Internet of Things最新文献_第4页

Comparison of distributed Machine Learning frameworks in a fog environment: Conceptual and Performance analysis 雾环境下分布式机器学习框架的比较：概念和性能分析

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-27 DOI: 10.1016/j.iot.2025.101774

Anusri Sanyadanam, Satish Narayana Srirama

{"title":"Comparison of distributed Machine Learning frameworks in a fog environment: Conceptual and Performance analysis","authors":"Anusri Sanyadanam, Satish Narayana Srirama","doi":"10.1016/j.iot.2025.101774","DOIUrl":"10.1016/j.iot.2025.101774","url":null,"abstract":"<div><div>The growing demand for real-time, latency-sensitive, and privacy-preserving analytics in IoT has brought fog computing as an alternative to cloud-based processing. However, training machine learning and deep learning (ML/DL) models in fog environments remains challenging due to limited computational resources. Despite the availability of numerous distributed ML frameworks, there is a lack of a comprehensive evaluation tailored to fog devices. This study conducts a comparative analysis of distributed ML frameworks for neural network training on resource-constrained fog nodes, using Raspberry Pi (RPi) devices. We started with Actor programming model-based frameworks and the study extended to general purpose distributed frameworks suitable for fog computing devices. We evaluate four actor-model-based frameworks (Akkordeon, DistBelief with Akka, Aktorain, and CANTO) along with general-purpose distributed frameworks (KubeRay, TensorFlow MultiWorkerMirroredStrategy (MWMS), Dask Distributed and Spark with Elephas). The frameworks are compared across key metrics including training time, accuracy, and resource utilization on diverse datasets. Our results highlight performance trade-offs: KubeRay offers a balance between efficiency and performance, Dask and MWMS achieve higher accuracy with increased latency, while Spark with Elephas excels in speed but struggles with accuracy. Although CANTO is optimized for fog-based training, it faces challenges with complex datasets. Overall, KubeRay emerges as the most practical choice for fog-based ML training because of its additional support for scalability and fault tolerance. This work bridges a critical research gap by providing experimental insights into the feasibility and performance of distributed ML frameworks in fog computing environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101774"},"PeriodicalIF":7.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge-enabled GNSS-IR for efficient water level monitoring in harsh environments 边缘GNSS-IR用于恶劣环境下的高效水位监测

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-26 DOI: 10.1016/j.iot.2025.101766

Erika Rosas , Benjamín Arratia , Ángel Martín Furones , Javier Prades , Pietro Manzoni , José M. Cecilia

{"title":"Edge-enabled GNSS-IR for efficient water level monitoring in harsh environments","authors":"Erika Rosas , Benjamín Arratia , Ángel Martín Furones , Javier Prades , Pietro Manzoni , José M. Cecilia","doi":"10.1016/j.iot.2025.101766","DOIUrl":"10.1016/j.iot.2025.101766","url":null,"abstract":"<div><div>Accurate water level monitoring in remote and harsh environments is critical for managing water resources, assessing climate impacts, and anticipating flood risks. Traditional in situ sensors often fail in these contexts due to corrosion, biofouling, or limited access for maintenance. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) offers a passive, low-cost alternative by extracting water level information from multipath reflections of GNSS signals. However, using multi-constellation GNSS-IR for near real-time monitoring is challenging due to its high computational and communication demands, especially in low-power, low-connectivity areas.</div><div>This paper presents a novel edge computing-based GNSS-IR system designed for deployment in harsh environments. The system, validated in the highly saline La Mata–Torrevieja Natural Park (Spain), integrates a low-cost GNSS receiver and a modular gateway that executes the GNSS-IR processing locally. To efficiently transmit results over long distances, it uses the AlLoRa protocol, an advanced LPWAN solution optimized for high-throughput, low-power communication. By eliminating the need for raw data transmission and enabling local analytics, the system reduces bandwidth, enhances responsiveness, and supports continuous operation in constrained conditions. Experimental validation demonstrates the system’s effectiveness in achieving near real-time water level estimation with minimal infrastructure.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101766"},"PeriodicalIF":7.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fine-grained framework for online IoT device firmware identification via version evolution analysis 一个细粒度框架，用于通过版本演变分析在线物联网设备固件识别

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-26 DOI: 10.1016/j.iot.2025.101767

