Stochastic adaptive sampling for mobile sensor networks using kernel regression

2010 ◽  
Author(s):  
Yunfei Xu ◽  
Jongeun Choi
Keyword(s):  
Sensor Networks ◽  
Adaptive Sampling ◽  
Kernel Regression ◽  
Mobile Sensor Networks ◽  
Mobile Sensor

Mobile Sensor Networks and Control: Adaptive Sampling of Spatiotemporal Processes

2020 ◽  
Vol 3 (1) ◽  
pp. 91-114
Author(s):  
Derek A. Paley ◽  
Artur Wolek
Keyword(s):  
Sensor Networks ◽  
Autonomous Vehicles ◽  
Adaptive Sampling ◽  
Mobile Sensor Networks ◽  
Discrete Models ◽  
Cooperative Groups ◽  
Mobile Sensor ◽  
Ambient Flow ◽  
Fluid Sensor ◽  
Wide Range

The control of mobile sensor networks uses sensor measurements to update a model of an unknown or estimated process, which in turn guides the collection of subsequent measurements—a feedback control framework called adaptive sampling. Applications for adaptive sampling exist in a wide range of settings, especially for unmanned or autonomous vehicles that can be deployed cheaply and in cooperative groups. The dynamics of mobile sensor platforms are often simplified to planar self-propelled particles subject to the ambient flow of the surrounding fluid. Sensor measurements are assimilated into continuous or discrete models of the process of interest, which in general can vary in space and time. The variability of the estimated process is one metric to score future candidate sampling trajectories, along with information- and uncertainty-based metrics. Sampling tasks are allocated to the network using centralized or decentralized optimization, in order to avoid redundant measurements and observational gaps.

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