Internet of Things最新文献

E-governance and the European Union: Agenda for implementation 电子政务和欧洲联盟：实施议程

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-12-01 DOI: 10.1016/j.iot.2024.101352

David Ramiro Troitino

引用次数: 0

Enhancing IoT security through emotion recognition and blockchain-driven intrusion prevention 通过情感识别和区块链驱动的入侵防御增强物联网安全

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-26 DOI: 10.1016/j.iot.2024.101442

Ernest Ntizikira , Lei Wang , Jenhui Chen , Kiran Saleem

{"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}

引用次数: 0

Insights for metrics in assessing TSCH scheduling efficiency 对评估TSCH调度效率的指标的见解

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-26 DOI: 10.1016/j.iot.2024.101440

Ivanilson França Vieira Junior , Jorge Granjal , Marilia Curado

{"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}

引用次数: 0

IoT-driven load forecasting with machine learning for logistics planning 物联网驱动的负荷预测与物流规划的机器学习

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-26 DOI: 10.1016/j.iot.2024.101441

Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub

{"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}

引用次数: 0

Constructing an IOT-based assistant service for recognizing vocal cord diseases 构建基于物联网的声带疾病识别辅助服务

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-26 DOI: 10.1016/j.iot.2024.101424

Chen-Kun Tsung , Yung-An Tsou , Rahmi Liza

{"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}

引用次数: 0

Multi-temporal-scale event detection and clustering in IoT systems 物联网系统中的多时标事件检测与聚类

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-23 DOI: 10.1016/j.iot.2024.101434

Youchan Park, In-Young Ko

{"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}

引用次数: 0

Authorization models for IoT environments: A survey 物联网环境的授权模型：调查

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-23 DOI: 10.1016/j.iot.2024.101430

Jaime Pérez Díaz, Florina Almenares Mendoza

引用次数: 0

Factories of the future in industry 5.0—Softwarization, Servitization, and Industrialization 工业 5.0 中的未来工厂--软件化、服务化和工业化

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-21 DOI: 10.1016/j.iot.2024.101431

Amr Adel , Noor HS Alani , Tony Jan

{"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}

引用次数: 0

Dynamic risk assessment approach for analysing cyber security events in medical IoT networks 用于分析医疗物联网网络安全事件的动态风险评估方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-20 DOI: 10.1016/j.iot.2024.101437

Ricardo M. Czekster , Thais Webber , Leonardo Bertolin Furstenau , César Marcon

{"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}

引用次数: 0

IoT-robotics for collaborative sweep coverage 用于协作清扫覆盖的物联网机器人

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-19 DOI: 10.1016/j.iot.2024.101417

Alba Amato , Dario Branco , Beniamino Di Martino , Caterina Fedele , Salvatore Venticinque

引用次数: 0