Zhen Lei , Yijia Li , Zhen Li , Xin Huang , Dan Yu , Nian Xue , Yongle Chen

{"title":"A fine-grained framework for online IoT device firmware identification via version evolution analysis","authors":"Zhen Lei , Yijia Li , Zhen Li , Xin Huang , Dan Yu , Nian Xue , Yongle Chen","doi":"10.1016/j.iot.2025.101767","DOIUrl":"10.1016/j.iot.2025.101767","url":null,"abstract":"<div><div>The rapid expansion of IoT networks has outpaced the capabilities of firmware management protocols, leaving numerous Internet-connected devices operating on outdated firmware that contains exploitable vulnerabilities. As vulnerabilities are closely tied to specific firmware versions, fine-grained version identification is critical for effective device management and security risk assessment. However, high firmware heterogeneity and subjective biases in feature selection pose significant challenges to online firmware version identification (OFVI) of IoT devices. To address these challenges, we first construct a dataset comprising 444,195 embedded web pages extracted from 1,000 successfully simulated firmware images. Through analyzing update patterns of embedded web interfaces during firmware version evolution, we propose <em>FirmID</em>, a novel OFVI framework for IoT devices that utilizes directory and content changes in embedded web interfaces. To handle the heterogeneity of firmware across different vendors, we introduce the Hierarchical Multimodal Attention Network (HMANet), a machine learning model specifically designed to capture differences across structural, textual, and functional modalities. To overcome the challenge of distinguishing hard samples caused by the frequent reuse of web pages in firmware iteration versions, we design a Hard Negative Mining Contrastive Loss that enhances intra-class compactness and inter-class separability. Moreover, to improve identification efficiency under uncertain network conditions, FirmID incorporates a complementary heuristic search algorithm, Firmware Identification with Monte Carlo Tree Search (FIMCTS). Experimental results demonstrate that FirmID surpasses state-of-the-art methods by 30.2% in accuracy and reduces file requests by 23.3% in recognition efficiency.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101767"},"PeriodicalIF":7.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synergizing IoT, AI, and blockchain for smart agriculture: Challenges, opportunities, and future directions 协同物联网、人工智能和区块链实现智慧农业：挑战、机遇和未来方向

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-25 DOI: 10.1016/j.iot.2025.101778

Avni Rustemi , Fisnik Dalipi

{"title":"Synergizing IoT, AI, and blockchain for smart agriculture: Challenges, opportunities, and future directions","authors":"Avni Rustemi , Fisnik Dalipi","doi":"10.1016/j.iot.2025.101778","DOIUrl":"10.1016/j.iot.2025.101778","url":null,"abstract":"<div><div>The integration of the Internet of Things (IoT), blockchain technology (BT), and Artificial Intelligence (AI) is transforming agriculture into a smart, data-driven system designed to enhance productivity, transparency, and automation. Population growth and limited resources make these technologies increasingly critical, especially in regions with scarce water, nutrients, or fertile soil. IoT provides real-time monitoring and physical data collection through sensors and edge devices, BT ensures data security, traceability, and transparency across supply chains, while AI enables predictive analytics and automated decision-making, reducing direct farmer intervention. This systematic literature review is focusing on the IoT implementations in the agriculture ecosystem, with the sole aim of increasing agricultural productivity and efficiency. Furthermore, it analyzes the interplay of IoT, AI, and BT in agriculture, with the emphasis on the measurable impacts, security of communication protocols, socio-technical implications, and automation and decision-making, among others. Despite their promise, integration faces notable barriers such as data privacy, interoperability, real-time processing, and implementation costs. Using the PRISMA framework, 35 studies were selected from an initial pool of 977 articles published between 2019 and 2025. A rigorous quality assessment extracted insights on integration strategies, technical limitations, and practical applications. The review highlights opportunities and challenges in adopting IoT, AI, and BT for sustainable smart agriculture. It concludes with recommendations for researchers, policymakers, technology developers, and practitioners to address current gaps, strengthen security and interoperability, and guide future advancements toward resilient and efficient agricultural systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101778"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging ontologies and Asset Administration Shells for decision-support: A case study on production planning within the injection molding domain 利用本体和资产管理外壳进行决策支持：注塑成型领域内生产计划的案例研究

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-25 DOI: 10.1016/j.iot.2025.101739

