Internet of Things最新文献

{"title":"Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0","authors":"Thao Nguyen-Da ,&nbsp;Phuong Nguyen-Thanh ,&nbsp;Ming-Yuan Cho","doi":"10.1016/j.iot.2024.101280","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101280","url":null,"abstract":"<div><p>Anomaly detection for industrial diesel generators, in which unexpected faults could lead to severe consequences, is still challenged due to their complex structure and nonstationary operation. Maintenance engineers who manually audit diesel generators for anomaly detection require significant expertise and knowledge. This study proposes a real-time intelligent AIoT system-based convolution neural network long short-term memory (CNN-LSTM) to enhance efficiency and decrease labor costs of industrial diesel generator maintenance service. The AIoT system could autonomously classify abnormal conditions of industrial diesel generators through supervised learning techniques. Several anomaly failure conditions are identified by maintenance experts and are simulated in the laboratory to collect the working parameters based on developed IoT modules. Pearson product-moment coefficients are computed to effectively evaluate the interdependence between collected variables and the target anomaly types. The proposed CNN-LSTM structure is hyperparameter fine-tuning for identifying the most critical configurations in failure-diagnosing applications. The developed approach is comprehensively analyzed and evaluated with other state-of-the-art individual deep learning algorithms, including recurrent neural network (RNN), LSTM, gate-recurrent unit (GRU), and CNN. The experiment results indicate that the proposed hybrid CNN-LSTM could achieve distinguished diagnosis precision of anomaly conditions of industrial diesel generators and significantly improve the classified performance in Industry 4.0.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582135","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}

{"title":"Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things","authors":"Stefano Milani ,&nbsp;Domenico Garlisi ,&nbsp;Carlo Carugno ,&nbsp;Christian Tedesco ,&nbsp;Ioannis Chatzigiannakis","doi":"10.1016/j.iot.2024.101266","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101266","url":null,"abstract":"<div><p>Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative data aggregation models. The cloud edge computing continuum (CECC) has been proposed as an evolution of the traditional central ultra-high-end processing cloud into a continuum of collaborative processing elements distributed from the cloud to the network edge. Until today, incorporating existing centralized and monolithic LPWAN architectures in the CECC faces multiple security-related implications. We propose <span>Edge2LoRa</span>, a complete secure solution to incorporate LPWAN architectures in CECC enabling faster data processing while reducing the transmission of sensitive data. It improves network performance through data pre-processing, traffic flow optimization, and real-time local analysis. <span>Edge2LoRa</span> gradually transform existing LPWAN deployments into agile and versatile infrastructures that enable the seamless and efficient processing of data throughout the CECC while guaranteeing service continuity and full-backwards compatibility. We implement <span>Edge2LoRa</span> in hardware compliant with the Things Stack and the LoRaWAN v1.0.4 and v1.1. We evaluate the performance in terms of networking and computing resource utilization, quality of service and security. The results provide a clear indication of the improvements to public and private LoRaWAN infrastructures without any disruption or service degradation for existing legacy services. In public LoRaWAN deployments where large-scale IoT data streams drive big data analytics, we demonstrate core network bandwidth usage reductions of up to 90% and data processing latency improvements by a <span><math><mrow><mo>×</mo><mn>10</mn></mrow></math></span> factor.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002075/pdfft?md5=f4be21b4d1b80588f756bd32accc24b8&pid=1-s2.0-S2542660524002075-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606569","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}

{"title":"Advancing UAV security with artificial intelligence: A comprehensive survey of techniques and future directions","authors":"Fadhila Tlili ,&nbsp;Samiha Ayed ,&nbsp;Lamia Chaari Fourati","doi":"10.1016/j.iot.2024.101281","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101281","url":null,"abstract":"<div><p>Unmanned Aerial Vehicles (UAVs) have become an integral part of modern smart cities and systems. However, the proliferation of UAVs has also brought a significant security concern. Therefore, UAVs security has attracted the interest of researchers in the field. To safeguard these leading devices, researchers and developers adopted artificial intelligence (AI) to improve the security system. Besides, emerging AI frameworks have entirely changed the landscape of UAVs quality of experience (QoE) by contributing to diverse issues. Meanwhile, owing to the novelty of the subject, AI-based frameworks for UAVs security were not carefully surveyed. The purpose of this study is to present a comprehensive survey of AI-based solutions for UAVs security provisioning through different techniques and algorithms. We assess and identify the recent AI solutions and the open research problems in the emerging field of UAV security. The study begins with an overview of UAV basics, their primary components, and the different communication links among networked UAVs. In addition, the paper explores UAVs security issues, threats, and attacks. This led to a discussion of proposed AI-based solutions for prevention and detection. Moreover, the paper categorizes AI-based solutions for UAV security and proposes a taxonomy to organize these approaches effectively. Finally, the review identifies open issues and future directions, offering valuable insights for researchers. The study methodology includes an extensive review of relevant publications in the past five years across UAVs, cyber-attacks, and AI. Therefore, this review presents an essential resource for understanding the current landscape of AI-based solutions for UAV security.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606572","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}

{"title":"Personal Autonomy and Human Corporeality: What Protects the Right to Personal Integrity?","authors":"","doi":"10.1016/j.iot.2024.101279","DOIUrl":"10.1016/j.iot.2024.101279","url":null,"abstract":"<div><p>This paper discusses the controversial and evolving nature of the right to personal integrity in the context of modern legal discourse, with a specific focus on its intersection with bodily boundaries and bio- and digital technologies. It points out that the traditional understanding of this right may no longer be sufficient to protect individuals, especially given the advancements in digital technologies, the Internet of bodies (IoB), as well as the challenges posed by the COVID-19 pandemic.</p><p>This research highlights the ongoing debate and legal complexities surrounding the right to personal integrity and personal autonomy, especially in the face of technological advancements, and suggests the need for a broader perspective to safeguard human dignity and physicality, both in the physical and digital domains. For these purposes, the evolving legal landscape is explored, with reference to the case law of the Court of Justice of the European Union (CJEU) and the European Court of Human Rights (ECtHR).</p><p>We believe that the classic concept of the right to personal integrity no longer ensures the security of the individual, and in order to achieve this goal, it is necessary to expand its scope taking into account the human dignity, especially in the context of the continuous progress of bio- and digital technologies.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698608","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}

{"title":"Drift-based task management in support of pervasive edge applications","authors":"Thanasis Moustakas,&nbsp;Kostas Kolomvatsos","doi":"10.1016/j.iot.2024.101277","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101277","url":null,"abstract":"<div><p>The Internet of Things (IoT) creates a sprawling network where multiple devices can interact, enabling a variety of devices to communicate, collect information, and carry out tasks in support of services that end users may enjoy. Edge Computing, known for its lower latency compared to the Cloud, has sparked interest in managing the execution of tasks within a huge ecosystem that acts as a cover upon the IoT infrastructure. The primary challenge lies in maximizing the use of limited edge resources while minimizing response time, prompting numerous research endeavors. However, existing efforts often overlook shifts in the collected data that may affect the execution of tasks and the production of knowledge. This paper focuses on developing a mechanism that considers data and concept drifts to optimize the management of tasks. The ultimate goal is to maximize the accuracy levels while optimizing resource utilization, through a tasks’ offloading scheme that accounts for distribution-based similarity, offering substantial benefits in managing these constrained resources efficiently. The shifts in data can help determine if the execution of a task is efficient in a specific node and become part of a reasoning model that guides the offloading decisions. Finally, we evaluate our model using a large set of experimental scenarios.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582132","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}

{"title":"Towards a distributed nodes selection mechanism for federated learning applied to blockchain-based IoT","authors":"Mohammed Riyadh Abdmeziem ,&nbsp;Hiba Akli ,&nbsp;Rima Zourane ,&nbsp;Amina Ahmed Nacer","doi":"10.1016/j.iot.2024.101276","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101276","url":null,"abstract":"<div><p>Integrating blockchain (BC) with Federated Learning (FL) shows promise but presents challenges, particularly in the selection of the most appropriate IoT nodes for sensitive tasks. Existing Artificial Intelligence (AI) based approaches are tailored to dynamic environments, but they are complex and resource-intensive. On the other hand, score-based methods are faster to implement but lack flexibility. In this paper, we propose a two-step hybrid solution which uses the reputation score approach to train a DRL model, creating a framework that combines the efficiency of deterministic methods with the adaptability of AI-based solutions. In fact, we designed a score-based method relying on devices attributes and behavior making the system operational from the outset. Also, this allows the gathering of relevant real-time data for training the DRL model. Besides, the variations in the performances of IoT devices pose a challenge in achieving synchronous aggregation. To address this, we designed a multi-level aggregation mechanism, which allows local models to be uploaded to the BC, where an aggregator is in charge of validation. The validated models are then aggregated into intermediate models. This process continues until a global model is formed. To evaluate our approach, we created several simulation scenarios including the number of nodes to assess scalability, the dropout rate to estimate availability, and the percentage of malicious nodes to evaluate the robustness of the system against attacks. These experiments aimed to demonstrate the effectiveness of our approach. The obtained results are promising highlighting its robustness and flexibility showing improved performance, security, and availability.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582385","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}

