H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network

Barceló-Ordinas, José M.; Ferrer-Cid, Pau; García-Vidal, Jorge; Viana, Mar; Ripoll, A.

doi:10.1016/j.dib.2021.107127

Cited by 16 publications

(11 citation statements)

References 6 publications

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“… [7] . These nodes were deployed in testbeds in Spain and Italy, and allowed the development of sensor calibration studies, in addition to publishing the data openly for other researchers to perform their studies [8] . As a continuation of the nodes that measured with MOX sensor technology, we have developed two prototype nodes in which we could connect the electrochemical (EQ) sensors in various conditions that would allow us to decide the working parameters of a node in the final deployment.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors

2022

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors

2022

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“…2) Spanish O 3 reference station network for Catalonia: this data set is formed by forty-three nodes in the area of Catalonia capturing hourly tropospheric ozone data between the months of January and February of 2021, with a total of 1076 samples. 3) H2020 Captor network [35]: this data set is formed by eight nodes, five low-cost sensors and three reference stations, deployed in the area of Catalonia (Spain) during the summer of 2017 to capture half-hourly tropospheric ozone concentration levels. This data set has a total of 2368 samples.…”

Section: Outlier Detection: Residual-basedmentioning

confidence: 99%

“…We call this algorithm Volterra graph-based outlier detection (VGOD). We perform several experiments on networks of reference stations in Spain measuring tropospheric ozone (O 3 ), as well as a sensor drift detection experiment using a small heterogeneous sensor network involving high-cost instrumentation and lowcost sensors, deployed in the Captor H2020 project [35]. Specifically, in this article we:…”

Section: Introductionmentioning

confidence: 99%

Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks

Ferrer-Cid

Barceló-Ordinas

García-Vidal

2022

IEEE Trans. Netw. Sci. Eng.

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Today's air pollution sensor networks pose new challenges given their heterogeneity of low-cost sensors and highcost instrumentation. Recently, with the advent of graph signal processing, sensor network measurements have been successfully represented by graphs depicting the relationships between sensors. However, one of the main problems of these sensor networks is their reliability, especially due to the inclusion of low-cost sensors, so the detection and identification of outliers is extremely important for maintaining the quality of the network data. In order to better identify the outliers of the sensors composing a network, we propose the Volterra graph-based outlier detection (VGOD) mechanism, which uses a graph learned from data and a Volterra-like graph signal reconstruction model to detect and localize abnormal measurements in air pollution sensor networks. The proposed unsupervised decision process is compared with other outlier detection methods, state-of-theart graph-based methods and non-graph-based methods, showing improvements in both detection and localization of anomalous measurements, so that anomalous measurements can be corrected and malfunctioning sensors can be replaced.

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“…Their applicability and effectiveness in solving specific monitoring and local decision support tasks are astonishing [ 55 ] compared to conventional methods. Most of these systems rely on simple sensory signal filtering methods [ 10 ], while others use AI-based solutions [ 21 , 56 ], deep learning [ 57 ], and other machine learning methods [ 58 ] to filter noises in the raw data [ 59 ], classifying events [ 60 ], detecting patterns [ 61 ], eliminating cyber threats in communication networks [ 60 , 62 , 63 ], compressing the data [ 64 ], etc.…”

Section: Review Of Recent Advancementsmentioning

confidence: 99%

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Jakovlev

Eglynas

Vozňák

et al. 2022

Sensors

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Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems.

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H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network

Cited by 16 publications

References 6 publications

Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors

Raw data collected from NO2, O3 and NO air pollution electrochemical low-cost sensors

Volterra Graph-Based Outlier Detection for Air Pollution Sensor Networks

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

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