Pub Date : 2024-12-01DOI: 10.1016/j.iot.2024.101352
David Ramiro Troitino
E-governance and Digital transformation in the European Union, ENDE, is a Jean Monnet network including prestigious experts on digital transformation holding Jean Monnet actions on digital aspects (Module, Chair or Centre of Excellence).
At a historical period of renovation in the paradigm of society, E-Governance and digitalization have seen their implementation accelerated by the recent COVID-19 pandemic. Therefore, understanding, suggesting adequate implementation and promoting such a change in the model of civilization is a priority as scientific target of the network. The main working areas are related to the practical implementation of E-governance in the European Union, including areas as politics, economy, law, international relations and social aspects. The objective of this special number of Internet of Things journal, is understanding and foreseeing the fast development on the digital agenda that can change completely the perception of the European Union, its influence over the citizen lives and the external scope of the organization.
{"title":"E-governance and the European Union: Agenda for implementation","authors":"David Ramiro Troitino","doi":"10.1016/j.iot.2024.101352","DOIUrl":"10.1016/j.iot.2024.101352","url":null,"abstract":"<div><div>E-governance and Digital transformation in the European Union, ENDE, is a Jean Monnet network including prestigious experts on digital transformation holding Jean Monnet actions on digital aspects (Module, Chair or Centre of Excellence).</div><div>At a historical period of renovation in the paradigm of society, E-Governance and digitalization have seen their implementation accelerated by the recent COVID-19 pandemic. Therefore, understanding, suggesting adequate implementation and promoting such a change in the model of civilization is a priority as scientific target of the network. The main working areas are related to the practical implementation of E-governance in the European Union, including areas as politics, economy, law, international relations and social aspects. The objective of this special number of Internet of Things journal, is understanding and foreseeing the fast development on the digital agenda that can change completely the perception of the European Union, its influence over the citizen lives and the external scope of the organization.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101352"},"PeriodicalIF":6.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.iot.2024.101442
Ernest Ntizikira , Lei Wang , Jenhui Chen , Kiran Saleem
As the Internet of Things (IoT) expands, ensuring the security and privacy of interconnected devices poses significant challenges. Traditional intrusion detection and prevention systems (IDPS) for IoT rely primarily on network traffic, anomaly detection, and signature-based approaches. This paper addresses deficiencies in conventional infrastructure security, particularly within Closed-Circuit Television (CCTV) operations, to fortify IoT environments against emerging intrusions and ensure heightened levels of privacy and security. Traditional intrusion detection and prevention systems (IDPSs) for IoT primarily rely on network traffic analysis, anomaly detection, and signature-based approaches. However, there is a promising opportunity to enhance IDPS effectiveness by incorporating CCTV cameras and human-inspired techniques. We present a novel approach to IoT security employing CCTV cameras, Raspberry Pi, and emotion recognition intrusion detection and prevention. Initially, two CCTV cameras are installed and connected to a Raspberry Pi for video recording and preprocessing. Emotions are then detected using a convolutional neural network (CNN). Anomalies are classified according to predefined criteria based on detected emotions: individuals meeting conditions such as fear, multiple failed logins (greater than 2), and activity after 6 PM are classified as intruders, those meeting one or two criteria are labeled suspicious, while others are considered normal (non-intruders). In the event of suspicious activity, an alarm is automatically generated, while for intruders, an internet ban is also applied in addition to an alarm. Our proposed system aims to provide a proactive and context-aware defense mechanism against IoT intrusions by integrating machine learning algorithms and blockchain technology, ensuring the robustness and reliability of IoT security.
