scholarly journals Spatio-Temporal River Contamination Measurements with Electrochemical Probes and Mobile Sensor Networks

2018 ◽  
Vol 10 (5) ◽  
pp. 1449 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Sergio Muñoz-Romero ◽  
Luis Cumbal ◽  
José Rojo-Álvarez
Keyword(s):  
Sensor Networks ◽  
Mobile Sensor Networks ◽  
Mobile Sensor ◽  
River Contamination ◽  
Spatio Temporal

Distributed Navigation Strategy of Mobile Sensor Networks With Probabilistic Wireless Communication Links

2015 ◽  
Author(s):  
Aqeel Madhag ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Wireless Communication ◽  
Prediction Error ◽  
Mobile Sensor Networks ◽  
Gaussian Random Fields ◽  
Communication Model ◽  
Mobile Sensor ◽  
Communication Links ◽  
Spatio Temporal ◽  
Physical Phenomena

Mobile sensor networks have been widely used to predict spatio-temporal physical phenomena for various scientific and engineering applications. To accommodate the realistic models of mobile sensor networks, we incorporated probabilistic wireless communication links based on packet reception ratio (PRR) with distributed navigation. We then derived models of mobile sensor networks that predict Gaussian random fields from noise-corrupted observations under probabilistic wireless communication links. For the given model with probabilistic wireless communication links, we derived the prediction error variances for further sampling locations. Moreover, we designed a distributed navigation that minimizes the network cost function formulated in terms of the derived prediction error variances. Further, we have shown that the solution of distributed navigation with the probabilistic wireless communication links for mobile sensor networks are uniformly ultimately bounded with respect to that of the distributed one with the R-disk communication model. According to Monte Carlo simulation results, agent trajectories under distributed navigation with the probabilistic wireless communication links are similar to those with the R-disk communication model, which confirming the theoretical analysis.

Optimal Coordination of Mobile Sensor Networks Using Gaussian Processes

2009 ◽  
Author(s):  
Yunfei Xu ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Gaussian Processes ◽  
Mobile Sensor Networks ◽  
Mobile Sensor ◽  
Covariance Functions ◽  
Limited Memory ◽  
Optimal Coordination ◽  
Spatio Temporal ◽  
Nonparametric Prediction ◽  
Navigation Strategies

In this paper, we introduce a family of spatio-temporal Gaussian processes specified by a class of covariance functions. Nonparametric prediction based on truncated observations is proposed for mobile sensor networks with limited memory and computational power. We show that there is a trade-off between precision and efficiency when prediction based on truncated observations is used. Next, we propose both centralized and distributed navigation strategies for mobile sensor networks to move in order to reduce prediction error variances at positions of interest. Simulation results demonstrate the effectiveness of the proposed schemes.

A Distributed Navigation Strategy for Mobile Sensor Networks With the Probabilistic Wireless Links

2017 ◽  
Vol 139 (3) ◽  
Author(s):  
Aqeel Madhag ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Wireless Communication ◽  
Prediction Error ◽  
Mobile Sensor Networks ◽  
Gaussian Random Fields ◽  
Communication Model ◽  
Mobile Sensor ◽  
Communication Links ◽  
Spatio Temporal ◽  
Physical Phenomena

Mobile sensor networks have been widely used to predict the spatio-temporal physical phenomena for various scientific and engineering applications. To accommodate the realistic models of mobile sensor networks, we incorporated probabilistic wireless communication links based on packet reception ratio (PRR) with distributed navigation. We then derived models of mobile sensor networks that predict Gaussian random fields from noise-corrupted observations under probabilistic wireless communication links. For the given model with probabilistic wireless communication links, we derived the prediction error variances for further sampling locations. Moreover, we designed a distributed navigation that minimizes the network cost function formulated in terms of the derived prediction error variances. Further, we have shown that the solution of distributed navigation with the probabilistic wireless communication links for mobile sensor networks are uniformly ultimately bounded with respect to that of the distributed one with the R-disk communication model. According to Monte Carlo simulation results, agent trajectories under distributed navigation with the probabilistic wireless communication links are similar to those with the R-disk communication model, which confirming the theoretical analysis.

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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