Internet of Things最新文献

{"title":"A study on DTN routing protocol for disaster situation while considering age of information","authors":"Fuka Isayama ,&nbsp;Tetsuya Shigeyasu","doi":"10.1016/j.iot.2025.101756","DOIUrl":"10.1016/j.iot.2025.101756","url":null,"abstract":"<div><div>It is well known that disasters make it more difficult to obtain information due to the failure of cell phone base stations and other pre-installed fixed infrastructure. In such situations, minimizing the damage caused by disasters also requires the collection of disaster-related information from affected areas. Delay/Disruption Tolerant Networking (DTN), a network technology that enables end-to-end communication, has attracted significant attention from network researchers. Accordingly, past DTN-based studies have aimed to collect as much disaster information as possible, even under unstable communication environments. However, these systems cannot handle situations where the target disaster information changes dynamically over time. To collect such information effectively, it is important to periodically retrieve target data at short intervals. Furthermore, in order to accurately understand the disaster situation, it is important to collect information under conditions that are as equitable as possible across all affected areas. According to the above background, this paper proposes TPR (Travel Path Relay) as a new DTN routing protocol for disaster information systems. TPR estimates relay nodes that are more likely to approach the destination, considering both information freshness and fairness, while maintaining a low number of message replications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101756"},"PeriodicalIF":7.6,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045848","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":"IoT and intelligent algorithms in smart cities’ transportation & mobility","authors":"Angel A. Juan ,&nbsp;Elena Perez-Bernabeu","doi":"10.1016/j.iot.2025.101727","DOIUrl":"10.1016/j.iot.2025.101727","url":null,"abstract":"","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101727"},"PeriodicalIF":7.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044587","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":"Co-creating a data-driven smart farming sensor networks for digital twin integration in Irish tillage farming","authors":"Fredrick Tom Otieno,&nbsp;Beulah Lazarus,&nbsp;Arghadyuti Banerjee,&nbsp;Khurram Riaz,&nbsp;Sudha-Rani N.V. Nalakurthi,&nbsp;Salem Gharbia","doi":"10.1016/j.iot.2025.101754","DOIUrl":"10.1016/j.iot.2025.101754","url":null,"abstract":"<div><div>Smart farming, integrating real-time environmental monitoring of soil and weather parameters, plays a critical role in advancing sustainable agriculture, improving productivity, and enhancing food security. In Ireland, the tillage sector supports approximately 10,000 farms contributing significantly to the national economy through cereal production. However, the sector faces mounting pressure to align with environmental regulations aimed at reducing greenhouse gas emissions, and soil and water pollution. There is a lack of established frameworks for on-farm environmental indicators monitoring. This study presents a pilot implementation of sensor-based monitoring within a Living Lab approach, emphasizing co-creation with tillage farmers. Through farmer engagement during farm visits and agricultural exhibitions, critical user requirements were identified as access to weather (64.7 %) and soil data (51.0 %), with 73.2 % preferring digital access. An Internet of Things-enabled system was deployed, capturing air temperature, humidity, and soil parameters (temperature, moisture, nutrients, electrical conductivity, and pH). The system allows for continuous, real-time data collection, overcoming limitations of traditional data acquisition methods. Data were transmitted and visualized on a web-based dashboard. The initial mean values were air temperature (11.9 °C), soil temperature (13.4 °C), humidity (70.55 %), nitrogen (10 mg/kg), phosphorus (3 mg/kg), potassium (40 mg/kg), pH (6.99), and EC (0.61 dS/m) which were within expected ranges for Irish conditions. These real-time data streams provide a foundation for digital twin development to later enable advanced analytics, predictive modelling, and informed decision-making. This pilot underscores the feasibility and value of smart farming approaches to enhance environmental compliance, sustainability, and policy alignment in Ireland’s tillage sector.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101754"},"PeriodicalIF":7.