Topic: Design
Published:
2026
- DetailsCampanile, L., Iacono, M., Mastroianni, M., Riccio, C., & Viscardi, B. (2026). A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 303–316. https://doi.org/10.1007/978-3-031-97645-2_20
Abstract
Adapting or designing a system which operates on personal data in EU is impacted by the privacy-by-design and privacy-by-default principles because of the prescriptions of the GDPR. In this paper we propose an approach to decision making which is based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The approach is applied to a GDPR system compliance design process, based on a case study about system performance evaluation by means of queuing networks, but is absolutely general with respect to analogous problems, in which cost issues should be balanced with technical performances and risk exposure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsCampanile, L., Iacono, M., Mastroianni, M., & Riccio, C. (2025). Performance Evaluation of an Edge-Blockchain Architecture for Smart City [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2025-June, 620–627. https://doi.org/10.7148/2025-0620
Abstract
This paper presents a simulation-based methodology to evaluate the performance of a privacy-compliant edge-blockchain architecture for smart city environments. The proposed model combines edge computing with a private, permissioned blockchain to ensure low-latency processing, secure data management, and verifiable transactions. Using a discrete-event simulation framework, we analyze the behavior of the system under realistic workloads and time-varying traffic conditions. The model captures edge operations, including preprocessing and cryptographic tasks, as well as blockchain validation using Proof of Stake consensus. Several experiments explore saturation thresholds, resource utilization, and latency dynamics, under both synthetic and realistic traffic profiles. Results reveal how architectural bottlenecks shift depending on resource allocation and input rate, and demonstrate the importance of balanced dimensioning between edge and blockchain layers. © ECMS Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita (Editors) 2025. - DetailsDi Bonito, L. P., Campanile, L., Iacono, M., & Di Natale, F. (2025). An eXplainable Artificial Intelligence framework to predict marine scrubbers performances [Article]. Engineering Applications of Artificial Intelligence, 160. https://doi.org/10.1016/j.engappai.2025.111860
Abstract
This study presents an eXplainable Artificial Intelligence (XAI) framework to predict the performance of marine scrubbers used for sulfur dioxide (SO2) removal from marine diesel engine flue gases. Using an aggregated dataset from a roll-on/roll-off (Ro-Ro) cargo ship equipped with an open-loop scrubber, combined with satellite data, the study constructs and evaluates multiple artificial intelligence models, including ensemble models, which were benchmarked against each other using standard regression metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). Results achieve high accuracy R2>0.92 and offer insights for optimizing scrubber operations. Nevertheless, artificial intelligence models lack transparency. To overcome this problem, this research integrates post-hoc explainability techniques to elucidate the contributions of various features to model predictions, thereby enhancing interpretability and reliability. The integration of SHapley Additive exPlanations (SHAP) and Explain Like I’m 5 (ELI5) not only confirmed the consistency of feature importance rankings (e.g. seawater acidity level, SO2 inlet concentration, outlet temperature) but also aligned with the physical-chemical principles of SO2 absorption. Quantitative comparisons with theoretical expectations demonstrated the reliability of the XAI insights, enhancing both model transparency and interpretability. This can improve the current capability of designing scrubber units by defining more efficient and less expensive options for environmental regulation compliance. © 2025 The Authors
2023
- DetailsConference Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins DesignCampanile, L., De Biase, M. S., De Fazio, R., Di Giovanni, M., Marulli, F., & Verde, L. (2023). Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design [Conference paper]. Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, 301–306. https://doi.org/10.1109/CSR57506.2023.10224945
Abstract
Nowadays, the problem of system robustness, es-pecially in critical infrastructures, is a challenging open question. Some systems provide crucial services continuously failing, threatening the availability of the provided services. By designing a robust architecture, this criticality could be overcome or limited, ensuring service continuity. The definition of a resilient system involves not only its architecture but also the methodology implemented for the calculation and analysis of some indices, quantifying system performance. This study provides an innovative architecture for Digital Twins implementation based on a hybrid methodology for improving the control system in realtime. The introduced approach brings together different techniques. In particular, the work combines the point of strengths of Model-based methods and Data-driven ones, aiming to improve system performances. © 2023 IEEE. - 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 - DetailsCampanile, L., Di Bonito, L. P., Iacono, M., & Di Natale, F. (2023). Prediction of chemical plants operating performances: a machine learning approach [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2023-June, 575–581. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163436467&partnerID=40&md5=2e96d04affd9bb4a126b224d7cc8d75a
Abstract
Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.
