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Long Cheng

Researcher at Clemson University

Publications -  101
Citations -  2304

Long Cheng is an academic researcher from Clemson University. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 22, co-authored 89 publications receiving 1580 citations. Previous affiliations of Long Cheng include Virginia Tech & Nanjing Agricultural University.

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QoS Aware Geographic Opportunistic Routing in Wireless Sensor Networks

TL;DR: This work exploits the geographic opportunistic routing (GOR) for QoS provisioning with both end-to-end reliability and delay constraints in WSNs and proposes an Efficient QoS-aware GOR (EQGOR), characterized by the low time complexity.
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Enterprise data breach: causes, challenges, prevention, and future directions

TL;DR: This review helps interested readers to learn about enterprise data leak threats, recent data leak incidents, various state-of-the‐art prevention and detection techniques, new challenges, and promising solutions and exciting opportunities.
Journal ArticleDOI

R3E: Reliable Reactive Routing Enhancement for Wireless Sensor Networks

TL;DR: The Reliable Reactive Routing Enhancement (R3E) is designed to enhance existing reactive routing protocols to provide reliable and energy-efficient packet delivery against the unreliable wireless links by utilizing the local path diversity.
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Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks

TL;DR: A distributed Minimum-Delay Energy-efficient flooding Tree (MDET) algorithm to construct an energy optimal tree with flooding delay bounding is designed and demonstrated that MDET achieves a comparable delivery latency with the minimum-delay flooding, and incurs only 10% more transmission cost than the lower bound, which yields a good balance between flooding delay and energy efficiency.
Proceedings ArticleDOI

Social-Loc: improving indoor localization with social sensing

TL;DR: Social-Loc is a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the estimation errors.