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Kirill Varshavskiy

dblp:146/0082 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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

Computer networks · 2

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 · 44% Internet of things and sensor networks · 44% Wireless networking · 13%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments › mobile crowdsourcing
crowdsensing
0.212014
Demo: SaveAlert: design for a sensor-driven CrowdWatch danger detection system · MobiSys 2014
Wireless sensing and localization › human sensing › human activity sensing
crowd monitoring
0.212014
Poster: SaveAlert: an efficient and scalable sensor-driven danger detection system · MobiCom 2014
Internet of things and sensor networks › sensor data management
sensor data collection
0.212014
Poster: SaveAlert: an efficient and scalable sensor-driven danger detection system · MobiCom 2014
Ubiquitous computing and smart environments › mobile sensing
smartphone and smartwatch sensing
0.112014
Demo: SaveAlert: design for a sensor-driven CrowdWatch danger detection system · MobiSys 2014

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

sensor fusion · 0.2peer-to-peer aggregation · 0.2adaptive sensing · 0.2
YearPublicationVenuePosition
2014 Poster: SaveAlert: an efficient and scalable sensor-driven danger detection system
abstract
SaveAlert is an adaptive framework for crowd-monitoring and danger-detection using off-the-shelf smartphones and other peripherals such as smartwatches. It is a system that provides users with an increased awareness of their surroundings by detecting and notifying them of impending danger, by relying only on sensor data collected from the users. Our framework's novelty is in how it performs efficient sensor data collection from potentially a large number of people by limiting the disturbance and stress on the existing Wi-Fi and cellular infrastructure. To the best of our knowledge, this is the first crowd-monitoring framework that takes advantage of peer-to-peer connections to perform local aggregation to alleviate the stress on existing infrastructures for better scalability and efficiency.
Güliz Seray Tuncay, Kirill Varshavskiy, Robin Kravets, Klara Nahrstedt
MobiCom2
2014 Demo: SaveAlert: design for a sensor-driven CrowdWatch danger detection system
abstract
SaveAlert is an adaptive framework for danger-detection using existing devices such as off-the-shelf smartphones and smartwatches. The framework can be extended to different sensors that could trigger distress signals to notify nearby users of potentially dangerous situations.
Güliz Seray Tuncay, Kirill Varshavskiy, Robin Kravets
MobiSys2