Internet of Things最新文献

{"title":"Photoplethysmography signals and physiological data in feature engineering and machine learning algorithms to calculate human-obesity-related indices","authors":"Chih-Ta Yen,&nbsp;Chia-Hsang Chang,&nbsp;Jung-Ren Wong","doi":"10.1016/j.iot.2025.101503","DOIUrl":"10.1016/j.iot.2025.101503","url":null,"abstract":"<div><div>The study developed a method based on photoplethysmography (PPG) and machine learning algorithms to predict three human-obesity-related indices: body mass index (BMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). This method eliminates the need for conventional, complex medical imaging examinations, such as computed tomography scans or magnetic resonance imaging. These conventional methods are not only time-consuming and expensive but computed tomography scans may also result in unnecessary radiation exposure to the body. PPG-based technology enables easy measurements without the need for complicated examination and measurement processes. In the proposed method, PPG signals are recorded and then processed to obtain statistical features, such as mean and variance. Subsequently, the measured data and extracted features are used in machine learning algorithms to predict human-obesity-related indices. Several feature engineering methods were employed to enhance the accuracy of our method, with the mean absolute errors for BMI, VAT, and SAT estimates decreasing from 0.419 to 0.228, from 0.624 to 0.563, and from 2.092 to 0.500, respectively. The results of the study indicate that combining PPG technology with machine learning and feature engineering methods is a convenient and effective method for measuring human-obesity-related indices. The information obtained through this method can enable individuals to understand their health status and adopt suitable measures for health management and disease prevention.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101503"},"PeriodicalIF":6.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222923","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":"Tiny keys hold big secrets: On efficiency of Pairing-Based Cryptography in IoT","authors":"Pericle Perazzo,&nbsp;Carlo Vallati","doi":"10.1016/j.iot.2025.101489","DOIUrl":"10.1016/j.iot.2025.101489","url":null,"abstract":"<div><div>Pairing-Based Cryptography (PBC) is a sub-field of elliptic curve cryptography that has been used to design ingenious security protocols including Short Signatures (SS), Identity-Based Encryption (IBE), and Attribute-Based Encryption (ABE). These protocols have extremely promising applications in diverse scenarios, including Internet of Things (IoT), which usually involves computing devices with limited processing, memory, and energy capabilities. Many studies in the literature evaluated the performance of PBC on typical IoT devices, giving promising results, and showing that a large class of constrained devices can run PBC schemes. However, in the last years, new advancements in Number Field Sieve algorithms threatened the security of PBC, so that all protocols must be re-parametrized with larger keys to maintain the same security level as before. Therefore, past literature reporting PBC performance on IoT devices must be redone because optimistic, and it is not clear whether present IoT devices will bear PBC. In this paper we evaluate the performance of some prominent PBC schemes on a very constrained device, namely the Zolertia RE-Mote platform, which is equipped with an ARM Cortex-M3 processor. From our experiments, the usage of IBE and SS schemes is still possible on IoT devices, but the security level is limited to 80 or 100 bits. Reaching greater security levels leads to higher execution times, which might not be compatible with many IoT applications. The usage of ABE is efficient only with IoT-oriented schemes, which offer good performance at the cost of a limited policy expressiveness.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101489"},"PeriodicalIF":6.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222959","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":"Intelligent human activity recognition for healthcare digital twin","authors":"Elif Bozkaya-Aras ,&nbsp;Tolga Onel ,&nbsp;Levent Eriskin ,&nbsp;Mumtaz Karatas","doi":"10.1016/j.iot.2025.101497","DOIUrl":"10.1016/j.iot.2025.101497","url":null,"abstract":"<div><div>Human activity recognition and healthcare monitoring are becoming increasingly popular as cost-effective and innovative solutions to improve the standard of healthcare in the era of Industry 4.0. The concept of the Internet of Healthcare Things (IoHT) supports these solutions and builds a virtualized and software-controlled infrastructure. This new approach leads to the development of new concepts by digitalizing and connecting everything. Despite the significant advancements in IoHT, there are still challenges in processing vast amounts of data and handling resource-limited devices. In this regard, digital twin technology is an emerging tool to enhance IoHT services. With the help of digital twin, data processing at the edge devices can effectively overcome these challenges by reducing data transfer limitations and latency while improving prediction accuracy. In this paper, we present an intelligent human activity recognition framework in healthcare digital twin services. Our framework creates digital twins of wearable and portable devices/sensors in the physical network, collects real-time and historical data, and applies advanced analytics for feedback. The main contributions of this paper are: (<em>i</em>) We propose a novel four-layer digital twin architecture framework for human activity recognition. (<em>ii</em>) We discuss how the layered architecture and data processing at the edge devices enhance decision-making and classification accuracy. It is also aimed to design an environment where data with different characteristics, priorities, and transmission timings (i.e., regularly transmitted and critical) are comprised so that we can measure the same state through multiple sensors to improve system performance. (<em>iii</em>) We develop an Artificial Neural Network (ANN) based model and evaluate the proposed digital twin-assisted model using two different datasets. The results show the benefits of the proposed digital twin-assisted framework, providing feedback to individuals.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101497"},"PeriodicalIF":6.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222958","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":"HYRIDE: HYbrid and Robust Intrusion DEtection approach for enhancing cybersecurity in Industry 4.0","authors":"Shubham Srivastav ,&nbsp;Amit K. Shukla ,&nbsp;Sandeep Kumar ,&nbsp;Pranab K. Muhuri","doi":"10.1016/j.iot.2025.101492","DOIUrl":"10.1016/j.iot.2025.101492","url":null,"abstract":"<div><div>The interconnectedness and smartness aspect between several components of Industry 4.0 has caused sudden increase in data and its exchange, which has resulted in significant cybersecurity challenges. Thus, a better threat intelligence technique is required for monitoring and identifying malicious cyberattacks. However, distinguishing between a normal event and a cyberattack can be difficult because label information is mostly unavailable. Therefore, it is imperative to develop a threat intelligence system that operates more effectively without supervision, i.e., without a label. Additionally, reducing the false positive rate in cyber threat detection is a more promising step for a safer and more reliable environment. Also, the enormous number of features in the data for intrusion detection tasks sometimes results in significant computing costs. Therefore, a novel hybrid feature selection based unsupervised intrusion detection system is proposed, which is termed as HYbrid and Robust Intrusion DEtection (HYRIDE), that uses a wide variety of feature selection techniques to obtain the fewest, best possible features. The local outlier factor, elliptic envelope, and histogram-based outlier score models are then trained using these features to identify threats in network traffic automatically. As a result, HYRIDE can effectively and efficiently distinguish between normal events and intrusions. The proposed methodology is empirically evaluated using popular datasets such as Telemetry datasets of Internet of Things (IoT) services, Operating systems datasets of Windows and Linux, as well as datasets of Network traffic (TON_IoT), University of New South Wales-Network Benchmark (UNSW-NB15), and Canadian Institute of Cybersecurity Intrusion Detection System (CICIDS 2017).</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101492"},"PeriodicalIF":6.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222925","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":"ORDIP: Principle, Practice and Guidelines for Open Research Data in Indoor Positioning","authors":"Grigorios G. Anagnostopoulos ,&nbsp;Paolo Barsocchi ,&nbsp;Antonino Crivello ,&nbsp;Cristiano Pendão ,&nbsp;Ivo Silva ,&nbsp;Joaquín Torres-Sospedra","doi":"10.1016/j.iot.2024.101485","DOIUrl":"10.1016/j.iot.2024.101485","url":null,"abstract":"<div><div>The community of indoor positioning research has identified the need for a paradigm shift towards more reproducible and open research dissemination. Despite recent efforts to openly share data and code, accompanying research results with Open Research Data (ORD) is far from being the <em>de facto</em> standard option for publications in the indoor positioning field. The lack of recognized public benchmarks and the rather slow adoption of ORD, set a great volume of astute contributions in the field to remain irreproducible. Performance comparisons may often be made on experiments performed in different settings, hindering their consistency, and eventually slowing down progress and the evolution of knowledge in the field. In this work, we systematically review the landscape of Open Research Data in Indoor Positioning, enlisting, presenting, and analyzing the characteristic features of the relevant available open datasets of the field. As a result of our systematic review, the statistical analysis of the 119 identified open datasets, highlights the tendencies and the missing elements, such as underrepresented technologies (such as Ultra-Wideband) and measurement types (such as Angle of Arrival, Time Difference of Arrival). A result that stands out is the frequency of crucial metadata information that remains undefined, such as the size of the area of collection (50% of the datasets), the ground truth collection protocol (21%), or the environment type (13%). As a fruit of the systematic analysis, we discuss potential shortcomings, and we share lessons learned and observed good practices regarding the provision of a new ORD and the reuse of existing ones. A significant practical contribution of this work is a list of guidelines that researchers aiming to collect and share a new ORD can follow as a simple checklist. In a broader context, we consider that ORDIP can help measure the future progress of the Indoor Positioning field in the ORD front through the snapshot of the current landscape that it provides. The Open provision of our full systematic analysis of the ORD can serve as a look-up table for easy access to the ORDs containing the most relevant features for each interested researcher, while our guidelines aim to support the community and spark the discussion towards a consensus-based standard for ORD of the field.