Topic: Monitoring
Published:
2026
- DetailsConference Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern ItalyCampanile, L., Di Bonito, L. P., Marulli, F., Balzanella, A., & Verde, R. (2026). Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern Italy [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 317–333. https://doi.org/10.1007/978-3-031-97645-2_21
Abstract
The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsDi Giovanni, M., Verde, L., Campanile, L., Romoli, M., Sabbarese, C., & Marrone, S. (2025). Assessing Safety and Sustainability of a Monitoring System for Nuclear Waste Management [Article]. IEEE Access, 13, 120486–120505. https://doi.org/10.1109/ACCESS.2025.3586735
Abstract
Nowadays, nuclear technologies are increasingly being integrated into industry, healthcare and manufacturing. As a side effect, waste materials are produced according to standard processes which are subject to international regulations. One of the most critical phases is the pre-disposal, due to the uncertainty related to the evolution of the materials and their potential impact on environmental protection. This paper introduces the architecture of a monitoring system able to accomplish safety goals and to guarantee energetic sustainability. The possibility of defining different system configurations (e. g., sensor scheduling policies, geometry of the sites, trustworthiness of the sensors) fosters a high adaptability to several monitoring scenarios, being characterised by different safety and sustainability levels. A methodology, integrating a model-based approach with data collection and processing, is proposed to quantitatively evaluate system configurations. This methodology is based on the definition of two metrics — one for safety and one for sustainability — and an assessment model. The model computes the metrics considering geometry of the place, scheduling and trustworthiness of monitoring sensors. This is a first step in the construction of a Decision Support System able to aid human operators in assessing system configurations and finding possible safety/sustainability trade-offs. A case study is used to show the feasibility of the approach: some configurations are evaluated on the real plant, placed at Řež in the Czech Republic, assessing them on the base of the defined metrics. © 2025 The Authors.
2024
- DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2023
- DetailsDi Giovanni, M., Campanile, L., D’Onofrio, A., Marrone, S., Marulli, F., Romoli, M., Sabbarese, C., & Verde, L. (2023). Supporting the Development of Digital Twins in Nuclear Waste Monitoring Systems [Conference paper]. Procedia Computer Science, 225, 3133–3142. https://doi.org/10.1016/j.procs.2023.10.307
Abstract
In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsDi Bonito, L. P., Campanile, L., Napolitano, E., Iacono, M., Portolano, A., & Di Natale, F. (2023). Analysis of a marine scrubber operation with a combined analytical/AI-based method [Article]. Chemical Engineering Research and Design, 195, 613–623. https://doi.org/10.1016/j.cherd.2023.06.006
Abstract
This paper describes the performances of a marine SO2 absorption scrubber installed onboard a large Ro-Ro cargo ship. The study is based on the reconstruction of an extensive dataset from one-year continuous monitoring of the scrubber’s performances and operating conditions. The dataset has been interpreted with a conventional analytical, physical-mathematical, model for absorbers’ rating and its combination with an Artificial Intelligence (AI) one. First, the analytical model has been used to provide a deterministic mathematical framework for the interpretation and the prediction of the scrubber’s performances in terms of absorbed SO2 molar flow and SO2 concentration at the scrubber exit. Then, data mining and AI techniques have been applied to develop an Artificial Neural Network able to predict the error between the actual SO2 concentration at the scrubber exit and the corresponding analytical model predictions. The final result is a combined model providing superior robustness and accuracy in the prediction of the scrubber performance while preserving a rationale for process design and operation. This interesting outcome suggests that the development of combined, or hybrid, Analytical/AI models can be a reliable and cost-effective way to improve chemical engineers’ ability to design and control marine scrubbers, as well as other chemical equipment. © 2023 Institution of Chemical Engineers
2021
- DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2021). Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13104 LNCS, 345–359. https://doi.org/10.1007/978-3-030-91825-5_21
Abstract
Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads. © 2021, Springer Nature Switzerland AG.
