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Sunlight incident on the Earth’s atmosphere is essential for life, and it is the driving force of a host of photo-chemical and environmental processes, such as the radiative heating of the atmosphere. We report the description and application of a physical methodology relative to how an ensemble of very low-cost sensors (with a total cost of <$20,...
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... the sunlight passes through the Earth's atmosphere, it is absorbed and scattered by various atmospheric components related to the path length through the atmosphere (Figure 1), which is a function of the solar zenith angle (Figure 2). The solar irradiance incident on the Earth's surface during the daytime is a function of the Earth's distance from the sun [15] (varies with season due to the ellipse orbit [16]), the solar zenith angle (can be calculated from latitude, longitude and the local solar time), the vertical profile of atmospheric light scatterers and absorbers and the surface reflectivity. ...
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... FreeDSM, however, is equipped with a digital sensor of illuminance TSL2591 (digital, 16bit) which uses two photodiodes (infrared radiation and both infrared and visible spectrum). These independent measurements can be subtracted, easily eliminating the influence of infrared radiation without any filter (like TSL237 devices) decreasing the cost of the device, keeping results very accurate and comparable to analog sensors [20]. In parallel to this development, that was started in 2021 and was initially based on a Raspberry and this same sensor, we have seen that it is being used in numerous projects that already incorporate recent lowcost communication technologies such as LoRa [7] or large-scale sky brightness measurement campaigns [5], which further supports our choice. ...
Light pollution is one of the fastest growing environmental problems in recent years, mainly affecting urbanized areas worldwide. It goes beyond the difficulty of the astronomical observation, causing a deep impact on the balance of ecosystems, wildlife and human health. To deal with this complex situation, it is necessary to increase public awareness and to demand from all implicated stakeholders efficient technical solutions to stop and mitigate unwanted light pollution effects. A continous monitoring of light pollution levels is also mandatory. In this context, we propose a new device called FreeDSM, an IoT based photometer to measure the quality of the dark sky at night. FreeDSM will be easy-to-use and easy-to-build, so every interested citizen can create their own and collaborate with the project. The main component of the photometer is an ESP32C3 microcontroller, a cheap, small, low consumption and powerful chip. Also, a TSL2591 optical sensor, of high sensitivity and large dynamic range, is included. Thanks to the proposed design, additional positioning or ambiance sensors could be incorporated. To handle all these components, the ESP32 firmware is implemented through Tasmota, an open-source home assistant solution for most of commercial sensors. And all the information to be gathered by the FreeDSM devices will be made public via a platform built using FIWARE, an open framework for IoT solutions. By doing so, this solution aims to standarization of IoT collected data, easing users own new applications development and public awareness increase of the problem. Our proposed approach is expected to empower individuals to take action against unnecessary outdoor lightning.
... Machine Learning (ML) algorithms [13] can learn the complex relationships between sensor measurements and environmental variables. Techniques such as random forest (RF) [14], k-nearest neighbors (kNN) [15,16], and Artificial Neural networks (ANN) [8,11] have been successfully applied to calibrate air pollutant sensors, improving their performance [17,18,19]. However, state-of-the-art studies [20] predominantly date back to the 20'which introduces an observation bias. ...
Low-cost sensors (LCS) have emerged as promising tools for air quality measurements, finding applications in indoor and outdoor monitoring situations. However, ensuring the reliability and accuracy of LCS measurements requires a crucial calibration step. This study aims to investigate the suit-ability of different outdoor calibration models for multivariate regression tasks, intending to enhance the calibration in the presence of pollutant interferences, and proposes a unique and comprehensive experimental setup. We show that incorporating multiple pollutant variables and meteorological data accounts for interferences. An outdoor evaluation assesses the performance and effectiveness of various machine learning calibration models in calibrating the sensors. This calibration pipeline applies to many sensor calibration situations.
... Further improvements to the device will include development of a custom board, the addition of additional environmental sensors and alternative, more advanced light sensors, including biosensors [25][26][27]. ...
Light pollution is an ongoing problem for city populations. Large numbers of light sources at night negatively affect humans’ day–night cycle. It is important to measure the amount of light pollution in order to effectively ascertain the amount of light pollution in the city area and effectively reduce it where possible and necessary. In order to perform this task, a prototype wireless sensor network for automated, long-term measurement of light pollution was developed for the Torun (Poland) city area. The sensors use LoRa wireless technology to collect sensor data from an urban area by way of networked gateways. The article investigates the sensor module architecture and design challenges as well as network architecture. Example results of light pollution measurements are presented, which were obtained from the prototype network.
... As Zhang et al. [51] explain, the algorithm can be described as follows: in the feature space of dimension N, we find a direction that maximizes the variance of the data. We then use that direction as our first principal direction and project the data onto the N − 1 dimension space, removing the principal direction. ...
Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.
Light pollution has notoriously become the environmental problem that has grown the most in modern times, especially in highly populated areas and developed countries. It is a very serious worldwide issue, altering the natural day-night life cycle and, therefore, affecting the well-being of a large number of living species, including human beings. Indeed, some serious health diseases have been clearly linked to the incidence of light pollution. And, not to be forgotten, unnecessary outdoor lighting levels cause an enormous waste of energy linked to fossil fuel combustion and the aggravation of climate change. In this context, rising public awareness is key to addressing the true ecological, energetic, or medical dimensions of the problem; also, a technical global solution is demanded from and implemented by the implied administrations and stakeholders. It is in this context that light pollution levels should be massively measured at schools, homes, parks or hospital roofs, ideally by a low-cost, understandable and user-friendly device, and preferably controllable from our smartphones. Our proposal aims to cover this need by spreading and facilitating the use of FreeDSM, an open IoT device for citizen light pollution monitoring. FreeDSM is based on a Raspberry Pi Zero, a cheap and low consumption device, in which different components are integrated. The main component is the TSL2591, an optical light sensor that covers the wavelength range 300–1000 nm and a great dynamic range: 600,000,000:1. Other optional components can be incorporated to include ambient data, positioning and data connectivity. All the registered devices will be connected in a public access service, gathering the information by means of Fiware, an open framework for IoT environments.KeywordsLight pollutionNatural light skyPhotometry
