Internet of Things最新文献

{"title":"A Multi-Sensor IoT Platform for monitoring medicine storage beyond the hospital","authors":"Maria Frontera-Bergas ,&nbsp;Miguel Vinaixa-Fernández ,&nbsp;Bartomeu Oliver-Riera ,&nbsp;Jaume Ramis-Bibiloni ,&nbsp;Eugeni Isern ,&nbsp;Bartomeu Alorda-Ladaria","doi":"10.1016/j.iot.2025.101711","DOIUrl":"10.1016/j.iot.2025.101711","url":null,"abstract":"<div><div>Ambient-sensitive pharmaceutical products degrade when they are exposed to unsuitable environmental conditions such as inappropriate temperature, humidity, light, or mechanical stress. While hospitals ensure proper storage, these conditions are often compromised once medications reach ambulatory patients. This paper presents a novel Internet of Things (IoT)-based platform architecture to monitor drug conservation beyond hospital facilities, extending into patients’ homes. The proposed system comprises a <em>monitoring network</em> and a <em>servers system</em>. The <em>monitoring network</em> includes sensor nodes placed within the original outer packaging of the medication, capturing key environmental variables, and patient-installed gateways that locally process and transmit data to the <em>servers system</em> for near real-time storage and analysis. The <em>servers system</em> detects preservation issues and generates alerts, notifying both patients and hospital pharmacy professionals to enable timely corrective actions. Locally, the gateway warns patients of suboptimal storage conditions, allowing immediate intervention. This platform fosters collaboration between patients and pharmacists, ensuring effective follow-up on drug conservation and administration. Special consideration has been given to energy efficiency to extend sensor node battery life, making the system practical for real-world deployments. The implemented sensor node collects data every 10 s to be transmitted to the gateway, resulting in an estimated battery life of 17.5 months—sufficient for long-term operation, without frequent maintenance. By enabling continuous drug quality monitoring beyond hospitals, including at patients’ homes, this platform supports safer and more effective drug therapies. This work represents a significant advancement in leveraging IoT technologies to improve medicine conservation in decentralized healthcare settings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101711"},"PeriodicalIF":7.6,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738565","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":"Cyber resilience in e-governance: A review of strategies, challenges, and directions","authors":"Pardeep Singh ,&nbsp;Seema Sirpal ,&nbsp;Om Pal","doi":"10.1016/j.iot.2025.101702","DOIUrl":"10.1016/j.iot.2025.101702","url":null,"abstract":"<div><div>As governments worldwide increasingly use digital platforms to deliver services, integrated security strategies within electronic governance (e-governance) systems are now imperative. This study provides a comprehensive review of the cybersecurity methods in e-governance focused on improving resilience and ensuring secure functioning in digital government services. It evaluates the challenges in cybersecurity frameworks, focusing on data breaches, illegal access, and emerging threats from sophisticated cyberattacks. In addition, it provides an overview of the prevailing security methods used in various global e-governance systems. Furthermore, it identifies the gaps in the literature and provides scope for further research by reviewing the current practices for enhancing e-governance security. This research highlights the persistent challenges of law-enforcing, implementing policies, and addressing underlying issues that hinder the adoption of cybersecurity measures. This work combines a wide range of global strategies and scholarly initiatives to give a unified view of present challenges and future work directions, especially when using AI and blockchain and being ready for developments after quantum computing. This study aims to enhance understanding of the development of resilient e-governance systems that are secure, adaptive, and capable of withstanding the evolving cyberthreat landscape.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101702"},"PeriodicalIF":7.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721804","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":"Energy-efficient altitude optimization in multi-UAV search and rescue: A hybrid swarm approach","authors":"Ali Hassan ,&nbsp;Rizwan Ahmad ,&nbsp;Sadaf Javed ,&nbsp;Waqas Ahmed ,&nbsp;Muhammad Sohaib J. Solaija ,&nbsp;Mohsen Guizani","doi":"10.1016/j.iot.2025.101712","DOIUrl":"10.1016/j.iot.2025.101712","url":null,"abstract":"<div><div>The Internet of Things (IoT) has revolutionized disaster response by enabling real-time data acquisition, processing, and communication through edge devices that significantly improve the efficiency of Urban Search and Rescue (USAR) operations. This work presents a novel hybrid optimization approach by