Topic: Life cycle
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
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - 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.
2022
- DetailsCampanile, L., Iacono, M., & Mastroianni, M. (2022). Towards privacy-aware software design in small and medium enterprises. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927958
Abstract
The legal definition of privacy regulations, like GDPR in the European Union, significantly impacted on the way in which software, systems and organizations should be designed or maintained to be compliant to rules. While the privacy community stated proper risk assessment and mitigation approaches to be applied, literature seems to suggest that the software engineering community, with special reference to companies, did actually concentrate on the specification phase, with less attention for the test phase of products. In coherence with the privacy-by-design approach, we believe that a bigger methodological effort must be put in the systematic adaptation of software development cycles to privacy regulations, and that this effort might be promoted in the industrial community by focusing on the relation between organizational costs vs technical features, also leveraging the benefits of targeted testing as a mean to lower operational privacy enforcement costs. © 2022 IEEE.
2021
- DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.
2020
- 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
- DetailsVerde, L., Marulli, F., De Fazio, R., Campanile, L., & Marrone, S. (2024). HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis [Article]. Computers in Biology and Medicine, 182. https://doi.org/10.1016/j.compbiomed.2024.109021
Abstract
Background: Voice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. Method: In this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. Results: The reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. Conclusions: The comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders. © 2024 The Author(s) - 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.
2022
- DetailsCampanile, L., Iacono, M., & Mastroianni, M. (2022). Towards privacy-aware software design in small and medium enterprises. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022. https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927958
Abstract
The legal definition of privacy regulations, like GDPR in the European Union, significantly impacted on the way in which software, systems and organizations should be designed or maintained to be compliant to rules. While the privacy community stated proper risk assessment and mitigation approaches to be applied, literature seems to suggest that the software engineering community, with special reference to companies, did actually concentrate on the specification phase, with less attention for the test phase of products. In coherence with the privacy-by-design approach, we believe that a bigger methodological effort must be put in the systematic adaptation of software development cycles to privacy regulations, and that this effort might be promoted in the industrial community by focusing on the relation between organizational costs vs technical features, also leveraging the benefits of targeted testing as a mean to lower operational privacy enforcement costs. © 2022 IEEE.
2021
- DetailsCampanile, L., Cantiello, P., Iacono, M., Lotito, R., Marulli, F., & Mastroianni, M. (2021). Applying Machine Learning to Weather and Pollution Data Analysis for a Better Management of Local Areas: The Case of Napoli, Italy [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 2021-April, 354–363. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135227609&partnerID=40&md5=5a7c117fa01d0ba8d779b0e092bc0f63
Abstract
Local pollution is a problem that affects urban areas and has effects on the quality of life and on health conditions. In order to not develop strict measures and to better manage territories, the national authorities have applied a vast range of predictive models. Actually, the application of machine learning has been studied in the last decades in various cases with various declination to simplify this problem. In this paper, we apply a regression-based analysis technique to a dataset containing official historical local pollution and weather data to look for criteria that allow forecasting critical conditions. The methods was applied to the case study of Napoli, Italy, where the local environmental protection agency manages a set of fixed monitoring stations where both chemical and meteorological data are recorded. The joining of the two raw dataset was overcome by the use of a maximum inclusion strategy as performing the joining action with”outer” mode. Among the four different regression models applied, namely the Linear Regression Model calculated with Ordinary Least Square (LN-OLS), the Ridge regression Model (Ridge), the Lasso Model (Lasso) and Supervised Nearest Neighbors Regression (KNN), the Ridge regression model was found to better perform with an R2 (Coefficient of Determination) value equal to 0.77 and low value for both MAE (Mean Absolute Error) and MSE (Mean Squared Error), equal to 0.12 and 0.04 respectively. © 2021 by SCITEPRESS - Science and Technology Publications, Lda.
2020
- 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.