IOT based air quality and particulate matter concentration monitoring system

Kalia, Puneet; Ansari, Mamtaz Alam

doi:10.1016/j.matpr.2020.02.179

Cited by 43 publications

(24 citation statements)

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“…It is also used in IoT applications. There are also several investigations in which they have worked with this technology in different scientific areas [ 75 , 76 , 77 , 78 , 79 ].…”

Section: Methodsmentioning

confidence: 99%

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

García

Ponluisa

Quiles

et al. 2022

Sensors

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This work proposes a method for real-time supervision and predictive fault diagnosis applicable to solar panel strings in real-world installations. It is focused on the detection and parametric isolation of fault symptoms through the analysis of the Voc-Isc curves. The method performs early, systematic, online, automatic, permanent predictive supervision, and diagnosis of a high sampling frequency. It is based on the supervision of predictive electrical parameters easily accessible by the design of its architecture, whose detection and isolation precedes with an adequate margin of maneuver, to be able to alert and stop by means of automatic disconnection the degradation phenomenon and its cumulative effect causing the development of a future irrecoverable failure. Its architecture design is scalable and integrable in conventional photovoltaic installations. It emphasizes the use of low-cost technology such as the ESP8266 module, ASC712-5A, and FZ0430 sensors and relay modules. The method is based on data acquisition with the ESP8266 module, which is sent over the internet to the computer where a SCADA system (iFIX V6.5) is installed, using the Modbus TCP/IP and OPC communication protocols. Detection thresholds are initially obtained experimentally by applying inductive shading methods on specific solar panels.

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“…It is also used in IoT applications. There are also several investigations in which they have worked with this technology in different scientific areas [ 75 , 76 , 77 , 78 , 79 ].…”

Section: Methodsmentioning

confidence: 99%

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

García

Ponluisa

Quiles

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Recent studies have proposed air quality monitoring systems based on IoT platforms that provide a high number of requested parameters in real-time. The systems for air quality measurement are often cloud-based, using centralized network storage and computational power, regardless of the amount and origin of data [20,21]. The data is collected from different low-cost sensors, transmitted by a low-power and long-range communication protocol to the core of the network [20,22].…”

Section: Related Workmentioning

confidence: 99%

“…The selection of viewpoints, used for the classification of reviewed system solutions, was performed to reveal the critical aspects regarding the research approach, research directions and intended applications of the proposed solutions. The majority of reviewed system solutions given in Table 1 [20][21][22][23][24][25][26]35,36,40,43] utilize a sensor-cloud approach given as an infrastructure that allows persistent system operation using sensors as an interface between physical and cyber worlds, the cloud computing services as the cyber backbone and the internet as the communication medium. Although architecture review can be found as a part of the associated research papers [22,35,36], the research novelty in these papers is more often related to the hardware design of sensing devices and algorithms for data processing.…”

Section: Related Workmentioning

confidence: 99%

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Popović

Radovanović

Vajs

et al. 2022

Sensors

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Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

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“…In the specific case of monitoring systems for buildings, Table 1 contains the studies developed in recent years based on IoT technology. According to the table, most of the studies carried out are based on monitoring indoor environmental parameters [17][18][19][20][21][22][23][24][25][26][27][28], the most interesting being the temperature and relative humidity inside the enclosure. However, it is essential to emphasize that many focus their IoT systems for air quality applications [17,21,26,27].…”

Section: Introductionmentioning

confidence: 99%

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

et al. 2022

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This work presented the design, development, and implementation of a low-cost IoT system for real-time monitoring of electrical consumption and environment parameters inside social housing. The IoT monitoring system is composed of a set of remote measurement nodes that wirelessly sense and transmit records of the air temperature and relative humidity inside the house as well as the voltage, current, and power consumed by several electrical devices. A server coordinator composed of a Raspberry Pi was used for the interaction with the sensors through the Message Queue Telemetry Transport (MQTT) protocol and allowed the measured data packaging, fragmentation, transfer, storage, and cloud upload for remote multiuser visualization. The system was experimentally evaluated in an inhabited single-story house under tropical climate conditions. Operation tests indicate a successful performance of the protocols implemented for remote visualization and cloud storage. Moreover, an analysis of measured data allows the feasibility of identifying the occupants’ energy consumption patterns and their relationship with the search for comfortable environmental conditions. Thus, the proposed friendly framework is a promising alternative to energy management. Its implementation in embedded Linux-based systems provides the flexibility to integrate control strategies based on artificial intelligence.

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IOT based air quality and particulate matter concentration monitoring system

Cited by 43 publications

References 0 publications

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

Solar Panels String Predictive and Parametric Fault Diagnosis Using Low-Cost Sensors

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

IoT System for the Continuous Electrical and Environmental Monitoring into Mexican Social Housing Evaluated under Tropical Climate Conditions

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