{"title":"Enhancing IoT security through emotion recognition and blockchain-driven intrusion prevention","authors":"Ernest Ntizikira , Lei Wang , Jenhui Chen , Kiran Saleem","doi":"10.1016/j.iot.2024.101442","DOIUrl":"10.1016/j.iot.2024.101442","url":null,"abstract":"<div><div>As the Internet of Things (IoT) expands, ensuring the security and privacy of interconnected devices poses significant challenges. Traditional intrusion detection and prevention systems (IDPS) for IoT rely primarily on network traffic, anomaly detection, and signature-based approaches. This paper addresses deficiencies in conventional infrastructure security, particularly within Closed-Circuit Television (CCTV) operations, to fortify IoT environments against emerging intrusions and ensure heightened levels of privacy and security. Traditional intrusion detection and prevention systems (IDPSs) for IoT primarily rely on network traffic analysis, anomaly detection, and signature-based approaches. However, there is a promising opportunity to enhance IDPS effectiveness by incorporating CCTV cameras and human-inspired techniques. We present a novel approach to IoT security employing CCTV cameras, Raspberry Pi, and emotion recognition intrusion detection and prevention. Initially, two CCTV cameras are installed and connected to a Raspberry Pi for video recording and preprocessing. Emotions are then detected using a convolutional neural network (CNN). Anomalies are classified according to predefined criteria based on detected emotions: individuals meeting conditions such as fear, multiple failed logins (greater than 2), and activity after 6 PM are classified as intruders, those meeting one or two criteria are labeled suspicious, while others are considered normal (non-intruders). In the event of suspicious activity, an alarm is automatically generated, while for intruders, an internet ban is also applied in addition to an alarm. Our proposed system aims to provide a proactive and context-aware defense mechanism against IoT intrusions by integrating machine learning algorithms and blockchain technology, ensuring the robustness and reliability of IoT security.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101442"},"PeriodicalIF":6.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.iot.2024.101440
Ivanilson França Vieira Junior , Jorge Granjal , Marilia Curado
Time-Slotted Channel Hopping (TSCH) Media Access Control (MAC) has become a leading wireless technology for industrial applications, offering deterministic communication while balancing latency, bandwidth, and energy consumption. This study addresses the critical challenge of cell scheduling within TSCH MAC, emphasising the importance of selecting scheduling mechanisms based on application-specific quality of service parameters. Despite numerous proposals and evaluations, the lack of standardised scheduling methods and comprehensive performance metrics remains a significant obstacle. Traditional network metrics often fail to capture key issues in TSCH-based mesh networks, potentially overlooking indicators of network stability. To address this gap, we examine both application and network metrics from a mesh network perspective and propose a set of complementary metrics tailored to the characteristics of TSCH. These metrics provide a more detailed evaluation of how scheduling impacts network reliability and efficiency. Given the diverse applications and configurations in industrial environments, this study offers insights into employing these complementary metrics for a more accurate assessment of the impact of TSCH scheduling. Ultimately, our approach aims to improve TSCH scheduling evaluation and contribute to advancing industrial wireless communication systems.
{"title":"Insights for metrics in assessing TSCH scheduling efficiency","authors":"Ivanilson França Vieira Junior , Jorge Granjal , Marilia Curado","doi":"10.1016/j.iot.2024.101440","DOIUrl":"10.1016/j.iot.2024.101440","url":null,"abstract":"<div><div>Time-Slotted Channel Hopping (TSCH) Media Access Control (MAC) has become a leading wireless technology for industrial applications, offering deterministic communication while balancing latency, bandwidth, and energy consumption. This study addresses the critical challenge of cell scheduling within TSCH MAC, emphasising the importance of selecting scheduling mechanisms based on application-specific quality of service parameters. Despite numerous proposals and evaluations, the lack of standardised scheduling methods and comprehensive performance metrics remains a significant obstacle. Traditional network metrics often fail to capture key issues in TSCH-based mesh networks, potentially overlooking indicators of network stability. To address this gap, we examine both application and network metrics from a mesh network perspective and propose a set of complementary metrics tailored to the characteristics of TSCH. These metrics provide a more detailed evaluation of how scheduling impacts network reliability and efficiency. Given the diverse applications and configurations in industrial environments, this study offers insights into employing these complementary metrics for a more accurate assessment of the impact of TSCH scheduling. Ultimately, our approach aims to improve TSCH scheduling evaluation and contribute to advancing industrial wireless communication systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101440"},"PeriodicalIF":6.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.iot.2024.101441
Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub
Forecasting electricity load in logistics is crucial for managing dynamic energy demands. This research introduces the Integrated Load Forecasting System (ILFS), integrating IoT-driven load forecasting with advanced machine learning. As pioneers in logistics-focused electricity load forecasting, we acknowledge challenges posed by operational metrics, external factors, and diverse features. Starting with thorough preparation, including managing missing data and normalization, ILFS incorporates novel approaches such as Hybrid Boruta with XGBoost (BXG) for feature selection and Uniform Manifold Projection and Approximation (UMAP) for lower dimensionality. In the classification phase, we introduce a pioneering approach: the Hybrid Huber Regression with ResNet (HRRN) model, fine-tuned using the Coyote Optimization Algorithm (COA). Demonstrating scalability and interpretability, ILFS adjusts to various electricity load data scenarios, capturing trends in logistics supply warehouses across different days. Validation metrics underscore ILFS’s efficacy, achieving 98% accuracy, 4 MAE, 12 MSE, 5 RMSE, and 0.99 R-squared (). With an average execution time of 7.2 s, ILFS outperforms current techniques, and rigorous statistical analyses support this superiority. ILFS emerges as a pivotal solution, meeting the necessities of precise electricity load forecasting in logistics driven by IoT technologies. This research strides towards harmonious integration of load forecasting, IoT, and logistics planning, ushering in advancements in the field.
