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Xiangmao Chang

Researcher at Nanjing University of Aeronautics and Astronautics

Publications -  39
Citations -  305

Xiangmao Chang is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Wireless sensor network & Computer science. The author has an hindex of 7, co-authored 34 publications receiving 211 citations. Previous affiliations of Xiangmao Chang include Beijing University of Posts and Telecommunications & Nanjing University.

Papers
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Journal ArticleDOI

An efficient energy hole alleviating algorithm for wireless sensor networks

TL;DR: Experimental results demonstrate that WSNEHPA can efficiently balance the energy consumption of the sensors in the first radius range of the sink, and that the lifetime of the WSNs can be extended efficiently.
Journal ArticleDOI

Sensor Placement Algorithms for Fusion-Based Surveillance Networks

TL;DR: Fast sensor placement algorithms based on a probabilistic data fusion model are presented that can meet the desired detection performance with a small number of sensors while achieving up to seven-fold speedup over the optimal algorithm.
Journal ArticleDOI

Accuracy-Aware Interference Modeling and Measurement in Wireless Sensor Networks

TL;DR: A new regression-based interference model is proposed and analytically characterize its accuracy based on statistics theory and an algorithm is proposed that accurately forecasts the performance of WSNs in the presence of cross-technology interference.
Proceedings ArticleDOI

Aquatic debris monitoring using smartphone-based robotic sensors

TL;DR: The design and implementation of SOAR - a vision-based surveillance robot system that integrates an off-the-shelf Android smartphone and a gliding robotic fish for debris monitoring and the rotation scheduling algorithm enables SOAR to capture debris arrivals with reduced energy consumption.
Journal ArticleDOI

Efficient Coverage Maintenance Based on Probabilistic Distributed Detection

TL;DR: This work proposes a new sensing coverage model based on the distributed detection theory, which captures two important characteristics of sensor networks, i.e., probabilistic detection by individual sensors and data fusion among sensors.