Internet of Things最新文献

FGLIoT: IoT device identification via federated graph learning and spatio-temporal feature fusion FGLIoT：基于联邦图学习和时空特征融合的物联网设备识别

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-10-01 DOI: 10.1016/j.iot.2025.101785

Xuhui Wang, Guanglu Sun, Xin Liu

{"title":"FGLIoT: IoT device identification via federated graph learning and spatio-temporal feature fusion","authors":"Xuhui Wang, Guanglu Sun, Xin Liu","doi":"10.1016/j.iot.2025.101785","DOIUrl":"10.1016/j.iot.2025.101785","url":null,"abstract":"<div><div>The device silo problem poses a significant challenge to the management and security of the Internet of Things (IoT). The key to solving this issue is to accurately identify IoT devices connected to the network while protecting data privacy. However, existing solutions overlook inter-packet semantic correlations, a fact which renders them unable to fully explore the potential behavior patterns in device communication traffic. Therefore, we propose FGLIoT, a federated graph learning-based method for IoT device identification. FGLIoT first represents the communication traffic data generated by IoT devices as packet sequence graphs, preserving the semantic information of packets. It then employs a graph learning module to capture inter-packet semantic correlations and learn representations of device communication behaviors. Subsequently, the representations are processed by spatial and temporal feature extractors to capture their spatial correlations and temporal dependences, respectively. Finally, residual connections are used to fuse the behavior representations with their spatial and temporal features, generating behavioral fingerprints for IoT device identification. Experimental results on three public IoT device datasets demonstrate the effectiveness of FGLIoT in solving the device silo problem.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101785"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220826","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 systematic literature review on AI in IoT systems: Tasks, applications, and deployment 关于物联网系统中人工智能的系统文献综述：任务、应用和部署

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-30 DOI: 10.1016/j.iot.2025.101779

Umair Khadam , Paul Davidsson , Romina Spalazzese

{"title":"A systematic literature review on AI in IoT systems: Tasks, applications, and deployment","authors":"Umair Khadam , Paul Davidsson , Romina Spalazzese","doi":"10.1016/j.iot.2025.101779","DOIUrl":"10.1016/j.iot.2025.101779","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) into Internet of Things (IoT) systems has garnered considerable attention for its ability to enhance efficiency, functionality, and decision making. To drive further research and practical applications, it is essential to gain a deeper understanding of the different roles of AI in IoT systems. In this systematic literature review, we analyze 103 articles describing Artificial Intelligence of Things (AIoT) systems found in three databases, i.e. Scopus, IEEE Xplore, and Web of Science. For each article, we examined the tasks for which AI was used, the input and output data, the application domain, the maturity level of the system, the AI methods used, and where the AI components were deployed. As a result, we identified six general tasks of AI in IoT systems, and thirteen subtasks, the most frequent being prediction, object and event recognition, and operational decision-making. Moreover, we conclude that most AI components in IoT systems process numeric data as input and that healthcare is the most common application domain followed by farming and transportation. Our analysis further revealed that most AIoT systems are in early development stages not validated in real environments. We also identified that Convolutional Neural Networks is the most frequently employed AI method, with supervised learning being the dominant approach. Additionally, we found that both AI deployment, either in the cloud or at the edge, are frequent, but that hybrid deployment is not that common. Finally, we identified key gaps in current AIoT research and based on this, we suggest directions for future research.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101779"},"PeriodicalIF":7.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220821","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

Non-invasive occupancy estimation and space utilization in smart buildings: Leveraging machine learning with PIR sensors and booking data 智能建筑中的非侵入式占用估计和空间利用：利用PIR传感器和预订数据的机器学习

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-29 DOI: 10.1016/j.iot.2025.101777

Azad Shokrollahi , Fredrik Karlsson , Reza Malekian , Jan A. Persson , Arezoo Sarkheyli-Hägele

{"title":"Non-invasive occupancy estimation and space utilization in smart buildings: Leveraging machine learning with PIR sensors and booking data","authors":"Azad Shokrollahi , Fredrik Karlsson , Reza Malekian , Jan A. Persson , Arezoo Sarkheyli-Hägele","doi":"10.1016/j.iot.2025.101777","DOIUrl":"10.1016/j.iot.2025.101777","url":null,"abstract":"<div><div>Occupancy estimation in smart buildings is essential for optimizing resource usage and enhancing operational efficiency. Existing estimation methods predominantly rely on cameras or advanced sensor fusion techniques, which, while accurate, are often expensive, invasive, and raise privacy concerns. Additionally, these approaches frequently require extra hardware, increasing installation complexity and operational costs. A significant gap in the literature lies in the limited use of existing smart building infrastructure, such as detection systems and booking data, for people counting. This study addresses these limitations by exclusively utilizing two binary PIR sensors (in-door and in-room) and booking data. Since PIR sensors and booking systems are already integrated into most smart building infrastructures, leveraging these existing resources helps reduce costs and simplifies implementation. The primary goal is to estimate the number of people between each in-door sensor trigger using machine learning models by incorporating people counting levels and time thresholds. Among the evaluated machine learning algorithms, the Extra Trees Classifier delivered strong performance, achieving 68.5% accuracy when the estimated occupancy differed from the actual count by at most one person, and 81.56% with a tolerance of two. These results are based on periods when the room was occupied. When both occupied and unoccupied periods were included, the accuracy was 96.10% for ±1 tolerance. Moreover, incorporating booking data enhanced people counting accuracy by 4%. The study also explores the method’s ability to identify underutilization and overutilization by comparing estimated occupancy with booking records and seating capacity, thereby supporting enhanced space management in smart buildings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101777"},"PeriodicalIF":7.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220718","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

