scholarly journals Random sampling in collaborative and distributed mobile sensor networks utilizing compressive sensing for scalar field mapping

2015 ◽  
Author(s):  
Minh Tuan Nguyen ◽  
Keith A. Teague
Keyword(s):  
Sensor Networks ◽  
Scalar Field ◽  
Compressive Sensing ◽  
Random Sampling ◽  
Mobile Sensor Networks ◽  
Mobile Sensor ◽  
Field Mapping

scholarly journals Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2575 ◽  
Author(s):  
Haifeng Zheng ◽  
Jiayin Li ◽  
Xinxin Feng ◽  
Wenzhong Guo ◽  
Zhonghui Chen ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Compressive Sensing ◽  
Mobile Sensor Networks ◽  
Temporal Data ◽  
Mobile Sensor

Relative Distance-Aware Data Delivery Scheme for Delay Tolerant Mobile Sensor Networks

2010 ◽  
Vol 21 (3) ◽  
pp. 490-504 ◽  
Author(s):  
Fu-Long XU ◽  
Ming LIU ◽  
Hai-Gang GONG ◽  
Gui-Hai CHEN ◽  
Jian-Ping LI ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Mobile Sensor Networks ◽  
Relative Distance ◽  
Data Delivery ◽  
Mobile Sensor ◽  
Delay Tolerant

Group Mobility Adaptive Event Delivery Scheme for Delay Tolerant Mobile Sensor Networks

2012 ◽  
Vol 23 (3) ◽  
pp. 629-647 ◽  
Author(s):  
Lei WU ◽  
Xiao-Min WANG ◽  
Ming LIU ◽  
Gui-Hai CHEN ◽  
Hai-Gang GONG
Keyword(s):  
Sensor Networks ◽  
Mobile Sensor Networks ◽  
Mobile Sensor ◽  
Group Mobility ◽  
Delay Tolerant

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Jongeun Choi ◽  
Dejan Milutinović
Keyword(s):  
Sensor Networks ◽  
Feedback Control ◽  
Control Theory ◽  
Mobile Sensor Networks ◽  
Optimal Feedback Control ◽  
Mobile Sensor ◽  
Optimal Feedback ◽  
Spatiotemporal Models ◽  
Feedback Control Theory

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.

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