| Title | Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Molka-Danielsen, J., Engelseth, P., Olešnaníková, V., Šarafín, P., Žalman, R. |
| Conference Name | 2017 5th International Conference on Enterprise Systems (ES) |
| Date Published | sep |
| Keywords | air quality, air quality monitoring, air temperature, autonomous assessment, autonomous wireless sensor network technologies, Big Data, Big Data analytics, Buildings, Carbon dioxide, CO2, confined spaces humans, continuous assessment, critical role, data analytics approach, decision making, effective decision making, Employment, ergonomics, health and safety, Heart rate, high risk industries, Humidity, indoor air quality, industrial workplace air quality, industrial workplace buildings, industrial workplaces, Logistics, logistics shipping base, Metrics, Monitoring, on-shore logistics base a regional shipping industry, oxygen displacer, perceived levels, potential BD problems, pubcrawl, reported medical health, resilience, Resiliency, Scalability, shipping industry, supply chain management, supply chain risk assessment, Temperature measurement, Temperature sensors, Transportation, visualization approach, Wireless Sensor Network Technologies, Wireless sensor networks, workplace environments, WSN technologies |
| Abstract | The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. 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. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management. |
| DOI | 10.1109/ES.2017.14 |
| Citation Key | molka-danielsen_big_2017 |
Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies