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Qingjun Xiao
Researcher at Southeast University
Publications - 58
Citations - 1092
Qingjun Xiao is an academic researcher from Southeast University. The author has contributed to research in topics: Wireless sensor network & Cardinality. The author has an hindex of 18, co-authored 53 publications receiving 861 citations. Previous affiliations of Qingjun Xiao include Georgia State University & Chinese Ministry of Education.
Papers
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Journal ArticleDOI
Reliable Anchor-Based Sensor Localization in Irregular Areas
TL;DR: A novel scheme called reliable anchor-based localization (RAL) is proposed, which can greatly reduce the localization error due to the irregular deployment areas of wireless sensor networks and improve the localization accuracy.
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Multihop Range-Free Localization in Anisotropic Wireless Sensor Networks: A Pattern-Driven Scheme
TL;DR: This paper proposes a pattern-driven localization scheme, inspired by the observation that in an anisotropic network the hop count field propagated from an anchor exhibits multiple patterns, under the interference of multiple anisotrop factors, which can satisfy the needs of many location-dependent protocols and applications, including geographical routing and tracking.
Journal ArticleDOI
A Survey on Security-Aware Measurement in SDN
TL;DR: A systematic survey on security-aware measurement technology in software-defined networking including intradomain and interdomain topology discovering techniques and a general overview of topology measurement in SDN is presented.
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Privacy-Preserving Transportation Traffic Measurement in Intelligent Cyber-physical Road Systems
TL;DR: A novel measurement scheme is proposed, which utilizes bit arrays to collect “masked” data and adopts maximum-likelihood estimation (MLE) to obtain the measurement result and demonstrates the practicality and scalability of the scheme.
Proceedings ArticleDOI
Hyper-Compact Virtual Estimators for Big Network Data Based on Register Sharing
TL;DR: This paper proposes a framework of virtual estimators that allows the idea of sharing to apply to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work.