{"title":"IoT-driven load forecasting with machine learning for logistics planning","authors":"Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub","doi":"10.1016/j.iot.2024.101441","DOIUrl":"10.1016/j.iot.2024.101441","url":null,"abstract":"<div><div>Forecasting electricity load in logistics is crucial for managing dynamic energy demands. This research introduces the Integrated Load Forecasting System (ILFS), integrating IoT-driven load forecasting with advanced machine learning. As pioneers in logistics-focused electricity load forecasting, we acknowledge challenges posed by operational metrics, external factors, and diverse features. Starting with thorough preparation, including managing missing data and normalization, ILFS incorporates novel approaches such as Hybrid Boruta with XGBoost (BXG) for feature selection and Uniform Manifold Projection and Approximation (UMAP) for lower dimensionality. In the classification phase, we introduce a pioneering approach: the Hybrid Huber Regression with ResNet (HRRN) model, fine-tuned using the Coyote Optimization Algorithm (COA). Demonstrating scalability and interpretability, ILFS adjusts to various electricity load data scenarios, capturing trends in logistics supply warehouses across different days. Validation metrics underscore ILFS’s efficacy, achieving 98% accuracy, 4 MAE, 12 MSE, 5 RMSE, and 0.99 R-squared (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>). With an average execution time of 7.2 s, ILFS outperforms current techniques, and rigorous statistical analyses support this superiority. ILFS emerges as a pivotal solution, meeting the necessities of precise electricity load forecasting in logistics driven by IoT technologies. This research strides towards harmonious integration of load forecasting, IoT, and logistics planning, ushering in advancements in the field.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101441"},"PeriodicalIF":6.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-26DOI: 10.1016/j.iot.2024.101424
Chen-Kun Tsung , Yung-An Tsou , Rahmi Liza
In this paper, we apply the Internet of Things (IoT) technology to construct the non-invasive examination, named IoT-based vocal cords (VC) disease inference system (i-VCD), to provide the disease inference assistant framework for physicians. The proposed i-VCD tracks patient’s voice recording during consulting by the IoT technology, analyzes the voice features, and outputs potential VC diseases. We evaluate several classification algorithms, including eXtreme gradient boosting (XGBoost), random forest, support vector machines, and artificial neural networks, to recognize the diseases based on the voice features. In the experiments, polyps, paralysis, and Reinke’s edema are considered as the target diseases, and two scenarios are proposed: the one-to-one model and the one-to-many model. In the one-to-one model, a classification algorithm is applied to recognize exactly one VC disease, while four diseases are evaluated together in the one-to-many model. The performance in the one-to-many model is worse than that in the one-to-one model because the sound features may overlap in various diseases. However, the one-to-many model is close to the clinical environment. The experiment results show that the i-VCD with XGBoost in the one-to-one model has 94%, 100%, and 100% for polyps, paralysis, and Reinke’s edema in accuracy, respectively. The accuracy is 93% in the one-to-many model, which outperforms related approaches. Moreover, i-VCD is also deployed in a cloud service so that the physicians can get the assistance of i-VCD easily. Eventually, i-VCD provides high performance in recognizing VC diseases in a non-invasive way and is helpful in clinical consulting.
