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.

Spatial Prediction With Mobile Sensor Networks Using Gaussian Process Regression Based on Gaussian Markov Random Fields

2011 ◽  
Author(s):  
Yunfei Xu ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Markov Random Fields ◽  
Mobile Sensor Networks ◽  
Spatial Prediction ◽  
Prediction Algorithm ◽  
Mobile Sensor ◽  
Covariance Functions ◽  
Wide Range

In this paper, a new class of Gaussian processes is proposed for resource-constrained mobile sensor networks. Such a Gaussian process builds on a GMRF with respect to a proximity graph over a surveillance region. The main advantages of using this class of Gaussian processes over standard Gaussian processes defined by mean and covariance functions are its numerical efficiency and scalability due to its built-in GMRF and its capability of representing a wide range of non-stationary physical processes. The formulas for Bayesian posterior predictive statistics such as prediction mean and variance are derived and a sequential field prediction algorithm is provided for sequentially sampled observations. For a special case using compactly supported kernels, we propose a distributed algorithm to implement field prediction by correctly fusing all observations in Bayesian statistics. Simulation results illustrate the effectiveness of our approach.

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.

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

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.

scholarly journals Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

Sensors ◽  
2011 ◽  
Vol 11 (3) ◽  
pp. 3051-3066 ◽  
Author(s):  
Yunfei Xu ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Gaussian Processes ◽  
Adaptive Sampling ◽  
Mobile Sensor Networks ◽  
Mobile Sensor
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