integrating Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to solve the NP-hard problem of minimizing the number of UAVs required for efficient area coverage. The performance of the proposed algorithm is evaluated by providing a comparison with GA-based, PSO-based, and fixed-altitude approaches. UAV altitude, energy capacity, and coverage radius are considered as key optimization parameters. Four navigation techniques including Uniform Grid Omni Navigation, Uniform Vesica Omni Navigation, Boundary Intersect Grid Omni Navigation, and Boundary Intersect Vesica Omni Navigation are used to reduce redundant waypoints and improve energy efficiency. In addition, a comprehensive energy model is considered that links UAV altitude to coverage area and waypoint distribution, providing a critical trade-off between coverage area and energy consumption. Simulation results is validated through case studies in NUST and Masdar City which show that the hybrid grid-based approach is highly effective for both regular and irregular area coverage, offering improved efficiency and minimizing UAV deployment. The proposed approach outperforms other methods, providing an efficient sub-optimal solution for real-world USAR UAV operations.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101712"},"PeriodicalIF":7.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756919","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":"SFIX:Scalable Financial-oriented Interpretable eXplanation","authors":"Abdullah Emir Cil ,&nbsp;Kazim Yildiz","doi":"10.1016/j.iot.2025.101713","DOIUrl":"10.1016/j.iot.2025.101713","url":null,"abstract":"<div><div>The use of artificial intelligence in finance undoubtedly has a significant contribution in providing financial services to customers in a more efficient and secure manner. However, black box artificial intelligence algorithms can pose challenges in ensuring the safe functioning of financial services and monitoring desired outcomes. In this study, we have tried to design an explainable artificial intelligence method called Scalable Financial-oriented Interpretable eXplanation (SFIX) specific to the finance sector. While designing the SFIX method, time-based approaches such as fraud detection, credit scoring, customer profiling and other applications used in finance were taken into account. The accuracy and consistency of the dataset are also included in the calculations to support explainability. Finally, a simplified version of the SFIX method is also designed for quick testing of the model in case of problems in finding the real dataset.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101713"},"PeriodicalIF":7.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756918","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":"Real-time efficiency of YOLOv5 and YOLOv8 in human intrusion detection across diverse environments and recommendation","authors":"Ali Hassan Sodhro,&nbsp;Sathwik Kannam,&nbsp;Michel Jensen","doi":"10.1016/j.iot.2025.101707","DOIUrl":"10.1016/j.iot.2025.101707","url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) are essential for securing areas such as industrial and construction sites. However, when implementing IDS as a service, confidence scores (confidence) provided by YOLOv8 are the most reliable metric as compared to the YOLOv5 available to take appropriate actions to secure these sites and prevent intruders. However, prior research has focused on YOLO’s human detection capabilities (whether it can detect or not), neglecting real-time performance in IDS. To address this gap, we propose and present comparative analysis of YOLOv5 and YOLOv8 in a real-time across diverse environmental conditions (luminance, indoor/outdoor, simulated weather). Our findings reveal an average performance of YOLOv5 (outdoor: 90.5%, indoor: 79.1%), YOLOv8 (outdoor: 99.1%, Indoor: 77.2%) confidence in real-time, with a logarithmic relationship between luminance and confidence. Outdoor environments perform better then indoor for both YOLOv5 and YOLOv8, while adverse weather conditions significantly reduce YOLOv8’s effectiveness and increase the efficiency of YOLOv5. Therefore, this enables IDS integrators to adjust minimum confidence thresholds to minimize the risk of preventing potential intruders. However, the consistent and inconsistent confidence scores by both YOLOv8 and YOLOv5 respectively, and impact of weather remains inconclusive due to simulated fog.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101707"},"PeriodicalIF":6.