{"title":"Constructing an IOT-based assistant service for recognizing vocal cord diseases","authors":"Chen-Kun Tsung , Yung-An Tsou , Rahmi Liza","doi":"10.1016/j.iot.2024.101424","DOIUrl":"10.1016/j.iot.2024.101424","url":null,"abstract":"<div><div>In this paper, we apply the Internet of Things (IoT) technology to construct the non-invasive examination, named IoT-based vocal cords (VC) disease inference system (i-VCD), to provide the disease inference assistant framework for physicians. The proposed i-VCD tracks patient’s voice recording during consulting by the IoT technology, analyzes the voice features, and outputs potential VC diseases. We evaluate several classification algorithms, including eXtreme gradient boosting (XGBoost), random forest, support vector machines, and artificial neural networks, to recognize the diseases based on the voice features. In the experiments, polyps, paralysis, and Reinke’s edema are considered as the target diseases, and two scenarios are proposed: the one-to-one model and the one-to-many model. In the one-to-one model, a classification algorithm is applied to recognize exactly one VC disease, while four diseases are evaluated together in the one-to-many model. The performance in the one-to-many model is worse than that in the one-to-one model because the sound features may overlap in various diseases. However, the one-to-many model is close to the clinical environment. The experiment results show that the i-VCD with XGBoost in the one-to-one model has 94%, 100%, and 100% for polyps, paralysis, and Reinke’s edema in accuracy, respectively. The accuracy is 93% in the one-to-many model, which outperforms related approaches. Moreover, i-VCD is also deployed in a cloud service so that the physicians can get the assistance of i-VCD easily. Eventually, i-VCD provides high performance in recognizing VC diseases in a non-invasive way and is helpful in clinical consulting.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101424"},"PeriodicalIF":6.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-23DOI: 10.1016/j.iot.2024.101434
Youchan Park, In-Young Ko
Sensor-based Internet of Things (IoT) systems detect events from the data stream and take appropriate actions through event processing. The core of event processing, event rules, are typically defined manually by domain experts. However, there are limitations to domain experts manually setting rules for all the unlabeled events in the runtime of IoT systems. Therefore, there is a need for methods that support the generation of rules for unlabeled events. This study addresses this issue by adding two phases to the existing event processing. The first phase is the detection of unlabeled events from the data stream. Considering the characteristics of IoT systems, we propose Multi-Temporal-Scale Sampling (MulTemS), an extension of anomaly detection techniques that can detect events of various temporal-scales from multivariate time-series data. The second phase is the formation of clusters among similar events. We propose Feature-based Clustering Number prediction and Clustering (FeatCNC), which predicts the number of clusters through feature extraction and performs domain-neutral clustering. Through experiments, we demonstrate that MulTemS can effectively detect events of multiple temporal-scales, and FeatCNC can reliably cluster events across diverse domains. Additionally, we verify that the integration of these two phases results in the better formation of clusters that capture the characteristics of the events.
{"title":"Multi-temporal-scale event detection and clustering in IoT systems","authors":"Youchan Park, In-Young Ko","doi":"10.1016/j.iot.2024.101434","DOIUrl":"10.1016/j.iot.2024.101434","url":null,"abstract":"<div><div>Sensor-based Internet of Things (IoT) systems detect events from the data stream and take appropriate actions through event processing. The core of event processing, event rules, are typically defined manually by domain experts. However, there are limitations to domain experts manually setting rules for all the unlabeled events in the runtime of IoT systems. Therefore, there is a need for methods that support the generation of rules for unlabeled events. This study addresses this issue by adding two phases to the existing event processing. The first phase is the detection of unlabeled events from the data stream. Considering the characteristics of IoT systems, we propose Multi-Temporal-Scale Sampling (MulTemS), an extension of anomaly detection techniques that can detect events of various temporal-scales from multivariate time-series data. The second phase is the formation of clusters among similar events. We propose Feature-based Clustering Number prediction and Clustering (FeatCNC), which predicts the number of clusters through feature extraction and performs domain-neutral clustering. Through experiments, we demonstrate that MulTemS can effectively detect events of multiple temporal-scales, and FeatCNC can reliably cluster events across diverse domains. Additionally, we verify that the integration of these two phases results in the better formation of clusters that capture the characteristics of the events.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101434"},"PeriodicalIF":6.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-23DOI: 10.1016/j.iot.2024.101430
Jaime Pérez Díaz, Florina Almenares Mendoza
Authorization models are pivotal in the Internet of Things (IoT) ecosystem, ensuring secure management of data access and communication. These models function after authentication, determining the specific actions that a device is allowed to perform. This paper aims to provide a comprehensive and comparative analysis of authorization solutions within IoT contexts, based on the requirements identified from the existing literature. We critically assess the functionalities and capabilities of various authorization solutions, particularly those designed for IoT cloud platforms and distributed architectures. Our findings highlight the urgent need for further development of authorization models optimized for the unique demands of IoT environments. Consequently, we address both the persistent challenges and the gaps within this domain. As IoT continues to reshape the technological landscape, the refinement and adaptation of authorization models remain imperative ongoing pursuits.
