisa Shirachi
dblp:72/2385
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › mobile sensing
mobile sensing platform |
0.1 | 1 | 2005 | Networked infomechanical systems: a mobile embedded networked sensor platform · IPSN 2005 |
Internet of things and sensor networks › wireless sensor network › distributed sensing
networked sensing |
0.1 | 1 | 2005 | Networked infomechanical systems: a mobile embedded networked sensor platform · IPSN 2005 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
DCOSS | 7 |
| 2005 | Networked infomechanical systems: a mobile embedded networked sensor platformabstractNetworked 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 |
IPSN | 7 |