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711350","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":"Blockchain-enabled secure and efficient task allocation for IoT networks using enhanced fuzzy reptile search algorithm","authors":"Vinay Maurya ,&nbsp;Vinay Rishiwal ,&nbsp;Mano Yadav ,&nbsp;Preeti Yadav ,&nbsp;Rashmi Chaudhry","doi":"10.1016/j.iot.2025.101708","DOIUrl":"10.1016/j.iot.2025.101708","url":null,"abstract":"<div><div>The Internet of Things(IoT) network is rapidly expanding, and the authentication and task allocation challenges between IoT devices, sensors, nodes, and gateways are highly complex. Traditional authentication schemes and task allocation methods frequently require increased scalability to address the resource constraints inherent in IoT devices. This paper presents a blockchain-based framework for secure and efficient task allocation in IoT networks that employs the Enhanced Fuzzy Reptile Search Algorithm (EFRSA<span><math><mo>−</mo></math></span>TA). The proposed framework uses Blockchain Technology to authenticate IoT devices via Smart contracts and Blockchain cryptography digital signatures (BCDS), ensuring task allocation security and integrity. Once authenticated via blockchain, tasks are distributed to devices and sensors using EFRSA<span><math><mo>−</mo></math></span>TA, which optimizes distribution based on resource availability, device location, and task priority. EFRSA<span><math><mo>−</mo></math></span>TA operates in two phases. First, it uses fuzzy logic to categorize task priorities, improving scheduling adaptability and responsiveness. In the second phase, an Enhanced Fuzzy Reptile Search Algorithm (EFRSA) and a novel validation function are used to offload tasks that exceed a device’s processing power and current workload. Blockchain Cryptography Digital Signature (BCDS) is compared to the existing ECC, HMAC, KCDH, LAKA and JWT algorithms to assess the framework’s effectiveness. On the other hand, EFRSA<span><math><mo>−</mo></math></span>TA is compared with several state-of-the-art optimization algorithms. Simulation results show that BCDS and EFRSA-TA significantly outperform these algorithms regarding Authentication time, false acceptance rate (FAR), Uptime &amp; error rate, blockchain overhead, framework scalability analysis, task allocation rate, throughput, energy consumption, and CPU utilization, confirming its superiority in authentication and optimizing task allocation within IoT networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101708"},"PeriodicalIF":6.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711352","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":"Survey of IoT multi-protocol gateways: Architectures, protocols and cybersecurity","authors":"Salma H. Abdelwahed ,&nbsp;Ibrahim M. Hefny ,&nbsp;Mohamed Hegazy ,&nbsp;Lobna A. Said ,&nbsp;Ahmed Soltan","doi":"10.1016/j.iot.2025.101703","DOIUrl":"10.1016/j.iot.2025.101703","url":null,"abstract":"<div><div>The Internet of Things (IoT) is expanding rapidly, and IoT gateways are essential for connecting various devices and networks. Multi-protocol IoT gateways support multiple communication technologies, enabling interoperability across diverse IoT ecosystems. This survey paper investigates the hardware design, software components, and key functions of multi-protocol gateways. It provides an overview of their communication protocols, focusing on wireless technologies such as Wi-Fi, Bluetooth, Zigbee, LoRa, and 4G. Additionally, the paper discusses cybersecurity measures, including encryption, authentication, and protection against cyber threats. Both academic research and commercial IoT gateways are reviewed to present a comprehensive picture. This study identifies gaps in current research, highlighting the need for stronger cybersecurity implementation, improved energy management, and the integration of smarter systems using artificial intelligence (AI). The paper discusses challenges such as scalability, standardization, evaluation, and compatibility among various technologies. It highlights important areas for enhancing IoT gateway design by summarizing recent developments. The findings will assist researchers, developers, and industry professionals in creating more secure, efficient, and adaptable IoT gateways for the future.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101703"},"PeriodicalIF":7.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756920","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":"Dynamic transmission adaptation algorithms for battery-free LoRaWAN networks","authors":"Fabrizio Giuliano ,&nbsp;Antonino Pagano ,&nbsp;Daniele Croce ,&nbsp;Gianpaolo Vitale ,&nbsp;Ilenia Tinnirello","doi":"10.1016/j.iot.2025.101706","DOIUrl":"10.1016/j.iot.2025.101706","url":null,"abstract":"<div><div>Demand for sustainable IoT solutions has increased over the years, with energy-harvesting technologies coming to the fore, and environmental-powered sensors gaining much importance. Indeed, the benefits can be outstanding for batteryless sensors in terms of increased durability, reduced maintenance (no need for battery replacement), and higher resistance to environmental factors. However, such batteryless devices must be accurately designed to cope with time-varying energy sources, such as solar or wind power. In particular, this work investigates adaptive transmission algorithms to optimize the performance and lifetime of LoRa-based batteryless IoT sensors. First, a thorough characterization is carried out concerning the