Patrick Sapel, Anna Garoufali, Christian Hopmann

{"title":"Leveraging ontologies and Asset Administration Shells for decision-support: A case study on production planning within the injection molding domain","authors":"Patrick Sapel, Anna Garoufali, Christian Hopmann","doi":"10.1016/j.iot.2025.101739","DOIUrl":"10.1016/j.iot.2025.101739","url":null,"abstract":"<div><div>A fundamental aspect of Industry 4.0 is interoperable asset-to-asset communication, essential for creating cross-company “lab of labs”. Such collaboration enables seamless data exchange across companies, streamlining manual processes like evaluating the capability of assets for specific manufacturing processes. While foundational technologies for asset interoperability exist, their integration and application in industrial contexts remain limited. Our research explores the integration of ontologies, which structure domain knowledge, and Asset Administration Shells (AAS), which represent assets in a standardized manner, to facilitate industrial interoperability. We have developed an architecture using an ontology-based graph database populated with AAS data, allowing automatic linking of AAS instances to corresponding class nodes. To demonstrate practical value, we have implemented this architecture using standardized software and tools, applying it to assess technical capabilities for a customer request in injection molding. Results confirm the potential for asset-to-asset communication in industry via graph databases, with benefits in flexible and scalable data management. However, limitations include unaddressed data safety and security concerns, as well as the need for updated database entries when AAS instances change. Additionally, challenges in scaling to integrate other domain ontologies should be tackled in future research. This work lays a foundation for advancing interoperable, cross-company data-sharing ecosystems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101739"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint optimisation of time and energy consumption for data aggregation in fog-enabled IoT networks 在雾支持的物联网网络中，联合优化数据聚合的时间和能量消耗

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-24 DOI: 10.1016/j.iot.2025.101775

Sira Yongchareon

引用次数: 0

Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues 智慧城市中的大数据和物联网应用：最新进展、挑战和关键问题

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-24 DOI: 10.1016/j.iot.2025.101770

Elias Dritsas, Maria Trigka

{"title":"Big data and Internet of Things applications in smart cities: Recent advances, challenges, and critical issues","authors":"Elias Dritsas, Maria Trigka","doi":"10.1016/j.iot.2025.101770","DOIUrl":"10.1016/j.iot.2025.101770","url":null,"abstract":"<div><div>The rapid urbanization of modern cities has been propelled by the convergence of the Internet of Things (IoT) and Big Data, enabling real-time monitoring, predictive analytics, and intelligent automation across transportation, energy, healthcare, and public safety. This survey systematically reviews advancements in IoT-enabled infrastructure, Big Data analytics, edge and cloud integration, digital twin technology, and blockchain for secure and scalable data management. The literature selection focused on peer-reviewed works published from 2020 onward, prioritizing journal and conference papers that present concrete smart city deployment or technical frameworks. Each technological domain is examined with respect to its operational benefits, limitations, and role in enabling resilient urban ecosystems. Emerging trends such as 6G-enabled IoT, federated learning (FL), quantum computing, and swarm intelligence are explicitly contextualized in terms of their maturity, current research focus, and prospective impacts. To address persisting challenges, including scalability bottlenecks, interoperability gaps, cybersecurity threats, and data governance issues, the survey identifies actionable directions such as geospatial data standardization, energy-aware orchestration, and lightweight consensus mechanisms. These targeted measures provide a foundation for guiding future research and practice toward secure, efficient, and human-centric smart city development.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101770"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GUIDE2FR: A smart monitoring platform with a digital twin of a firefighter training tower for emergency scenarios GUIDE2FR：一个智能监控平台，具有消防员训练塔的数字孪生体，用于紧急情况

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-23 DOI: 10.1016/j.iot.2025.101768

Marcos Delgado Álvaro , Robert Novak , Pedro Rafael Fernández Barbosa , Iván Chicano Capelo , Micael Gallego , M. Cristina Rodriguez-Sanchez

{"title":"GUIDE2FR: A smart monitoring platform with a digital twin of a firefighter training tower for emergency scenarios","authors":"Marcos Delgado Álvaro , Robert Novak , Pedro Rafael Fernández Barbosa , Iván Chicano Capelo , Micael Gallego , M. Cristina Rodriguez-Sanchez","doi":"10.1016/j.iot.2025.101768","DOIUrl":"10.1016/j.iot.2025.101768","url":null,"abstract":"<div><div>This paper describes the implementation of a digital twin for buildings to enhance the emergency response capabilities of first responder teams, including firefighters, police, and emergency medical services. The proposed platform improves the planning of preventive evacuation strategies and supports real-time operational decisions during emergencies. It integrates wireless monitoring beacons, specifically designed for hostile environments, a cloud-based data management system, and a predictive model to monitor environmental air quality parameters, which are critical during emergency scenarios. The platform uses a digital twin to simulate the building’s behavior, incorporating multimedia content and time series graphs to enhance situational awareness and decision-making. Real-time building data are seamlessly integrated with predictive models generated from smart building sensors, offering a comprehensive visualization on a web-based monitoring interface. This approach provides critical insights to decision-makers, improving the safety and efficiency of both rescue operations and preventive measures.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101768"},"PeriodicalIF":7.6,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust priority aware multi-criterion offloading in digital twin UAVs networks 数字双机网络中鲁棒优先级感知多准则卸载

