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Real-time information processing of environmental sensor network data using bayesian gaussian processes

Authors:

Michael A. Osborne,

Stephen J. Roberts,

Nicholas R. JenningsAuthors Info & Claims

ACM Transactions on Sensor Networks (TOSN), Volume 9, Issue 1

Article No.: 1, Pages 1 - 32

https://doi.org/10.1145/2379799.2379800

Published: 30 November 2012 Publication History

Abstract

In this article, we consider the problem faced by a sensor network operator who must infer, in real time, the value of some environmental parameter that is being monitored at discrete points in space and time by a sensor network. We describe a powerful and generic approach built upon an efficient multi-output Gaussian process that facilitates this information acquisition and processing. Our algorithm allows effective inference even with minimal domain knowledge, and we further introduce a formulation of Bayesian Monte Carlo to permit the principled management of the hyperparameters introduced by our flexible models. We demonstrate how our methods can be applied in cases where the data is delayed, intermittently missing, censored, and/or correlated. We validate our approach using data collected from three networks of weather sensors and show that it yields better inference performance than both conventional independent Gaussian processes and the Kalman filter. Finally, we show that our formalism efficiently reuses previous computations by following an online update procedure as new data sequentially arrives, and that this results in a four-fold increase in computational speed in the largest cases considered.

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Dash RJenamani M(2024)Outlier Resilient Online Multivariate Change Point Detection Using Subsequence Divergence Estimation in Sensor Data StreamsIEEE Sensors Journal10.1109/JSEN.2024.346249324:23(39218-39229)Online publication date: 1-Dec-2024
https://doi.org/10.1109/JSEN.2024.3462493
Varga BPereira MKulcsár BPariota LPéni T(2023)Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325102224:6(6268-6279)Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3251022
Wu DXie YQiang Z(2023)An efficient EM algorithm for two-layer mixture model of gaussian process functional regressionsPattern Recognition10.1016/j.patcog.2023.109783143(109783)Online publication date: Nov-2023
https://doi.org/10.1016/j.patcog.2023.109783
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Index Terms

Real-time information processing of environmental sensor network data using bayesian gaussian processes
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 9, Issue 1

November 2012

233 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/2379799

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 30 November 2012

Accepted: 01 August 2011

Revised: 01 April 2011

Received: 01 September 2010

Published in TOSN Volume 9, Issue 1

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Cited By

Dash RJenamani M(2024)Outlier Resilient Online Multivariate Change Point Detection Using Subsequence Divergence Estimation in Sensor Data StreamsIEEE Sensors Journal10.1109/JSEN.2024.346249324:23(39218-39229)Online publication date: 1-Dec-2024
https://doi.org/10.1109/JSEN.2024.3462493
Varga BPereira MKulcsár BPariota LPéni T(2023)Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325102224:6(6268-6279)Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3251022
Wu DXie YQiang Z(2023)An efficient EM algorithm for two-layer mixture model of gaussian process functional regressionsPattern Recognition10.1016/j.patcog.2023.109783143(109783)Online publication date: Nov-2023
https://doi.org/10.1016/j.patcog.2023.109783
Chen XDing XWang XZhao YLiu CLiu HChen K(2022)Multi-Task Data Imputation for Time-Series Forecasting in Turbomachinery Health PrognosticsMachines10.3390/machines1101001811:1(18)Online publication date: 23-Dec-2022
https://doi.org/10.3390/machines11010018
Ahmed HAbdulrazak BBlanchet FAloulou HMokhtari M(2022)Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian ApproachIEEE Access10.1109/ACCESS.2022.321878510(116107-116119)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3218785
Covino STobar FTreves A(2022)Detecting the periodicity of highly irregularly sampled light curves with Gaussian processes: the case of SDSS J025214.67−002813.7Monthly Notices of the Royal Astronomical Society10.1093/mnras/stac596513:2(2841-2849)Online publication date: 7-Mar-2022
https://doi.org/10.1093/mnras/stac596
Wang BZhang JZhang YWang MWang S(2021)Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT NetworkACM Transactions on Sensor Networks10.1145/343231217:3(1-33)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3432312
Yang AWang PYang H(2021) In Situ Blind Calibration of Sensor Networks for Infrastructure Monitoring IEEE Sensors Journal10.1109/JSEN.2021.310927821:21(24274-24284)Online publication date: 1-Nov-2021
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Liu BQi YChen K(2020)Sequential online prediction in the presence of outliers and change points: An instant temporal structure learning approachNeurocomputing10.1016/j.neucom.2020.07.011413(240-258)Online publication date: Nov-2020
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Coluccia AFascista A(2019)A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor NetworksSensors10.3390/s1905098819:5(988)Online publication date: 26-Feb-2019
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