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101485"},"PeriodicalIF":6.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223226","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":"Distributed inference in IoT-based aerial network of UAVs","authors":"HyungBin Park,&nbsp;SuKyoung Lee,&nbsp;ShinYoung Cho","doi":"10.1016/j.iot.2024.101479","DOIUrl":"10.1016/j.iot.2024.101479","url":null,"abstract":"<div><div>Interest in unmanned aerial vehicle (UAV) has increased drastically due to its ability as a mobile internet of things (IoT) device and capability of accessing remote regions. Due to its robust capabilities with the additional compute capabilities of the onboard IoT device, its tasks range widely from monitoring smart farms to surveillance and rescue related missions. The rise in intelligent tasks has led to an increase in required computing resources as more of these tasks utilize deep neural networks (DNNs) for inference. However, the computing resources of these IoT devices are fixed and limited onboard an UAV and require additional assistance. Some works have suggested UAV–edge collaboration, yet face restrictions for remote deployment. Other works have suggested UAV-to-UAV collaboration, but have not considered simultaneously occurring requests for multiple DNNs and heterogeneous IoT hardware onboard UAVs. Motivated by the need for performing critical tasks in remote areas with heterogeneity in the requested DNNs, we propose a deployment which considers such information during planning. We test our proposed deployment with several others methods to compare e2e latency performance and energy efficiency. Proposed deployment demonstrates lower e2e latency and greater energy efficiency than all other comparison methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101479"},"PeriodicalIF":6.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222883","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":"Low-cost chronobiological monitoring: A tested IoT-enabled diagnostic tool in tropical and Antarctic environments","authors":"Daniela P.A. Marins ,&nbsp;Wesley S. Costa ,&nbsp;Bruno P. de S. Rocha ,&nbsp;Jordano R. Celestrini ,&nbsp;Cristina E. de Alvarez ,&nbsp;Marcelo E.V. Segatto","doi":"10.1016/j.iot.2024.101475","DOIUrl":"10.1016/j.iot.2024.101475","url":null,"abstract":"<div><div>Light intensity and spectral composition notably impact the human circadian rhythm. The human body is a physiological system that regulates its sleep-awake cycle through a constant rhythm of light and darkness. For a long time, the lighting research field has been concerned with understanding this circadian rhythm to improve people’s quality of life. To better understand the influence of light on the human circadian rhythm, a remote monitoring device was developed that reliably measures the light spectrum and human circadian rhythm in different environments, including Antarctica and a tropical location study. The designed apparatus aims to facilitate the comprehension of the impact of light on the human circadian rhythm and provide accessible measurements through cost-effective tools. Results show that the developed monitoring prototype can collect and transmit environmental and human data. Therefore, the low-cost equipment developed can be reproduced and used by research institutions to collect data in different environments and improve the understanding of the influence of light on human activities. The cross-sectional analysis of the collected data revealed evidence of the significant influence of light on regulating the human circadian rhythm in tropical and Antarctica case studies. The collected information makes it possible to predict human reactions to the light environment, correlate these responses with seasonal periods, and comprehend how various forms of artificial and natural light interact with individuals and their living spaces. This prototype offers a non-invasive tool for assessing sleep quality and daytime activities, providing knowledge of how lighting conditions can impact overall well-being.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101475"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182410","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":"A rapid household mite detection and classification technology based on artificial intelligence-enhanced scanned images","authors":"Lydia Hsiao-Mei Lin ,&nbsp;Wei-Cheng Lien ,&nbsp;Cindy Yu-Ting Cheng ,&nbsp;You-Cheng Lee ,&nbsp;Yi-Ting Lin ,&nbsp;Chin-Chia Kuo ,&nbsp;Yi-Ting Lai ,&nbsp;Yan-Tsung Peng","doi":"10.1016/j.iot.2024.101484","DOIUrl":"10.1016/j.iot.2024.101484","url":null,"abstract":"<div><div>Household mites, recognized as a principal allergen, can induce allergic rhinitis in over 90 % of patients worldwide. It is indispensable to accurately assess mite pollutant exposure within living environments to heighten awareness regarding mite prevention. Current techniques for household mite detection and quantification, however, suffer from limitations such as complex sampling requirements, time-consuming analysis processes, and high costs, which ultimately contribute to a lack of awareness among residents. Therefore, this study develops an innovative artificial intelligence (AI) technique