{"title":"Authorization models for IoT environments: A survey","authors":"Jaime Pérez Díaz, Florina Almenares Mendoza","doi":"10.1016/j.iot.2024.101430","DOIUrl":"10.1016/j.iot.2024.101430","url":null,"abstract":"<div><div>Authorization models are pivotal in the Internet of Things (IoT) ecosystem, ensuring secure management of data access and communication. These models function after authentication, determining the specific actions that a device is allowed to perform. This paper aims to provide a comprehensive and comparative analysis of authorization solutions within IoT contexts, based on the requirements identified from the existing literature. We critically assess the functionalities and capabilities of various authorization solutions, particularly those designed for IoT cloud platforms and distributed architectures. Our findings highlight the urgent need for further development of authorization models optimized for the unique demands of IoT environments. Consequently, we address both the persistent challenges and the gaps within this domain. As IoT continues to reshape the technological landscape, the refinement and adaptation of authorization models remain imperative ongoing pursuits.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101430"},"PeriodicalIF":6.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.iot.2024.101431
Amr Adel , Noor HS Alani , Tony Jan
The transition from industry 4.0 to industry 5.0 represents a profound paradigm shift in the manufacturing domain, emphasizing the convergence of advanced digital technologies with human-centric collaboration, sustainability, and operational resilience. This paper rigorously investigates the transformative potential of softwarization and servitization within sophisticated cloud architectures, which are pivotal enablers for the realization of Factories-of-the-Future (FoF) in the industry 5.0 context. We explore the technical intricacies of softwarization, harnessing technologies such as Software-Defined Networking (SDN), Network Functions Virtualization (NFV), Artificial Intelligence as a Service (AIaaS), and cloud-native architectures to achieve unprecedented levels of flexibility, scalability, and operational efficiency in manufacturing ecosystems. Concurrently, servitization facilitates a shift from traditional product-centric models to dynamic, service-oriented frameworks, enabling highly customizable, on-demand manufacturing processes and significantly enhancing customer engagement. By meticulously examining the symbiotic relationship between these technologies, this paper presents a comprehensive roadmap that addresses critical technical challenges, including scalability, interoperability with legacy systems, and robust cybersecurity in distributed environments. Furthermore, we identify emergent opportunities such as AI-driven predictive maintenance, large-scale hyper-personalization, and the dynamic orchestration of Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) paradigms. Our contributions offer valuable insights for researchers, industry experts, and stakeholders, aiming to fully leverage the potential of Industry 5.0 to drive innovation and transform global manufacturing practices.
