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Gang Qu

Researcher at University of Maryland, College Park

Publications -  269
Citations -  8330

Gang Qu is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Physical unclonable function & Energy consumption. The author has an hindex of 42, co-authored 257 publications receiving 7017 citations. Previous affiliations of Gang Qu include Zhejiang University & University of California, Los Angeles.

Papers
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Proceedings ArticleDOI

Exposure in wireless Ad-Hoc sensor networks

TL;DR: This work has developed an efficient and effective algorithm for exposure calculation in sensor networks, specifically for finding minimal exposure paths and provides an unbounded level of accuracy as a function of run time and storage.
Journal ArticleDOI

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

TL;DR: This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, and focuses on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms.
Proceedings ArticleDOI

Power optimization of variable voltage core-based systems

TL;DR: The design methodology for the low power core-based real-time system-on-chip based on dynamically variable voltage hardware is developed, with the highlight of the proposed approach the non-preemptive scheduling heuristic which results in solutions very close to optimal ones for many test cases.
Journal ArticleDOI

Power optimization of variable-voltage core-based systems

TL;DR: This work developed the design methodology for the low-power core-based real-time SOC based on dynamically variable voltage hardware and proposes a nonpreemptive scheduling heuristic, which results in solutions very close to optimal ones for many test cases.
Journal ArticleDOI

A Privacy-Preserving Trust Model Based on Blockchain for VANETs

TL;DR: A blockchain-based anonymous reputation system (BARS) is proposed to establish a privacy-preserving trust model for VANETs and the results show that BARS is able to established a trust model with transparency, conditional anonymity, efficiency, and robustness for VIANETs.