2022
- DetailsCampanile, L., Biase, M. S. de, Marrone, S., Raimondo, M., & Verde, L. (2022). On the Evaluation of BDD Requirements with Text-based Metrics: The ETCS-L3 Case Study [Conference paper]. Smart Innovation, Systems and Technologies, 309, 561–571. https://doi.org/10.1007/978-981-19-3444-5_48
Abstract
A proper requirement definition phase is of a paramount importance in software engineering. It is the first and prime mean to realize efficient and reliable systems. System requirements are usually formulated and expressed in natural language, given its universality and ease of communication and writing. Unfortunately, natural language can be a source of ambiguity, complexity and omissions, which may cause system failures. Among the different approaches proposed by the software engineering community, Behavioural-Driven Development (BDD) is affirming as a valid, practical method to structure effective and non-ambiguous requirement specifications. The paper tackles with the problem of measuring requirements in BDD by assessing some traditional Natural Language Processing-related metrics with respect to a sample excerpt of requirement specification rewritten according to the BDD criteria. This preliminary assessment is made on the ERTMS-ETCS Level 3 case study whose specification, up to this date, is not managed by a standardisation body. The paper demonstrates the necessity of novel metrics able to cope with the BDD specification paradigms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
2021
- DetailsCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Designing a GDPR compliant blockchain-based IoV distributed information tracking system [Article]. Information Processing and Management, 58(3). https://doi.org/10.1016/j.ipm.2021.102511
Abstract
Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation and helping granting a correct handling of related legal issues. However, there are privacy issues that have to be considered, as tracked information potentially include data about private persons (position, personal habits), commercially relevant information (state of the fleet of a company, freight movement and related planning, logistic strategies), or even more critical knowledge (e.g., considering vehicles belonging to police, public authorities, governments or officers in sensible positions). In the European Union, all this information is covered by the General Data Protection Regulation (GDPR). In this paper we propose a reference model for a system that manages relevant information to show how blockchain can support GDPR compliant solutions for Internet of Vehicles, taking as a reference an integrated scenario based on Italy, and analyze a subset of its use cases to show its viability with reference to privacy issues. © 2021 Elsevier Ltd - DetailsCampanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1
Abstract
Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG.
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
- DetailsCampanile, L., Iacono, M., Mastroianni, M., Riccio, C., & Viscardi, B. (2026). A TOPSIS-Based Approach to Evaluate Alternative Solutions for GDPR-Compliant Smart-City Services Implementation [Conference paper]. Lecture Notes in Computer Science, 15893 LNCS, 303–316. https://doi.org/10.1007/978-3-031-97645-2_20
Abstract
Adapting or designing a system which operates on personal data in EU is impacted by the privacy-by-design and privacy-by-default principles because of the prescriptions of the GDPR. In this paper we propose an approach to decision making which is based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The approach is applied to a GDPR system compliance design process, based on a case study about system performance evaluation by means of queuing networks, but is absolutely general with respect to analogous problems, in which cost issues should be balanced with technical performances and risk exposure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
- DetailsCampanile, L., Iacono, M., Mastroianni, M., & Riccio, C. (2025). Performance Evaluation of an Edge-Blockchain Architecture for Smart City [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2025-June, 620–627. https://doi.org/10.7148/2025-0620
Abstract
This paper presents a simulation-based methodology to evaluate the performance of a privacy-compliant edge-blockchain architecture for smart city environments. The proposed model combines edge computing with a private, permissioned blockchain to ensure low-latency processing, secure data management, and verifiable transactions. Using a discrete-event simulation framework, we analyze the behavior of the system under realistic workloads and time-varying traffic conditions. The model captures edge operations, including preprocessing and cryptographic tasks, as well as blockchain validation using Proof of Stake consensus. Several experiments explore saturation thresholds, resource utilization, and latency dynamics, under both synthetic and realistic traffic profiles. Results reveal how architectural bottlenecks shift depending on resource allocation and input rate, and demonstrate the importance of balanced dimensioning between edge and blockchain layers. © ECMS Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita (Editors) 2025. - DetailsDi Bonito, L. P., Campanile, L., Iacono, M., & Di Natale, F. (2025). An eXplainable Artificial Intelligence framework to predict marine scrubbers performances [Article]. Engineering Applications of Artificial Intelligence, 160. https://doi.org/10.1016/j.engappai.2025.111860
Abstract
This study presents an eXplainable Artificial Intelligence (XAI) framework to predict the performance of marine scrubbers used for sulfur dioxide (SO2) removal from marine diesel engine flue gases. Using an aggregated dataset from a roll-on/roll-off (Ro-Ro) cargo ship equipped with an open-loop scrubber, combined with satellite data, the study constructs and evaluates multiple artificial intelligence models, including ensemble models, which were benchmarked against each other using standard regression metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). Results achieve high accuracy R2>0.92 and offer insights for optimizing scrubber operations. Nevertheless, artificial intelligence models lack transparency. To overcome this problem, this research integrates post-hoc explainability techniques to elucidate the contributions of various features to model predictions, thereby enhancing interpretability and reliability. The integration of SHapley Additive exPlanations (SHAP) and Explain Like I’m 5 (ELI5) not only confirmed the consistency of feature importance rankings (e.g. seawater acidity level, SO2 inlet concentration, outlet temperature) but also aligned with the physical-chemical principles of SO2 absorption. Quantitative comparisons with theoretical expectations demonstrated the reliability of the XAI insights, enhancing both model transparency and interpretability. This can improve the current capability of designing scrubber units by defining more efficient and less expensive options for environmental regulation compliance. © 2025 The Authors