2020
- DetailsCampanile, L., Iacono, M., Marrone, S., & Mastroianni, M. (2020). On Performance Evaluation of Security Monitoring in Multitenant Cloud Applications [Article]. Electronic Notes in Theoretical Computer Science, 353, 107–127. https://doi.org/10.1016/j.entcs.2020.09.020
Abstract
In this paper we present a modeling approach suitable for practical evaluation of the delays that may affect security monitoring systems in (multitenant) cloud based architecture, and in general to support professionals in planning and evaluating relevant parameters in dealing with new designs or migration projects. The approach is based on modularity and multiformalism techniques to manage complexity and guide designers in an incremental process, to help transferring technical knowledge into modeling practice and to help easing the use of simulation. We present a case study based on a real experience, triggered by a new legal requirement that Italian Public Administration should comply about their datacenters. © 2020 The Author(s) - DetailsCampanile, L., Iacono, M., Lotito, R., & Mastroianni, M. (2020). A WSN Energy-Aware approach for air pollution monitoring in waste treatment facility site: A case study for landfill monitoring odour [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 526–532. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089477488&partnerID=40&md5=13ea9ca38f15c5b885ef7e501067010c
Abstract
The gaseous emissions derived from industrial plants are generally subject to a strictly program of monitoring, both continuous or one-spot, in order to comply with the limits imposed by the permitting license. Nowadays the problem of odour emission, and the consequently nuisance generated to the nearest receptors, has acquired importance so that is frequently asked a specific implementation of the air pollution monitoring program. In this paper we studied the case study of a generic landfill for the implementation of the odour monitoring system and time-specific use of air pollution control technology. The off-site monitoring is based on the deployment of electronic nose as part of a specifically built WSN system. The nodes outside the landfill boundary do not act as a continuously monitoring stations but as sensors activated when specific conditions, inside and outside the landfill, are achieved. The WSN is then organized on an energy-aware approach so to prolong the lifetime of the entire system, with significant cost-benefit advancement, and produce a monitoring-structure that can answer to specific input like threshold overshooting. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2020). Performance evaluation of a fog WSN infrastructure for emergency management [Article]. Simulation Modelling Practice and Theory, 104. https://doi.org/10.1016/j.simpat.2020.102120
Abstract
Advances in technology and the rise of new computing paradigms, such as Fog computing, may boost the definition of a new generation of advanced support services in critical applications. In this paper we explore the possibilities of a Wireless Sensor Network support (WSN) for a Fog computing system in an emergency management architecture that has been previously presented. Disposable intelligent wireless sensors, capable of processing tasks locally, are deployed and used to support and protect the intervention of a squad of firemen equipped with augmented reality and life monitoring devices to provide an environmental monitoring system and communication infrastructure,in the framework of a next-generation, cloud-supported emergency management system. Simulation is used to explore the design parameter space and dimension the workloads and the extension of the WSN, according to an adaptive behavior of the resulting Fog computing system that varies workloads to save the integrity of the WSN. © 2020 Elsevier B.V.
2026
- DetailsConference Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern ItalyCampanile, L., Di Bonito, L. P., Marulli, F., Balzanella, A., & Verde, R. (2026). Toward Privacy-Aware Environmental Monitoring of CO2 and Air Pollutants in Southern Italy [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 317–333. https://doi.org/10.1007/978-3-031-97645-2_21
Abstract
The increasing levels of CO2 and air pollutants represent a major challenge to environmental sustainability and public health, particularly in regions characterized by complex geographic and socio-economic dynamics. This work proposes a study focused on the Southern Italy regions, where environmental vulnerabilities are displayed, along with a limited availability of high-granularity data. The main aim of this work is to build and provide a comprehensive and detailed dataset tailored to the region’s unique needs, by leveraging datasets from EDGAR for greenhouse gases and air pollutants, integrated with demographic and territorial morphology data from ISTAT. The creation of composite indicators to monitor trends in emissions and pollution on a fine spatial scale is supported by the data set. These indicators enable initial insight into spatial disparities in pollutant concentrations, offering valuable data to inform targeted policy interventions. The work provided a foundation for next analytical studies, integrating different datasets and highlighting the potential for complex spatiotemporal analysis. The study provides a robust dataset and preliminary insights, enhancing the understanding of environmental dynamics in Southern Italy. Subsequent efforts will focus on extending this methodology to more extensive geographic contexts and incorporating real-time data for adaptive monitoring. The proposed framework also lays the groundwork for privacy-aware environmental monitoring solutions, enabling future integration with edge and IoT-based architectures while addressing privacy and data protection concerns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsDi Giovanni, M., Verde, L., Campanile, L., Romoli, M., Sabbarese, C., & Marrone, S. (2025). Assessing Safety and Sustainability of a Monitoring System for Nuclear Waste Management [Article]. IEEE Access, 13, 120486–120505. https://doi.org/10.1109/ACCESS.2025.3586735
