Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Online Environmental Field Reconstruction in Space and Time

  • Yunfei Xu
  • Jongeun Choi
  • Sarat Dass
  • Tapabrata Maiti

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Also part of the SpringerBriefs in Control, Automation and Robotics book sub series (BRIEFSCONTROL)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 1-9
  3. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 11-18
  4. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 19-26
  5. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 27-52
  6. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 53-75
  7. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 77-90
  8. Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti
    Pages 91-106
  9. Back Matter
    Pages 107-115

About this book

Introduction

This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.

Keywords

Adaptive Sampling Bayesian Inference Distibuted Control Distributed Algorithms Environmental Monitoring Gaussiaan Processes Markov Random Fields Mobile Networks Plume Tracking

Authors and affiliations

  • Yunfei Xu
    • 1
  • Jongeun Choi
    • 2
  • Sarat Dass
    • 3
  • Tapabrata Maiti
    • 4
  1. 1.Department of Mechanical EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Department of Mechanical EngineeringMichigan State UniversityEast LansingUSA
  3. 3.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA
  4. 4.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21921-9
  • Copyright Information The Author(s) 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-21920-2
  • Online ISBN 978-3-319-21921-9
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • About this book
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