with multi-feature fusion for household mite detection and classification to evaluate indoor mite infestation levels. This system incorporates a symmetric Generative Adversarial Network (GAN) and multiple Image Signal Processing (ISP) models to not only enhance the visual quality of images obtained from scanned Dust Mite Traps but also facilitate data augmentation, thus significantly improving the detection, classification, and quantification accuracy of two prevalent household mite species: dust mite and Cheyletid mite. With the enhanced You Only Look Once (YOLO) model, the integrated AI framework demonstrates rapid and precise mite detection and quantification, achieving an accuracy rate of 85.4 % and a counting error of only 7.1 %. Furthermore, the visualization process improves human visual interpretation, effectively raising awareness about dust mite contamination for indoor environment quality. The proposed AI models offer a cost-effective, efficient tool for assessing mite infestation within homes and increase awareness about mite protection, thereby reducing the risks of exposure to indoor allergens.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101484"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182020","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":"Time-efficient approximate trajectory planning for AoI-centered multi-UAV IoT networks","authors":"Amirahmad Chapnevis,&nbsp;Eyuphan Bulut","doi":"10.1016/j.iot.2024.101461","DOIUrl":"10.1016/j.iot.2024.101461","url":null,"abstract":"<div><div>The gathering of data produced by ground Internet of Things (IoT) devices can be facilitated with the assistance from Unmanned Aerial Vehicles (UAVs) especially in hard-to-reach areas. However, the limited battery of UAVs requires a careful planning of their trajectories. As the timely delivery of data can be critical in certain applications, Age of Information (AoI) should also be integrated during this planning. Most of the existing works that study AoI-centered UAV trajectory planning focus on the timing of the data gathering by the UAV, without considering the time UAV needs to deliver it to a specific point. This study broadens the perspective by incorporating multiple UAVs and Ground Base Stations (GBSs) throughout the region, to be used for the delivery of data collected by UAVs, defining the AoI. We also allow UAVs to visit IoT locations only after a data is generated, which can happen during the mission of UAVs. Our goal is to optimize the UAV trajectories considering multiple prioritized goals, namely, minimization of maximum AoI, then the minimization of sum of AoI for all collected data and finally the sum of UAV path lengths. Using Integer Linear Programming (ILP), we first find out the optimal solution. In order to avoid the long running times and provide a scalable yet time-efficient solution, we propose a heuristic based method. Extensive simulation results under various setups show that the heuristic approach provides results with reasonable margins to ILP results and is also scalable, making the proposed solution more practical.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101461"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181541","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":"RAAF-MEC: Reliable and anonymous authentication framework for IoT-enabled mobile edge computing environment","authors":"Omar Alruwaili ,&nbsp;Muhammad Tanveer ,&nbsp;Saud Alhajaj Aldossari ,&nbsp;Saad Alanazi ,&nbsp;Ammar Armghan","doi":"10.1016/j.iot.2024.101459","DOIUrl":"10.1016/j.iot.2024.101459","url":null,"abstract":"<div><div>The Internet of Things (IoT) devices are becoming increasingly integral to daily life, with cloud computing platforms serving as essential hubs for managing and processing the vast data generated by distributed IoT devices and sensors. The advent of 6G-powered cloud services facilitates applications such as augmented reality, virtual reality, autonomous driving, and healthcare, all of which require rapid data processing. Mobile edge computing (MEC) extends cloud capabilities to the network’s edge, enabling large-scale, real-time data processing. However, this transition introduces security challenges due to the open nature of MEC infrastructures, which increases the risk of data breaches and privacy violations. To address these challenges, RAAF-MEC is proposed as an innovative authentication framework designed specifically for IoT-enabled MEC environments. The framework incorporates hash functions, PUF, ECC, and GIFT-COFB. GIFT-COFB, a lightweight encryption mechanism, and NIST finalist, ensures data authenticity and integrity. PUF technology, integrated on the MEC server side, dynamically derives secret keys, mitigating the risk of privileged insider attacks by eliminating the need to store keys in the MEC server’s database. This approach enhances security by preventing unauthorized access to sensitive key material. RAAF-MEC also supports single sign-on for seamless access across MEC servers. The effectiveness of RAAF-MEC has been validated through comprehensive formal and informal security assessments, as well as performance evaluations against existing authentication frameworks. Our results show that RAAF-MEC reduces computational costs by 27.3% to 52.12% and communication costs by 69.44% to 75%, while significantly enhancing security features.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"29 ","pages":"Article 101459"},"PeriodicalIF":6.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181554","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}