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096286","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":"Federated learning for sustainable intrusion detection systems: A review of green computing strategies and future directions","authors":"Jithish J. ,&nbsp;Nagarajan Mahalingam ,&nbsp;Bo Wang ,&nbsp;Kiat Seng Yeo","doi":"10.1016/j.iot.2025.101730","DOIUrl":"10.1016/j.iot.2025.101730","url":null,"abstract":"<div><div>The rapid proliferation of IoT devices creates dual cybersecurity challenges: traditional centralized IDS consume excessive energy and raise privacy concerns, while federated learning implementations, despite their distributed nature, lack comprehensive sustainability considerations. This study systematically reviews federated learning approaches for intrusion detection systems through a green computing lens, examining how energy efficiency and sustainability can be integrated throughout FL-IDS life-cycle while maintaining robust security. We conducted a systematic literature review of recent FL-IDS implementations and developed a taxonomy that categorizes green computing strategies according to machine learning life-cycle stages: data preparation, local training, aggregation, and inference. Our analysis identified several green computing strategies including model compression techniques, adaptive client selection, energy-aware aggregation protocols, and lightweight inference methods. However, the review reveals that sustainability metrics are inconsistently reported across studies, and carbon footprint assessments remain notably absent from current FL-IDS literature. While federated learning demonstrates potential for sustainable intrusion detection, significant gaps persist between current implementations and fully green cybersecurity systems, highlighting the need for standardized energy metrics, carbon-aware orchestration, and integration with renewable energy sources in future research.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101730"},"PeriodicalIF":7.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933349","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 smartphone-based datasets for indoor localization: A machine learning perspective","authors":"Gaetano Carmelo La Delfa ,&nbsp;Javier Prieto ,&nbsp;Salvatore Monteleone ,&nbsp;Hamaad Rafique ,&nbsp;Maurizio Palesi ,&nbsp;Davide Patti","doi":"10.1016/j.iot.2025.101753","DOIUrl":"10.1016/j.iot.2025.101753","url":null,"abstract":"<div><div>Indoor localization has gained significant attention in recent years due to its applications across sectors such as healthcare, logistics, manufacturing, and retail. However, while outdoor localization has been effectively addressed with GPS, indoor localization remains challenging despite significant research progress. Many studies have explored the capabilities of modern smartphones, equipped with a variety of sensors, to develop machine-learning methods for indoor localization, ranging from classical fingerprinting to deep sequence models and transformers. Nevertheless, most rely on small, proprietary datasets that are not publicly available. Large, high-quality public datasets are essential for researchers to efficiently test, refine, and validate algorithms, enable comparisons between different approaches and develop robust and accurate localization solutions. To reduce data collection time and costs and help researchers find the most appropriate datasets for their needs, this paper surveys 20 publicly available high-quality indoor localization datasets suitable for Machine Learning, released between 2014 and 2024, that cover various sensing technologies. The survey reveals a shift toward multi-sensor data collection, extending beyond Wi-Fi and Bluetooth signals to include inertial sensors such as accelerometers and gyroscopes, as well as magnetic fields. It also highlights that while over 75% of datasets cover multi-floor structures or multiple buildings, there is a scarcity of datasets covering diverse types of indoor environments, with most focused on office or academic settings. Moreover, the temporal dimension, crucial in dynamic indoor scenarios, remains largely underrepresented, limiting the development of ML models for tracking dynamic trajectories or adapting to evolving signal patterns.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101753"},"PeriodicalIF":7.6,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010496","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":"Exploring healthcare in the 6G and AI era: Opportunities and challenges","authors":"Houssein Taleb ,&nbsp;Guillaume Andrieux ,&nbsp;Daniele Khalife ,&nbsp;Alain Ajami ,&nbsp;Abbass Nasser","doi":"10.1016/j.iot.2025.101744","DOIUrl":"10.1016/j.iot.2025.101744","url":null,"abstract":"<div><div>The integration of AI with emerging 6G wireless communications promises to revolutionize healthcare delivery by providing ultra-fast, reliable, and intelligent medical services. Unlike previous generations of mobile phones, 6G is expected to offer sub-millisecond latency, data rates up to terabits per second, and connectivity for more than 10 million devices per square kilometer. Together, these technologies will enable unprecedented healthcare applications, such as real-time remote robotic surgery, holographic telemedicine, and continuous monitoring using bio-nanosensors within the bio-nano-internet of things.