Abstract
Nowadays, nuclear technologies are increasingly being integrated into industry, healthcare and manufacturing. As a side effect, waste materials are produced according to standard processes which are subject to international regulations. One of the most critical phases is the pre-disposal, due to the uncertainty related to the evolution of the materials and their potential impact on environmental protection. This paper introduces the architecture of a monitoring system able to accomplish safety goals and to guarantee energetic sustainability. The possibility of defining different system configurations (e. g., sensor scheduling policies, geometry of the sites, trustworthiness of the sensors) fosters a high adaptability to several monitoring scenarios, being characterised by different safety and sustainability levels. A methodology, integrating a model-based approach with data collection and processing, is proposed to quantitatively evaluate system configurations. This methodology is based on the definition of two metrics — one for safety and one for sustainability — and an assessment model. The model computes the metrics considering geometry of the place, scheduling and trustworthiness of monitoring sensors. This is a first step in the construction of a Decision Support System able to aid human operators in assessing system configurations and finding possible safety/sustainability trade-offs. A case study is used to show the feasibility of the approach: some configurations are evaluated on the real plant, placed at Řež in the Czech Republic, assessing them on the base of the defined metrics. © 2025 The Authors.
2024
- DetailsCampanile, L., Di Bonito, L. P., Natale, F. D., & Iacono, M. (2024). Ensemble Models for Predicting CO Concentrations: Application and Explainability in Environmental Monitoring in Campania, Italy [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 38(1), 558–564. https://doi.org/10.7148/2024-0558
Abstract
Monitoring of non-linear phenomena, such as pollution dynamics, which is the result of several combined factors and the evolution of environmental conditions, greatly benefits by AI tools; a larger benefit derives by the application of explainable solutions, which are capable of providing elements to understand those dynamics for better informed decisions. In this paper we discuss a case with real data in which a posteriori explanations have been produced after the application of ensemble models. © ECMS Daniel Grzonka, Natalia Rylko, Grazyna Suchacka, Vladimir Mityushev (Editors) 2024.
2023
- DetailsDi Giovanni, M., Campanile, L., D’Onofrio, A., Marrone, S., Marulli, F., Romoli, M., Sabbarese, C., & Verde, L. (2023). Supporting the Development of Digital Twins in Nuclear Waste Monitoring Systems [Conference paper]. Procedia Computer Science, 225, 3133–3142. https://doi.org/10.1016/j.procs.2023.10.307
Abstract
In a world whose attention to environmental and health problems is very high, the issue of properly managing nuclear waste is of a primary importance. Information and Communication Technologies have the due to support the definition of the next-generation plants for temporary storage of such wasting materials. This paper investigates on the adoption of one of the most cutting-edge techniques in computer science and engineering, i.e. Digital Twins, with the combination of other modern methods and technologies as Internet of Things, model-based and data-driven approaches. The result is the definition of a methodology able to support the construction of risk-aware facilities for storing nuclear waste. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) - DetailsDi Bonito, L. P., Campanile, L., Napolitano, E., Iacono, M., Portolano, A., & Di Natale, F. (2023). Analysis of a marine scrubber operation with a combined analytical/AI-based method [Article]. Chemical Engineering Research and Design, 195, 613–623. https://doi.org/10.1016/j.cherd.2023.06.006
Abstract
This paper describes the performances of a marine SO2 absorption scrubber installed onboard a large Ro-Ro cargo ship. The study is based on the reconstruction of an extensive dataset from one-year continuous monitoring of the scrubber’s performances and operating conditions. The dataset has been interpreted with a conventional analytical, physical-mathematical, model for absorbers’ rating and its combination with an Artificial Intelligence (AI) one. First, the analytical model has been used to provide a deterministic mathematical framework for the interpretation and the prediction of the scrubber’s performances in terms of absorbed SO2 molar flow and SO2 concentration at the scrubber exit. Then, data mining and AI techniques have been applied to develop an Artificial Neural Network able to predict the error between the actual SO2 concentration at the scrubber exit and the corresponding analytical model predictions. The final result is a combined model providing superior robustness and accuracy in the prediction of the scrubber performance while preserving a rationale for process design and operation. This interesting outcome suggests that the development of combined, or hybrid, Analytical/AI models can be a reliable and cost-effective way to improve chemical engineers’ ability to design and control marine scrubbers, as well as other chemical equipment. © 2023 Institution of Chemical Engineers
2021
- DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2021). Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13104 LNCS, 345–359. https://doi.org/10.1007/978-3-030-91825-5_21
Abstract
Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads. © 2021, Springer Nature Switzerland AG.
