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• W4294310669 abstract "The real-life deployments of air pollution monitoring systems are sparse, due to large size, high cost, and high-power consumption. Such sparsely deployed sensing stations are unable to provide a fine granular pollution mapping of a given geographical area. By deploying low-cost, low-power, miniature air pollution monitoring sensor nodes, the air pollution map of the whole area can be accurately measured. However, the accuracy of the sensed data of the low-cost miniature multisensing nodes (MSNs) needs to be addressed. This article presents an autocalibration method of low-cost MSNs, with the help of sparsely deployed high-cost sensing stations (HCSSs). The datasets from the HCSSs are collected and used to calibrate the MSN using a suitable learning-based regressor model at the nearby edge node. To this end, this article proposes a cross correlation-based method of determining the optimum time to recalibrate the low-cost sensors in a multisensing node. This method eliminates the requirement of taking the MSNs offline to calibrate/recalibrate them. To apply the proposed autocalibration method, this article additionally presents the design of a low-cost, low-power particulate matter (PM) sensor. To validate the performance of the low-cost PM sensor, the calibrated PM data are compared with the data collected from a colocated commercially available PM sensor, which is considered as a reference. The low-cost PM sensor is 91% more cost-efficient and 57% more energy-efficient compared with the commercial high-cost PM sensor, while maintaining the sensing error within a given threshold." @default.
• W4294310669 created "2022-09-02" @default.
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• W4294310669 date "2022-01-01" @default.
• W4294310669 modified "2023-10-18" @default.
• W4294310669 title "Local Reference-Free In-Field Calibration of Low-Cost Air Pollution Monitoring Sensors" @default.
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• W4294310669 doi "https://doi.org/10.1109/tim.2022.3203446" @default.
• W4294310669 hasPublicationYear "2022" @default.
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