</div><div>This survey systematically analyzes the integration of AI and 6G technologies, focusing on how their convergence will enable enhanced edge computing, federated and generative AI models, low-latency analytics for personalized treatment, predictive diagnostics, and efficient resource utilization. We present a comprehensive comparison of 5G and 6G architectures, highlighting the limitations of current systems and demonstrating how 6G advancements can address critical healthcare needs, including data throughput, mobility, and security.</div><div>Furthermore, this work identifies detailed opportunities, such as AI-powered virtual nurse assistants, AI-enhanced drug discovery accelerated by hyper-responsive 6G infrastructures, and digital twin-enabled patient simulation. Alongside these opportunities, we critically examine the technical challenges related to spectrum management in the terahertz band, the design of energy-efficient IoT devices, robust data privacy frameworks that integrate federated learning and blockchain technology, ethical considerations surrounding AI explainability, and equitable access to healthcare.</div><div>By filling gaps in the existing literature, this paper presents a comprehensive framework that combines AI and 6G, specifically designed for healthcare systems. Our findings underscore the transformative potential of this combination for achieving proactive and accessible healthcare, while outlining a roadmap for overcoming prevailing technical, ethical, and infrastructural barriers.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101744"},"PeriodicalIF":7.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933364","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":"Hybrid CNN-LSTM model for predicting nitrogen, phosphorus, and potassium (NPK) fertilization requirements: Integrating satellite spectral indices with field microclimate data","authors":"Abdellatif Moussaid,&nbsp;Yousra Gamoussi,&nbsp;Hamza Briak","doi":"10.1016/j.iot.2025.101746","DOIUrl":"10.1016/j.iot.2025.101746","url":null,"abstract":"<div><div>This study presents a deep learning approach to predict nitrogen (N), phosphorus (P), and potassium (K) fertilization requirements using satellite and climate data. A hybrid CNN-LSTM model was developed to combine spatial features of Sentinel-2 vegetation indices (NDVI, NDRE, MSAVI, RECI) with temporal daily climate variables, including temperature, humidity, precipitation, wind speed, and solar radiation.</div><div>The model was trained on 3,208 samples integrating spectral, climatic, and field information such as parcel size and observation dates, and tested on a fully separated five-month period. The evaluation on the normalized scale demonstrated strong performance, with test results as follows: for nitrogen, MSE <span><math><mo>=</mo></math></span> 0.0208, MAE <span><math><mo>=</mo></math></span> 0.1132, and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>9542</mn></mrow></math></span>; for phosphorus, MSE <span><math><mo>=</mo></math></span> 0.0281, MAE <span><math><mo>=</mo></math></span> 0.1313, and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>9480</mn></mrow></math></span>; and for potassium, MSE <span><math><mo>=</mo></math></span> 0.0225, MAE <span><math><mo>=</mo></math></span> 0.1154, and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>9474</mn></mrow></math></span>. The model’s stability was further confirmed by consistent predictions across four individual months. This approach effectively integrates multimodal data for robust nutrient forecasting and can assist farmers in optimizing fertilization strategies. The outcomes support improved crop management, reduced environmental impact, and increased yields, especially in regions with limited ground data.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101746"},"PeriodicalIF":7.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920293","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":"The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities","authors":"Saeid Jamshidi ,&nbsp;Negar Shahabi ,&nbsp;Amin Nikanjam ,&nbsp;Kawser Wazed Nafi ,&nbsp;Foutse Khomh ,&nbsp;Carol Fung","doi":"10.1016/j.iot.2025.101735","DOIUrl":"10.1016/j.iot.2025.101735","url":null,"abstract":"<div><div>The Internet of Things (IoT) has revolutionized digital ecosystems by interconnecting billions of devices across various industries, enabling enhanced automation, real-time monitoring, and data-driven decision-making. However, this expansion has introduced significant security and privacy challenges due to the heterogeneous nature of IoT devices, resource constraints, and the decentralized nature of their architectures. Large Language Models (LLMs) have recently shown promise in improving cybersecurity by enabling automated threat intelligence, anomaly detection, malware classification, and privacy-aware security enforcement. Therefore, this systematic review investigates research published between 2015 and 2025 to examine the intersection of LLMs, IoT security, and privacy. We evaluate state-of-the-art LLM-based security frameworks, highlighting their effectiveness, limitations, and impact on IoT cybersecurity. In addition, this review identifies key research gaps and challenges, providing insight into the scalability, efficiency, and adaptability of LLM-driven security solutions. This work aims to contribute to the advancement of AI-driven IoT security frameworks, supporting the development of resilient and privacy-preserving cybersecurity architectures.