Environmental Sensor Placement with Convolutional Gaussian Neural Processes

• URL: http://arxiv.org/abs/2211.10381v5
• Date: Mon, 15 May 2023 17:03:27 GMT
• Title: Environmental Sensor Placement with Convolutional Gaussian Neural Processes
• Authors: Tom R. Andersson, Wessel P. Bruinsma, Stratis Markou, James Requeima, Alejandro Coca-Castro, Anna Vaughan, Anna-Louise Ellis, Matthew A. Lazzara, Dani Jones, J. Scott Hosking, Richard E. Turner
• Abstract summary: It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
• Score: 65.13973319334625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

Related papers

• Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Air Quality Sensor Fusion [6.963971634605796]
  We propose a novel unsupervised domain adaptation (UDA) method specifically tailored for regression tasks on graph-structured data. We incorporate spatial-temporal graph neural networks (STGNNs) to model the relationships between sensors. Our approach allows low-cost IoT sensors to learn calibration parameters from expensive reference sensors.
  arXiv  Detail & Related papers  (2024-11-11T12:20:57Z)
• A physics-driven sensor placement optimization methodology for temperature field reconstruction [9.976807723785006]
  We propose a novel physics-driven sensor placement optimization (PSPO) method for temperature field reconstruction. The PSPO method significantly outperforms random and uniform selection methods, improving the reconstruction accuracy by nearly an order of magnitude.
  arXiv  Detail & Related papers  (2024-09-27T03:26:38Z)
• Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics [8.715570103753697]
  Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. We propose a Heterogeneous Temporal Graph Neural Network (HTGNN) framework to address this problem. HTGNN explicitly models signals from diverse sensors and integrates operating conditions into the model architecture.
  arXiv  Detail & Related papers  (2024-07-26T12:16:53Z)
• Data-Induced Interactions of Sparse Sensors [3.050919759387984]
  We take a thermodynamic view to compute the full landscape of sensor interactions induced by the training data. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts.
  arXiv  Detail & Related papers  (2023-07-21T18:13:37Z)
• Detection of Sensor-To-Sensor Variations using Explainable AI [2.2956649873563952]
  chemi-resistive gas sensing devices are plagued by issues of sensor variations during manufacturing. This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP) The methodology is tested using artificial and realistic Ozone concentration profiles to train a Gated Recurrent Unit (GRU) model.
  arXiv  Detail & Related papers  (2023-06-19T11:00:54Z)
• Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration [55.41644538483948]
  This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. It contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format. The focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data.
  arXiv  Detail & Related papers  (2023-04-27T20:21:18Z)
• Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields [58.720142291102135]
  We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements. We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands. We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
  arXiv  Detail & Related papers  (2022-06-28T12:43:38Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.