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

Matt Sayler

dblp:51/7891 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2009
—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
Wireless sensing and localization · 100%

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

TopicWeightPapersLastEvidence papers
Wireless sensing and localization › localization algorithms
energy-efficient localization
0.112009
EnLoc: Energy-Efficient Localization for Mobile Phones · INFOCOM 2009
Wireless sensing and localization › smartphone sensing
smartphone-based localization
0.112009
EnLoc: Energy-Efficient Localization for Mobile Phones · INFOCOM 2009

Methods — techniques the papers use, named apart from their topics

prediction-based heuristics · 0.1
YearPublicationVenuePosition
2009 EnLoc: Energy-Efficient Localization for Mobile Phones
abstract
A growing number of mobile phone applications utilize physical location to express the context of information. Most of these location-based applications assume GPS capabilities. Unfortunately, GPS incurs an unacceptable energy cost that can reduce the phone's battery life to less than nine hours. Alternate localization technologies, based on WiFi or GSM, improve battery life at the expense of localization accuracy. This paper quantifies this important tradeoff that underlies a range of emerging services. Driven by measurements from Nokia N95 phones, we develop an energy-efficient localization framework called EnLoc. The framework characterizes the optimal localization accuracy for a given energy budget, and develops prediction- based heuristics for real-time use. Evaluation on traces from real users demonstrates the possibility of achieving good localization accuracy for a realistic energy budget.
Ionut Constandache, Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury, Landon P. Cox
INFOCOM3