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101735"},"PeriodicalIF":7.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920294","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":"Characterizing time-critical internet of things","authors":"Sebastian Leclerc ,&nbsp;Alessio Bucaioni ,&nbsp;Mohammad Ashjaei","doi":"10.1016/j.iot.2025.101721","DOIUrl":"10.1016/j.iot.2025.101721","url":null,"abstract":"<div><div>The Internet of Things (IoT) is increasingly being adopted in diverse domains, many of which require strict timing constraints and predictable behavior. Despite the growing importance of timing characteristics in IoT applications, current approaches to address timing requirements are often fragmented, context-specific, and lack a unified understanding. Consequently, addressing timing aspects in IoT remains largely ad hoc and dependent on individual applications, making it challenging to generalize findings or systematically apply established solutions. The goal of this study is to provide a comprehensive understanding of how timing is defined, characterized, and measured within the IoT community. We conducted this study through a systematic and structured mix methods research approach. First, we performed a systematic review of the literature, extracting and analyzing information from 38 primary studies, selected from a rigorous process involving 1176 studies. Second, to complement the literature findings, we conducted an expert survey involving 28 respondents from academia and industry, representing a variety of roles with specialized expertise in IoT systems and timing-related issues. We identified two primary characterizations of timing within the IoT: time-criticality and predictability. Additionally, we collected and categorized 113 distinct timing metrics from literature into commonly found layers of an IoT system. The majority of the surveyed practitioners and researchers (75%) agree with our categorization and consider this research useful and relevant (71.5%). We believe that our study provides practitioners and researchers with insights into timing characteristics and metrics in IoT applications, towards the ultimate goal of standardization.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101721"},"PeriodicalIF":7.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906799","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":"Resource-efficient fog computing vision system for occupancy monitoring: A real-world deployment in university libraries","authors":"Alejandro S. Martínez-Sala,&nbsp;Lucio Hernando-Cánovas,&nbsp;Juan C. Sánchez-Aarnoutse,&nbsp;Juan J. Alcaraz","doi":"10.1016/j.iot.2025.101748","DOIUrl":"10.1016/j.iot.2025.101748","url":null,"abstract":"<div><div>This paper presents a fog computing system for real-time occupancy monitoring across three university libraries, using ceiling-mounted, top-view cameras positioned above each entrance. Video streams from low-cost cameras are securely transmitted to a fog server deployed within the university’s intranet. Top-view person tracking ensures privacy compliance by inherently eliminating facial recognition, but introduces challenges such as non-standard human appearance, occlusions, and lighting variations. For person detection, we employ a YOLOv5 model initially trained on top-view human annotations, further refined through transfer learning using a curated dataset from the three libraries. The system features a two-stage processing pipeline. First, a lightweight background subtraction algorithm filters frames with potential motion, which are queued via RabbitMQ for sequential processing. Second, a People Flow Counting module applies the optimized YOLOv5 model to detect and count individuals in each frame, followed by a custom tracking algorithm and virtual line-crossing logic to ensure accurate flow tracking. Each library is handled independently through a batch processing approach, updating occupancy estimates with bounded delay using a single CPU-only fog server. This architecture maintains low latency while avoiding server overload and minimizing energy use. The system has been in continuous production for over twelve months, demonstrating reliable performance across all three libraries on commodity hardware. Quantitative evaluation confirms 94 % accuracy in people flow detection, validating the system’s robustness, scalability, and practical utility for long-term, privacy-preserving deployment in smart campus environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101748"},"PeriodicalIF":7.6,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989076","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}