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Husheng Li

Researcher at University of Tennessee

Publications -  315
Citations -  6622

Husheng Li is an academic researcher from University of Tennessee. The author has contributed to research in topics: Cognitive radio & Smart grid. The author has an hindex of 42, co-authored 282 publications receiving 5957 citations. Previous affiliations of Husheng Li include Sequans & Qualcomm.

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Journal ArticleDOI

Indoor Positioning Systems Based on Visible Light Communication: State of the Art

TL;DR: This paper surveys over 100 papers ranging from pioneering papers to the state-of-the-art in the field to present the positioning technology, and emphasizes and analyzes the accuracy of VLC-based-IPS in the experiment and simulation environments.
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Time Synchronization Attack in Smart Grid: Impact and Analysis

TL;DR: A novel Time Synchronization Attack (TSA) is proposed to attack the timing information in smart grid and the effectiveness of TSA is demonstrated for three applications of phasor measurement unit (PMU) in smartgrid, namely transmission line fault detection, voltage stability monitoring and event locationing.
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Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

TL;DR: This work proposes two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports and validate the effectiveness and robustness of these approaches.
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Multicast Routing for Decentralized Control of Cyber Physical Systems with an Application in Smart Grid

TL;DR: The challenges of uncertain destinations and multiple routing modes, which are significantly different from traditional data networks, are addressed by employing the theories of hybrid systems and linear matrix inequalities, thus forming a novel framework for studying the communication sub-system in cyber physical systems.
Proceedings ArticleDOI

Attack-proof collaborative spectrum sensing in cognitive radio networks

TL;DR: A malicious user detection algorithm is developed that calculates the suspicious level of secondary users based on their past reports, and the proposed trust value indicator can effectively differentiate honest and malicious secondary users.