TY - GEN
T1 - Leveraging the Industrial Internet of Things (IIoT) for Real-Time CO2 Monitoring, Measurement and Visualization
T2 - Technologies, Applications and Future Directions
AU - Christensen, Mads Scott-Fordsmand
PY - 2025
Y1 - 2025
N2 - Global CO2 emissions reduction requires industries to manage and understand their CO2 emission levels in real-time. This paper examines the Industrial Internet of Things (IIoT) for real-time monitoring, measurement, and visualization of reducing CO2 emissions in industrial and environmental domains. Methodology: The methodology consists of a literature review based on peer-reviewed publications and use cases to explore the current state and practical implications. Furthermore, a technical analysis of IIoT systems, CO2 sensors, and data processing techniques is also identified. Results: IIoT systems can support CO2 emission monitoring and accuracy optimization in industrial domains by combining CO2 sensors, wireless communication, and data fusion techniques. In addition, machine learning and artificial intelligence can be used to reduce anomalies in CO2 sensor readings and predictive maintenance of systems. Challenges: Challenges include interoperability, data security and system scalability. To resolve these issues standardized communication protocols, data security methods and implementation barriers should be improved. Future Directions: To enhance data processing and security features, future work should focus on integrating edge computing, artificial intelligence, machine learning, and blockchain techniques. In addition, data visualizations and cost-effective solutions should also be in focus, to provide more adoptable IIoT systems in industrial domains. Conclusion: As IIoT systems and CO2 sensor technologies evolve, IIoT systems can contribute significantly to increasing global air quality and CO2 emission control in industry, agricultural, and urban areas.
AB - Global CO2 emissions reduction requires industries to manage and understand their CO2 emission levels in real-time. This paper examines the Industrial Internet of Things (IIoT) for real-time monitoring, measurement, and visualization of reducing CO2 emissions in industrial and environmental domains. Methodology: The methodology consists of a literature review based on peer-reviewed publications and use cases to explore the current state and practical implications. Furthermore, a technical analysis of IIoT systems, CO2 sensors, and data processing techniques is also identified. Results: IIoT systems can support CO2 emission monitoring and accuracy optimization in industrial domains by combining CO2 sensors, wireless communication, and data fusion techniques. In addition, machine learning and artificial intelligence can be used to reduce anomalies in CO2 sensor readings and predictive maintenance of systems. Challenges: Challenges include interoperability, data security and system scalability. To resolve these issues standardized communication protocols, data security methods and implementation barriers should be improved. Future Directions: To enhance data processing and security features, future work should focus on integrating edge computing, artificial intelligence, machine learning, and blockchain techniques. In addition, data visualizations and cost-effective solutions should also be in focus, to provide more adoptable IIoT systems in industrial domains. Conclusion: As IIoT systems and CO2 sensor technologies evolve, IIoT systems can contribute significantly to increasing global air quality and CO2 emission control in industry, agricultural, and urban areas.
KW - Air Quality
KW - Blockchain Technology
KW - CO2 Monitoring
KW - Carbon Footprint Monitoring
KW - Edge Computing
KW - Emission Management
KW - Environmental Sustainability
KW - Industrial Internet of Things (IIoT)
KW - Machine Learning
KW - Predictive Maintenance
KW - Real-Time Data Processing
KW - Security and Privacy in IIoT
KW - Smart Buildings
KW - Smart Cities
UR - http://www.scopus.com/inward/record.url?scp=85218467046&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-78572-6_3
DO - 10.1007/978-3-031-78572-6_3
M3 - Article in proceedings
SN - 978-3-031-78571-9
T3 - Communications in Computer and Information Science
SP - 35
EP - 59
BT - Global Internet of Things and Edge Computing Summit
A2 - Presser, Mirko
A2 - Skarmeta, Antonio
A2 - Krco, Srdjan
A2 - González Vidal, Aurora
PB - Springer
CY - Cham
ER -