Ziguo Zhong
University of Minnesota
36 Papers
203 Citations
Ziguo Zhong is an academic researcher from University of Minnesota. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 15, co-authored 36 publications. Previous affiliations of Ziguo Zhong include Southeast University & Texas Instruments.
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Papers
Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks
TL;DR: FIND is proposed, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections and shows that average ranking difference is a provable indicator of possible data faults.
FIND: faulty node detection for wireless sensor networks
Shuo Guo,Ziguo Zhong,Tian He +2 more
- 04 Nov 2009
TL;DR: FIND is proposed, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections and shows that average ranking difference is a provable indicator of possible data faults.
Tracking with Unreliable Node Sequences
Ziguo Zhong,Ting Zhu,Dan Wang,Tian He +3 more
- 19 Apr 2009
TL;DR: This paper proposes a robust tracking framework using node sequences, an ordered list extracted from unreliable sensor readings, which provides a useful layer of abstraction, making the design framework generic and compatible with different physical sensing modalities.
Asymmetrical Round Trip Based Synchronization-Free Localization in Large-Scale Underwater Sensor Networks
TL;DR: An asymmetrical round trip based localization (ARTL) algorithm is proposed in this paper that has low computational complexity and excellent scalability, and can achieve highly accurate ranging in large-scale UWSNs.
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MSP: multi-sequence positioning of wireless sensor nodes
Ziguo Zhong,Tian He +1 more
- 06 Nov 2007
TL;DR: This evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution, and provides a nice tradeoff between physical cost (anchors) and soft cost (events), while maintaining localization accuracy.