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Junbei Zhang

dblp:161/9648 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved

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%
Software engineering, system software, and programming languages
1 paper
Concurrent programming · 100%
Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%

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

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › industrial iot
internet of things
0.212015
SIFT: building an internet of safe things · IPSN 2015
Concurrent programming › concurrency control
conflict detection
0.212015
SIFT: building an internet of safe things · IPSN 2015
Ubiquitous computing and smart environments › smart home
home automation
0.112015
SIFT: building an internet of safe things · IPSN 2015

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

policy verification · 0.7declarative programming · 0.7
YearPublicationVenuePosition
2015 SIFT: building an internet of safe things
abstract
As the number of connected devices explodes, the use scenarios of these devices and data have multiplied. Many of these scenarios, e.g., home automation, require tools beyond data visualizations, to express user intents and to ensure interactions do not cause undesired effects in the physical world. We present SIFT, a safety-centric programming platform for connected devices in IoT environments. First, to simplify programming, users express high-level intents in declarative IoT apps. The system then decides which sensor data and operations should be combined to satisfy the user requirements. Second, to ensure safety and compliance, the system verifies whether conflicts or policy violations can occur within or between apps. Through an office deployment, user studies, and trace analysis using a large-scale dataset from a commercial IoT app authoring platform, we demonstrate the power of SIFT and highlight how it leads to more robust and reliable IoT apps.
Chieh-Jan Mike Liang, Börje Karlsson 0001, Nicholas D. Lane, Feng Zhao 0001, Junbei Zhang, Zheyi Pan, Yong Yu 0001
IPSN5