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-19 DOI: 10.1016/j.iot.2025.101763

Muhammad Yahya , Muhammad Naeem , Zeeshan Kaleem , Waleed Ejaz

{"title":"Robust priority aware multi-criterion offloading in digital twin UAVs networks","authors":"Muhammad Yahya , Muhammad Naeem , Zeeshan Kaleem , Waleed Ejaz","doi":"10.1016/j.iot.2025.101763","DOIUrl":"10.1016/j.iot.2025.101763","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) play a critical role in replenishing the energy of power-constrained Internet of Things (IoT) devices, particularly in public safety operations, thereby maintaining continuous system functionality. Integrating Mobile Edge Computing (MEC) into UAV platforms enables offloading computational tasks to aerial nodes, optimizing resource utilization. Efficient orchestration of communication, computation, caching, and energy resources is imperative to maximize the benefits of UAV-assisted MEC networks. Additionally, ensuring high situational awareness is essential for supporting priority-based latency-sensitive applications. Digital twin technology can be instrumental in minimizing latency by generating a real-time digital representation of the physical infrastructure, enabling enhanced system monitoring and optimization. Accordingly, we have formulated an optimization problem to maximize the number of IoT devices UAVs can support while adhering to predefined constraints. The formulated problem is a mixed integer non-linear programming model. Additionally, the dynamic management of tasks with varying priorities and computational demands introduces a significant resource allocation and scheduling challenge. Our proposed approach entails an efficient task offloading and priority-based scheduling strategy that prioritizes tasks, allocating computational resources to those with higher priority. The approach encompasses a multi-stage offloading strategy combining an interior-point method with a learning algorithm to address the inherent complexity and provide a viable solution. Simulation results validate the effectiveness of the proposed approach, outperforming conventional methods. Specifically, the Penalty Function Method Heuristic combined with the Interior Point Method achieves superior user connectivity compared to the Simple Relaxation Heuristic strategy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101763"},"PeriodicalIF":7.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CARES: A Hybrid caregivers recommendation system using deep learning and knowledge graphs CARES：一个使用深度学习和知识图谱的混合型护理推荐系统

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-19 DOI: 10.1016/j.iot.2025.101769

Qiaoyun Zhang , Sze-Han Wang , Chung-Chih Lin , Chih-Yung Chang , Diptendu Sinha Roy

{"title":"CARES: A Hybrid caregivers recommendation system using deep learning and knowledge graphs","authors":"Qiaoyun Zhang , Sze-Han Wang , Chung-Chih Lin , Chih-Yung Chang , Diptendu Sinha Roy","doi":"10.1016/j.iot.2025.101769","DOIUrl":"10.1016/j.iot.2025.101769","url":null,"abstract":"<div><div>Recommendation systems have prospered by leveraging user-item interactions and their features for personalized recommendations. Recent advancements in deep learning further enhance these recommendation systems with powerful backbones for learning from user-item data. However, solely depending on these interactions often leads to the cold-start problem, where items lacking historical data cannot be effectively recommended. Additionally, the issue of high similarity between user and item features frequently goes unresolved. This paper introduces a Hybrid Caregiver Recommendation mechanism, called CARES, designed to recommend suitable caregivers for postpartum women using deep learning and knowledge graphs. Initially, the proposed CARES utilizes Extreme Gradient Boosting (XGBoost) to identify important features, addressing the issue of feature similarity. Then it employs <em>K</em>-Means clustering to group postpartum women and caregivers based on similar features. Subsequently, it utilizes a Deep & Cross Network (DCN) to automatically learn feature interactions and constructs knowledge graphs to tackle the cold start problem. The proposed CARES also integrates exploration and exploitation strategies to balance the accuracy and diversity of recommendations. The proposed CARES compares with existing mechanisms on real datasets, and the simulation results demonstrate its effectiveness in terms of precision, recall, and F1-Score.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101769"},"PeriodicalIF":7.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0