device’s power consumption, focusing on both sensor measurement and data transmission operations. The performed analysis takes into account also different network scenarios, considering possible changes of the device parameters. Second, a transmission adaptation scheme for the optimizing data transmission intervals, named <em>Uniform Transmission Adaptation</em> (UTA), is proposed. Finally, tailored energy storage solutions are developed, depending on the available energy capacity and considering direct coupling and the use of renewable sources, like photovoltaic cells. Through large-scale simulations in a massive IoT scenario, we quantitatively assess network performance, energy consumption and network efficiency. Simulations show that in massive network conditions, the Packet Delivery Ratio (PDR) reaches 87% with UTA, compared to about 70% achieved with fixed interval transmission strategies. Furthermore, the loss of energy productivity (LoEP) in the fixed transmission scenario is around 3.75% during winter, whereas with UTA it is reduced to near 0%, demonstrating a reduction in energy losses. The findings provide a basis for the design of sensor devices with optimal energy management, in order to meet given reliability requirements, and tackling important challenges of batteryless IoT networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101706"},"PeriodicalIF":6.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687258","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":"Fast and low cost FPGA-based architecture for arrhythmia detection with CNN","authors":"Luca Greco,&nbsp;Francesco Moscato,&nbsp;Pierluigi Ritrovato,&nbsp;Mario Vento","doi":"10.1016/j.iot.2025.101705","DOIUrl":"10.1016/j.iot.2025.101705","url":null,"abstract":"<div><div>Deep Neural Networks have been applied in many fields and have exhibited extraordinary abilities. However, many challenges arise when dealing with embedded or low-resource computing architectures in different contexts like healthcare or IoT in Industry 4.0. In recent years, rapid growth has been seen in using machine learning techniques to interpret sensor data in healthcare applications. Convolutional Neural Networks (CNNs) are highly effective, but they have a significant drawback: they require large amounts of computational resources, usually available only “on the Cloud”. Edge and Fog nodes in healthcare applications (e.g. wearable sensors) are generally ill-suited to running CNN models with requirements like low computational resources, real-time execution, (very) low power consumption or low intrusiveness. In order to get through these difficulties, we propose a solution based on novel data-flow architectures and layer partitioning that enables fast classification in CNNs even when dealing with low resources. We apply our approach in developing a classifier (based on CNNs) for arrhythmia detection, which maintains good precision on low-power and low-cost FPGAs. We prove that the presented approach is general enough to distribute computation on parallel FPGAs too. Results show interesting performance improvements even when using low-resource hardware to implement the classifier.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101705"},"PeriodicalIF":6.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704548","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":"Threat detection in the 6G enabled Industrial IoT Networks using Deep Learning: A review on the state-of-the-art solutions, challenges and future research directions","authors":"Gaoyang Guo ,&nbsp;Faizan Qamar ,&nbsp;Syed Hussain Ali Kazmi ,&nbsp;Muhammad Habib ur Rehman","doi":"10.1016/j.iot.2025.101686","DOIUrl":"10.1016/j.iot.2025.101686","url":null,"abstract":"<div><div>The integration of the Industrial Internet of Things (IIoT) with sixth-generation (6G) communication technology is a critical foundation for the next generation of intelligent manufacturing and industrial automation. However, this advancement introduces significant security challenges, particularly in threat detection for IIoT systems. This paper systematically reviews existing research on threat detection in 6G-IIoT environments using Deep Learning (DL) techniques. It examines key challenges related to data processing, privacy protection, and model performance. The study first outlines the security requirements of IIoT within a 6G network environment and evaluates the application of various DL models for threat detection. It then identifies key limitations in current research, including dataset imbalance and the limited generalization capability of existing models. Finally, potential future research directions are discussed to advance the development of more intelligent and efficient threat detection mechanisms, ensuring the security and stability of IIoT systems in the 6G era.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101686"},"PeriodicalIF":6.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665833","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}