Incremental firmware update over-the-air for low-power IoT devices over LoRaWAN 通过LoRaWAN进行低功耗物联网设备的无线增量固件更新

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-27 DOI: 10.1016/j.iot.2025.101772

Andrea De Simone, Giovanna Turvani, Fabrizio Riente

{"title":"Incremental firmware update over-the-air for low-power IoT devices over LoRaWAN","authors":"Andrea De Simone, Giovanna Turvani, Fabrizio Riente","doi":"10.1016/j.iot.2025.101772","DOIUrl":"10.1016/j.iot.2025.101772","url":null,"abstract":"<div><div>Remote firmware updates in Internet of Things (IoT) devices remain a major challenge due to the constraints of many IoT communication protocols. In particular, transmitting full firmware images over low-bandwidth links such as Long Range Wide Area Network (LoRaWAN) is often impractical. Existing techniques, such as firmware partitioning, can alleviate the problem but are often insufficient, especially for battery-powered devices where time and energy are critical constraints. Consequently, physical maintenance is still frequently required, which is costly and impractical in large-scale deployments. In this work, we introduce <em>bpatch</em>, a lightweight method for generating highly compact delta patches that enable on-device firmware reconstruction. The algorithm is explicitly designed for low-power devices, minimizing memory requirements and computational overhead during the update process. We evaluate <em>bpatch</em> on 173 firmware images across three architectures. Results show that it reduces update payloads by up to 39,000×for near-identical updates and by 9–18×for typical minor revisions, eliminating the need to transmit full firmware images. Experimental results further demonstrate significant time and energy savings, with performance comparable to more complex alternatives. <em>bpatch</em> is released as open-source and, although demonstrated on LoRaWAN, the approach is flexible and can be adapted to other IoT communication technologies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101772"},"PeriodicalIF":7.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220827","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 Secured Swarm Intelligence-based Path Selection framework for Malicious Low-power and Lossy Networks under RPL protocol RPL协议下基于安全群智能的恶意低功耗有损网络路径选择框架

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2025-09-27 DOI: 10.1016/j.iot.2025.101776

Hanin Almutairi , Salem AlJanah , Ning Zhang

{"title":"A Secured Swarm Intelligence-based Path Selection framework for Malicious Low-power and Lossy Networks under RPL protocol","authors":"Hanin Almutairi , Salem AlJanah , Ning Zhang","doi":"10.1016/j.iot.2025.101776","DOIUrl":"10.1016/j.iot.2025.101776","url":null,"abstract":"<div><div>Low-power and Lossy Networks (LLNs) face persistent challenges, including dynamic topologies, unreliable links, limited energy, and constrained computational resources. These issues are exacerbated under malicious conditions such as Packet Dropping Attacks (PDAs), where conventional routing and security mechanisms fall short due to their high computational overhead. To address these challenges, this paper proposes the Secured Swarm Intelligence-based Path Selection (S-SIPaS) framework, designed to enhance reliability and security in Malicious LLNs (MLLNs). S-SIPaS builds on our previous SIPaS framework by integrating a lightweight trust model and a novel Secured Ant Colony Objective Function (S-ACOF) into the RPL protocol. S-ACOF applies Ant Colony Optimisation (ACO) principles to compute globally optimal, trustworthy paths while reducing energy consumption and control overhead. A key feature of S-SIPaS is its three-phase trust model: monitoring, trust measurement, and trust determination, which detects and isolates malicious nodes based on packet-forwarding behaviour, without relying on cryptographic techniques.</div><div>The framework combines multiple routing metrics, including physical distance, energy level, link quality, and trust score, enabling adaptive and efficient path selection in dynamic LLNs. Simulation results show that S-SIPaS improves Packet Delivery Ratio (PDR) by up to 51% over existing methods, especially in high-density and high-attack scenarios.</div><div>Despite strong performance, the framework has limitations: (i) it requires C1-class nodes (e.g., Z1); (ii) evaluation is limited to simulations; and (iii) it currently addresses only PDA threats and static topologies. Overall, S-SIPaS offers an effective, scalable, and secure routing solution for enhancing MLLNs and IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"34 ","pages":"Article 101776"},"PeriodicalIF":7.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220824","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

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