Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices

• URL: http://arxiv.org/abs/2003.12011v1
• Date: Tue, 24 Mar 2020 10:26:51 GMT
• Title: Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices
• Authors: Saverio De Vito, Girolamo Di Francia, Elena Esposito, Sergio Ferlito, Fabrizio Formisano and Ettore Massera
• Abstract summary: Low cost chemical microsensors array have shown capable to provide relatively accurate air pollutant quantitative estimations. Their accuracy have shown limited in long term field deployments due to negative influence of several technological issues. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models.
• Score: 1.957338076370071
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and note-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance.

Related papers

• A task of anomaly detection for a smart satellite Internet of things system [0.9427635404752934]
  This paper proposes an unsupervised deep learning anomaly detection system. Based on the generative adversarial network and self-attention mechanism, it automatically learns the complex linear and nonlinear dependencies between environmental sensor variables. It can monitor the abnormal points of real sensor data with high real-time performance and can run on the intelligent satellite Internet of things system.
  arXiv  Detail & Related papers  (2024-03-21T14:26:29Z)
• Unsupervised Learning for Fault Detection of HVAC Systems: An OPTICS -based Approach for Terminal Air Handling Units [1.0878040851638]
  This study introduces an unsupervised learning strategy to detect faults in terminal air handling units and their associated systems. The methodology involves pre-processing historical sensor data using Principal Component Analysis to streamline dimensions. Results showed that OPTICS consistently surpassed k-means in accuracy across seasons.
  arXiv  Detail & Related papers  (2023-12-18T18:08:54Z)
• A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments [0.5779598097190628]
  We propose a zero transfer samples, global calibration methodology as a technological enabler for IoT AQ multisensory devices. This work is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning concepts. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices.
  arXiv  Detail & Related papers  (2023-10-27T13:04:53Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments [67.24598072875744]
  Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies. This can hamper dramatically subsequent decision-making, such as forecasting.
  arXiv  Detail & Related papers  (2022-06-15T19:46:59Z)
• Cloud Failure Prediction with Hierarchical Temporary Memory: An Empirical Assessment [64.73243241568555]
  Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex. This paper presents the first systematic study that assesses HTM in the context of failure prediction.
  arXiv  Detail & Related papers  (2021-10-06T07:09:45Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants [1.2114524594104759]
  We propose MAQ-CaF, a modular air quality calibration, and forecasting methodology. It side-steps the challenges of unreliability through its modular machine learning-based design. It stores the calibrated data both locally and remotely with an added feature of future predictions.
  arXiv  Detail & Related papers  (2021-04-22T13:34:06Z)
• Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [53.38353133198842]
  Air quality significantly affects human health, it is increasingly important to accurately and timely predict the Air Quality Index (AQI) This paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.
  arXiv  Detail & Related papers  (2020-07-23T13:32:47Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.