A template framework for environmental timeseries data acquisition

Samourkasidis, Argyrios; Papoutsoglou, Evangelia; Athanasiadis, Ioannis N.

doi:10.1016/j.envsoft.2018.10.009

Cited by 10 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A removable data storage simplifies data collection process by just changing the microSD card from device and then retrieve the logs in a more comfortable place and time by computer operations. In addition, the data captured must be able to be saved in a physical and logical format so that they can be easily integrated into systems with FAIR policies as defined in [32]. For low-power consumption during sleep mode, a P-channel MOSFET deliver controlled power to the microSD card only when a read / write process is requested by the MCU (controlled by firmware).…”

Section: Data Storagementioning

confidence: 99%

CEAZA mega board: an open-source data logger for scientists

Gallardo

Nelson²,

MacDonell³

2022

IJRES

View full text Add to dashboard Cite

<p>Over the last decade many researchers have taken advantage of the technology boom related to the launch of the Arduino platform to make their own datalogging devices. Many of these developments ended with the first functional prototypes in which multiple electronic boards are mixed by wiring/soldering and then used in datalogging activities. In this study we present a new, simple, robust, and expandable datalogger board based on maker’s community integrations. Our datalogger board extends previous work in this area as we designed an Arduino Mega 2560 derivative integrated board that is compatible with existing developments but was also designed and implemented considering requirements such as low power consumption, expandability, and integration. Different tests were made so reliability in low temperatures and low energy needs are satisfied. Is expected that the scientific community can add this board to their tool set, as this board solves the energy problem and present an easy transition from handmade logger integrations.</p>

show abstract

Section: Data Storagementioning

confidence: 99%

CEAZA mega board: an open-source data logger for scientists

Gallardo

Nelson²,

MacDonell³

2022

IJRES

View full text Add to dashboard Cite

show abstract

“…If one considers monitoring of six indoor parameters at a minute sampling frequency, it will have 8640 samples in one day per location. Considering a network with multiple locations, it brings challenges to data management and processing [106][107][108].…”

Section: Pre-processing Of Low-cost Sensor (Lcs) Datamentioning

confidence: 99%

Low-Cost Air Quality Sensing towards Smart Homes

et al. 2021

View full text Add to dashboard Cite

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

show abstract

“…The simplest data integration solutions provide only syntactic integration [76] of the data sources, enabling uniform access to heterogeneous model paradigms and formats. Thus, it is common to generate RDF data from stored relational data, since many datasets are already available in relational format.…”

Section: Smart Environmental Data Integrationmentioning

confidence: 99%

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

et al. 2020

View full text Add to dashboard Cite

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains.

show abstract

A template framework for environmental timeseries data acquisition

Cited by 10 publications

References 23 publications

CEAZA mega board: an open-source data logger for scientists

CEAZA mega board: an open-source data logger for scientists

Low-Cost Air Quality Sensing towards Smart Homes

Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

Contact Info

Product

Resources

About