2020
- DetailsCampanile, L., Iacono, M., Marrone, S., & Mastroianni, M. (2020). On Performance Evaluation of Security Monitoring in Multitenant Cloud Applications [Article]. Electronic Notes in Theoretical Computer Science, 353, 107–127. https://doi.org/10.1016/j.entcs.2020.09.020
Abstract
In this paper we present a modeling approach suitable for practical evaluation of the delays that may affect security monitoring systems in (multitenant) cloud based architecture, and in general to support professionals in planning and evaluating relevant parameters in dealing with new designs or migration projects. The approach is based on modularity and multiformalism techniques to manage complexity and guide designers in an incremental process, to help transferring technical knowledge into modeling practice and to help easing the use of simulation. We present a case study based on a real experience, triggered by a new legal requirement that Italian Public Administration should comply about their datacenters. © 2020 The Author(s) - DetailsCampanile, L., Iacono, M., Lotito, R., & Mastroianni, M. (2020). A WSN Energy-Aware approach for air pollution monitoring in waste treatment facility site: A case study for landfill monitoring odour [Conference paper]. IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security, 526–532. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089477488&partnerID=40&md5=13ea9ca38f15c5b885ef7e501067010c
Abstract
The gaseous emissions derived from industrial plants are generally subject to a strictly program of monitoring, both continuous or one-spot, in order to comply with the limits imposed by the permitting license. Nowadays the problem of odour emission, and the consequently nuisance generated to the nearest receptors, has acquired importance so that is frequently asked a specific implementation of the air pollution monitoring program. In this paper we studied the case study of a generic landfill for the implementation of the odour monitoring system and time-specific use of air pollution control technology. The off-site monitoring is based on the deployment of electronic nose as part of a specifically built WSN system. The nodes outside the landfill boundary do not act as a continuously monitoring stations but as sensors activated when specific conditions, inside and outside the landfill, are achieved. The WSN is then organized on an energy-aware approach so to prolong the lifetime of the entire system, with significant cost-benefit advancement, and produce a monitoring-structure that can answer to specific input like threshold overshooting. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. - DetailsCampanile, L., Gribaudo, M., Iacono, M., & Mastroianni, M. (2020). Performance evaluation of a fog WSN infrastructure for emergency management [Article]. Simulation Modelling Practice and Theory, 104. https://doi.org/10.1016/j.simpat.2020.102120
Abstract
Advances in technology and the rise of new computing paradigms, such as Fog computing, may boost the definition of a new generation of advanced support services in critical applications. In this paper we explore the possibilities of a Wireless Sensor Network support (WSN) for a Fog computing system in an emergency management architecture that has been previously presented. Disposable intelligent wireless sensors, capable of processing tasks locally, are deployed and used to support and protect the intervention of a squad of firemen equipped with augmented reality and life monitoring devices to provide an environmental monitoring system and communication infrastructure,in the framework of a next-generation, cloud-supported emergency management system. Simulation is used to explore the design parameter space and dimension the workloads and the extension of the WSN, according to an adaptive behavior of the resulting Fog computing system that varies workloads to save the integrity of the WSN. © 2020 Elsevier B.V.