{"title":"Strategy analysis of badminton players using deep learning from IMU and UWB wearables","authors":"Ben Van Herbruggen,&nbsp;Jaron Fontaine,&nbsp;Jonas Simoen,&nbsp;Lennert De Mey,&nbsp;Daniel Peralta,&nbsp;Adnan Shahid,&nbsp;Eli De Poorter","doi":"10.1016/j.iot.2024.101260","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101260","url":null,"abstract":"<div><p>Badminton is a fast-paced sport that requires a high level of skill and coordination. To improve their skills, players can use activity trackers to monitor different shots and activities. These trackers utilize inertial measurement units (IMUs), which measures acceleration and angular velocity on the rackets, and the ultra-wideband (UWB) sensors, which measure the location of the player. This study first analyzes the use of UWB localization for tracking badminton players on the court and analyzes the location where shots are played. Furthermore, this study focuses on using both IMU at the racket and wrist and UWB sensors to recognize strategies utilized in badminton matches, employing convolutional neural network (CNN) and Long-Short-Term Memory (LSTM) models. The goal is to classify thirteen badminton shots, as well as an extra class that contains non-shot activities. The output of this shot classifier is provided to the strategy recognition model, which can identify four main strategies, with eleven variations in total, alongside an additional class designated for non-strategy instances such as movement or rest intervals. We trained and tested the models on data from six skilled badminton players. The best results were achieved by using both IMU and UWB data. The proposed 2D-CNN achieved a shot classification accuracy of 90.9%, while the proposed LSTM achieved a strategy recognition accuracy of 80%. The results of this study suggest that neural networks can be used to effectively classify badminton shots and strategies to improve the training of badminton players, as well as to analyze match data.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582130","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}

{"title":"A methodology for ontology-based interoperability of dynamic risk assessment frameworks in IoT environments","authors":"Carmen Sánchez-Zas,&nbsp;Xavier Larriva-Novo,&nbsp;Víctor A. Villagrá,&nbsp;Diego Rivera,&nbsp;Andrés Marín-Lopez","doi":"10.1016/j.iot.2024.101267","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101267","url":null,"abstract":"<div><p>Proper cyber risk management is essential for organizations to make informed decisions and avoid potential financial losses, reputational damage, operational disruptions and other negative impacts. To this end, different institutions have defined risk analysis and risk management methodologies to address the problem and monitor cyber security in organizations. In this aspect, ontologies provide a very powerful tool for interoperability in risk management, given the heterogeneity of input information considered in the different steps of each framework and the ability they provide to perform logical reasoning in order to infer new knowledge. Throughout this study we analyze the different properties of some of the methodologies with the highest adoption rate, proposing an interoperable framework based on an ontology that allows compatibility between different systems, with a dynamic, flexible and efficient operation.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002087/pdfft?md5=9ae8797eb071b80c7d8af8828d638e33&pid=1-s2.0-S2542660524002087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582383","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}

{"title":"SAFE: An ICT platform for supporting monitoring, localization and rescue operations in case of earthquake","authors":"","doi":"10.1016/j.iot.2024.101273","DOIUrl":"10.1016/j.iot.2024.101273","url":null,"abstract":"<div><p>Earthquakes are unpredictable natural events that cause a high number of casualties every year. During an earthquake, furniture and mobile equipment can either become obstacles or act as passive life safety systems by providing safe shelter for people involved in the event. The combination of Internet of Things (IoT) devices and anti-seismic furniture could serve as a means to transform them into intelligent protection systems to protect people in case of an earthquake, facilitating their detection and localization during the rescue operations through the integration of a supportive Information and Communication Technology (ICT) infrastructure. In this article, we report about the design and development of the SAFE ICT Platform which comprises wireless IoT devices integrated into anti-seismic furniture that activate in response to earthquakes to signal the presence of individuals needing rescue. The SAFE ICT Platform also includes local and cloud computing systems for device management and monitoring, alongside a localization and support system to assist rescuers in pinpointing and aiding individuals. A comprehensive evaluation scenario of the proposed solution is also discussed.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002142/pdfft?md5=2c87d21dff5739b6deb18fc794d75169&pid=1-s2.0-S2542660524002142-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622331","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}

{"title":"Comparative analysis of agricultural IoT systems: Case studies IoF2020 and CyberGreen","authors":"M. Muñoz,&nbsp;R.A. González Morales,&nbsp;J.A. Sánchez-Molina","doi":"10.1016/j.iot.2024.101261","DOIUrl":"https://doi.org/10.1016/j.iot.2024.101261","url":null,"abstract":"<div><p>The Internet of Things (IoT) has revolutionized the agro-industrial sensor field, introducing new monitoring systems that go beyond traditional machine-to-machine (M2M) communication. This article reviews various IoT systems dedicated to data acquisition in the agro-industrial sector, with a particular emphasis on their monitoring capabilities and limitations in challenging environments such as greenhouses. The research involves testing different commercial systems using IoT stations. Given the complexity of monitoring climate and irrigation variables within greenhouses, these systems must withstand extreme weather conditions while ensuring robustness and quality in data collection. The article highlights the requirements and characteristics essential for effective monitoring, thereby providing valuable insights into the selection and optimization of data loggers in agricultural settings.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2542660524002026/pdfft?md5=a9e452342d0bb29b4256429a1a7870d8&pid=1-s2.0-S2542660524002026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582382","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}