Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Chris Savarese

dblp:25/5072 · DBLP profile ↗
← Back
2ranked-venue papers
2as first author
0since 2021 · last 2002
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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 · 56% Wireless sensing and localization · 44%

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

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
localization algorithms
0.012002
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks · USENIX ATC, General Track 2002
Internet of things and sensor networks
wireless ad hoc and sensor networks
0.012002
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks · USENIX ATC, General Track 2002
Internet of things and sensor networks › wireless sensor network › distributed sensing › networked sensing
distributed sensor networks
0.012002
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks · USENIX ATC, General Track 2002
YearPublicationVenuePosition
2002 Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks
Chris Savarese, Jan M. Rabaey, Koen Langendoen
USENIX ATC, General Track1
2001 Location in distributed ad-hoc wireless sensor networks
abstract
Evolving networks of ad-hoc wireless sensing nodes rely heavily on the ability to establish position information. The algorithms presented herein rely on range measurements between pairs of nodes and the a priori coordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. Major obstacles include overcoming inaccuracies in range measurements as great as /spl plusmn/50%, as well as the development of initial guesses for node locations in clusters with few or no anchor nodes. Solutions to these problems are presented and discussed, using position error as the primary metric. Algorithms are compared according to position error, scalability, and communication and computational requirements. Early simulations yield average position errors of 5% in the presence of both range and initial position inaccuracies.
Chris Savarese, Jan M. Rabaey, Jan Beutel
ICASSP1