2023
- DetailsConference Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins DesignCampanile, L., De Biase, M. S., De Fazio, R., Di Giovanni, M., Marulli, F., & Verde, L. (2023). Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design [Conference paper]. Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, 301–306. https://doi.org/10.1109/CSR57506.2023.10224945
Abstract
Nowadays, the problem of system robustness, es-pecially in critical infrastructures, is a challenging open question. Some systems provide crucial services continuously failing, threatening the availability of the provided services. By designing a robust architecture, this criticality could be overcome or limited, ensuring service continuity. The definition of a resilient system involves not only its architecture but also the methodology implemented for the calculation and analysis of some indices, quantifying system performance. This study provides an innovative architecture for Digital Twins implementation based on a hybrid methodology for improving the control system in realtime. The introduced approach brings together different techniques. In particular, the work combines the point of strengths of Model-based methods and Data-driven ones, aiming to improve system performances. © 2023 IEEE. - 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 - DetailsCampanile, L., Di Bonito, L. P., Iacono, M., & Di Natale, F. (2023). Prediction of chemical plants operating performances: a machine learning approach [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2023-June, 575–581. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163436467&partnerID=40&md5=2e96d04affd9bb4a126b224d7cc8d75a
Abstract
Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.
2022
- DetailsCampanile, L., Biase, M. S. de, Marrone, S., Raimondo, M., & Verde, L. (2022). On the Evaluation of BDD Requirements with Text-based Metrics: The ETCS-L3 Case Study [Conference paper]. Smart Innovation, Systems and Technologies, 309, 561–571. https://doi.org/10.1007/978-981-19-3444-5_48
Abstract
A proper requirement definition phase is of a paramount importance in software engineering. It is the first and prime mean to realize efficient and reliable systems. System requirements are usually formulated and expressed in natural language, given its universality and ease of communication and writing. Unfortunately, natural language can be a source of ambiguity, complexity and omissions, which may cause system failures. Among the different approaches proposed by the software engineering community, Behavioural-Driven Development (BDD) is affirming as a valid, practical method to structure effective and non-ambiguous requirement specifications. The paper tackles with the problem of measuring requirements in BDD by assessing some traditional Natural Language Processing-related metrics with respect to a sample excerpt of requirement specification rewritten according to the BDD criteria. This preliminary assessment is made on the ERTMS-ETCS Level 3 case study whose specification, up to this date, is not managed by a standardisation body. The paper demonstrates the necessity of novel metrics able to cope with the BDD specification paradigms. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
2021
- DetailsCampanile, L., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Designing a GDPR compliant blockchain-based IoV distributed information tracking system [Article]. Information Processing and Management, 58(3). https://doi.org/10.1016/j.ipm.2021.102511
Abstract
Blockchain technologies and distributed ledgers enable the design and implementation of trustable data logging systems that can be used by multiple parties to produce a non-repudiable database. The case of Internet of Vehicles may greatly benefit of such a possibility to track the chain of responsibility in case of accidents or damages due to bad or omitted maintenance, improving the safety of circulation and helping granting a correct handling of related legal issues. However, there are privacy issues that have to be considered, as tracked information potentially include data about private persons (position, personal habits), commercially relevant information (state of the fleet of a company, freight movement and related planning, logistic strategies), or even more critical knowledge (e.g., considering vehicles belonging to police, public authorities, governments or officers in sensible positions). In the European Union, all this information is covered by the General Data Protection Regulation (GDPR). In this paper we propose a reference model for a system that manages relevant information to show how blockchain can support GDPR compliant solutions for Internet of Vehicles, taking as a reference an integrated scenario based on Italy, and analyze a subset of its use cases to show its viability with reference to privacy issues. © 2021 Elsevier Ltd - DetailsCampanile, L., Cantiello, P., Iacono, M., Marulli, F., & Mastroianni, M. (2021). Risk Analysis of a GDPR-Compliant Deletion Technique for Consortium Blockchains Based on Pseudonymization [Conference paper]. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12956 LNCS, 3–14. https://doi.org/10.1007/978-3-030-87010-2_1
Abstract
Blockchains provide a valid and profitable support for the implementation of trustable and secure distributed ledgers, in support to groups of subjects that are potentially competitors in conflict of interest but need to share progressive information recording processes. Blockchains prevent data stored in blocks from being altered or deleted, but there are situations in which stored information must be deleted or made inaccessible on request or periodically, such as the ones in which GDPR is applicable. In this paper we present literature solutions and design an implementation in the context of a traffic management system for the Internet of Vehicles based on the Pseudonymization/Cryptography solution, evaluating its viability, its GDPR compliance and its level of risk. © 2021, Springer Nature Switzerland AG.
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.