Monitoring of CO2 amount in closed objects via WSN

Zalman, Robert; Olesnanikova, Veronika; Ševčı́k, Peter; Sarafin, Peter

doi:10.15439/2015f332

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…where V c =5, R L is load resistance and R s is the sensor resistance (between 30Kohm and 200Kohm). It is possible to obtain values of CO 2 , CO and NH 4 levels in the air of the monitored environment using the following equations [14].…”

Section: Results Analysismentioning

confidence: 99%

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

2020

IJITEE

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Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.

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Section: Results Analysismentioning

confidence: 99%

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

2020

IJITEE

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“…Consumption in standby mode of the module is only 50 A. The speed of wireless transmission of this module is 1Mbps or 2Mbps [29].…”

Section: Methodsmentioning

confidence: 99%

Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base

Molka-Danielsen

Engelseth

Wang

2018

Journal of Industrial Information Integration

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The future of logistics shipping bases will be to seek efficient flows of materials to meet the needs of business partners. Supply chain and operations managers of supply bases will need to integrate technologies that allow for greater automation, digitalization, flexibility and improved communications among stakeholders. The technologies that are likely to boost integration will consist of a plethora of Industrial Internet-of-Things (IIoT) technologies that may include Wireless Sensor Network (WSN) technologies and could be applied for improved monitoring of healthy and safe industrial workplaces for workers. However, little is known regarding how WSN technologies can be implemented on a larger scale and its implications when integrated on standard logistics and operations of industrial workplaces such as a shipping base. The WSN sensor units represent an integrating resource that are capable of monitoring air temperature, humidity and levels of carbon dioxide (CO2) and other gasses and of disseminating this information to different actors in the production system. Air quality factors play a critical role in the perceived levels of workers' comfort and in reported medical health. The low cost of wireless sensor network (WSN) technologies offer potential for continuous, autonomous and importantly networked assessment of industrial workplace air quality that may have implications for operations management and quality of production. This paper initially presents a case study that monitors air quality that is collected with WSN technologies from two workshops carried out by a large on-shore logistics base that supports offshore petroleum logistics. The case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. However, with the advancement in IIoT technologies and the emergence of smart sensing and actuating devices, it is possible to form a digital closed-loop system that we argue is essential for managers to link together information about air quality with supply chain and operations management decisions. We propose that central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. We discuss how WSN technologies can be integrated into the logistics management and operations of the shipping base. Through an analytical discussion of BD we explore how to extend the potential application of IIoT and Visual Analytics to facilitate a smart workplace for the Industry 4.0 era.

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Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies

Molka-Danielsen

Engelseth

Olesnanikova

et al. 2017

2017 5th International Conference on Enterprise Systems (ES)

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Monitoring of CO2 amount in closed objects via WSN

Cited by 4 publications

References 8 publications

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base

Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies

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