{"title":"Factories of the future in industry 5.0—Softwarization, Servitization, and Industrialization","authors":"Amr Adel , Noor HS Alani , Tony Jan","doi":"10.1016/j.iot.2024.101431","DOIUrl":"10.1016/j.iot.2024.101431","url":null,"abstract":"<div><div>The transition from industry 4.0 to industry 5.0 represents a profound paradigm shift in the manufacturing domain, emphasizing the convergence of advanced digital technologies with human-centric collaboration, sustainability, and operational resilience. This paper rigorously investigates the transformative potential of softwarization and servitization within sophisticated cloud architectures, which are pivotal enablers for the realization of Factories-of-the-Future (FoF) in the industry 5.0 context. We explore the technical intricacies of softwarization, harnessing technologies such as Software-Defined Networking (SDN), Network Functions Virtualization (NFV), Artificial Intelligence as a Service (AIaaS), and cloud-native architectures to achieve unprecedented levels of flexibility, scalability, and operational efficiency in manufacturing ecosystems. Concurrently, servitization facilitates a shift from traditional product-centric models to dynamic, service-oriented frameworks, enabling highly customizable, on-demand manufacturing processes and significantly enhancing customer engagement. By meticulously examining the symbiotic relationship between these technologies, this paper presents a comprehensive roadmap that addresses critical technical challenges, including scalability, interoperability with legacy systems, and robust cybersecurity in distributed environments. Furthermore, we identify emergent opportunities such as AI-driven predictive maintenance, large-scale hyper-personalization, and the dynamic orchestration of Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) paradigms. Our contributions offer valuable insights for researchers, industry experts, and stakeholders, aiming to fully leverage the potential of Industry 5.0 to drive innovation and transform global manufacturing practices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101431"},"PeriodicalIF":6.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advancements in Medical Internet of Things (MIoT) technology ease remote health monitoring and effective management of medical devices. However, these developments also expose systems to novel cyber security risks as sophisticated threat actors exploit infrastructure vulnerabilities to access sensitive data or deploy malicious software, threatening patient safety, device reliability, and trust. This paper introduces a lightweight dynamic risk assessment approach using scenario-based simulations to analyse cyber security events in MIoT infrastructures and supplement cyber security activities within organisations. The approach includes synthetic data and threat models to enrich discrete-event simulations, offering a comprehensive understanding of emerging threats and their potential impact on healthcare settings. Our simulation scenario illustrates the model’s behaviour in processing data flows and capturing the characteristics of healthcare settings. Our findings demonstrate its validity by highlighting potential threats and mitigation strategies. The insights from these simulations highlight the model’s flexibility, enabling adaptation to various healthcare settings and supporting continuous risk assessment to enhance MIoT system security and resilience.
{"title":"Dynamic risk assessment approach for analysing cyber security events in medical IoT networks","authors":"Ricardo M. Czekster , Thais Webber , Leonardo Bertolin Furstenau , César Marcon","doi":"10.1016/j.iot.2024.101437","DOIUrl":"10.1016/j.iot.2024.101437","url":null,"abstract":"<div><div>Advancements in Medical Internet of Things (MIoT) technology ease remote health monitoring and effective management of medical devices. However, these developments also expose systems to novel cyber security risks as sophisticated threat actors exploit infrastructure vulnerabilities to access sensitive data or deploy malicious software, threatening patient safety, device reliability, and trust. This paper introduces a lightweight dynamic risk assessment approach using scenario-based simulations to analyse cyber security events in MIoT infrastructures and supplement cyber security activities within organisations. The approach includes synthetic data and threat models to enrich discrete-event simulations, offering a comprehensive understanding of emerging threats and their potential impact on healthcare settings. Our simulation scenario illustrates the model’s behaviour in processing data flows and capturing the characteristics of healthcare settings. Our findings demonstrate its validity by highlighting potential threats and mitigation strategies. The insights from these simulations highlight the model’s flexibility, enabling adaptation to various healthcare settings and supporting continuous risk assessment to enhance MIoT system security and resilience.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101437"},"PeriodicalIF":6.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.iot.2024.101417
Alba Amato , Dario Branco , Beniamino Di Martino , Caterina Fedele , Salvatore Venticinque
The combination of Task Scheduling (TS) approaches in Multi-Agent Systems (MAS) and Path Finding (PF) can produce a wide range of solutions in several application contexts, such of logistics, sweeping and cleaning of large areas, and surveillance missions by Unmanned Vehicles. This paper presents a task assignment method for multi-agents-based collaborative sweep covering, where it is relevant to follow the optimal route in order to maximize the expected results with the available resources and constraints. The designed solution uses a smart planner, which computes optimal routes, and a centralized scheduler that assigns tasks to unmanned robots according to different priority queues. The prototype implementation integrates off-the-shelf IoT technologies to drive a simple robot in a controlled environment. Image processing technologies are used either to estimate in advance the expected reward for the planned route and afterward to get a feedback about the task execution.
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