Internet of Things最新文献

Bibliometric analysis of secure IoT for quantum computing 量子计算安全物联网的文献计量分析

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.iot.2026.101872

Hamza Ibrahim , Love Allen Chijioke Ahakonye , Jae-Min Lee , Dong-Seong Kim

{"title":"Bibliometric analysis of secure IoT for quantum computing","authors":"Hamza Ibrahim , Love Allen Chijioke Ahakonye , Jae-Min Lee , Dong-Seong Kim","doi":"10.1016/j.iot.2026.101872","DOIUrl":"10.1016/j.iot.2026.101872","url":null,"abstract":"<div><div>The convergence of Quantum Machine Learning (QML) and Blockchain is emerging as a transformative paradigm to address escalating security and scalability challenges in 6G-enabled Industrial Internet of Things (IIoT) networks. This study presents the first comprehensive bibliometric and meta-analysis of this nascent interdisciplinary field. We analyzed 159 peer-reviewed publications (indexed from January 2022 through December 22, 2024) from Scopus, employing a systematic Kitchenham-based methodology for literature selection and VOSviewer for science mapping. Our analysis reveals a 75% annual growth rate since 2022, with India (37.7%), the USA (12.6%), and South Korea (12.6%) as the leading contributors. Keyword co-occurrence analysis identified four dominant thematic clusters: “6G Network Security,” “Quantum Computing and AI,” “Blockchain and Decentralization,” and “IIoT Applications.” The study’s novelty lies in synthesizing bibliometric insights with a proposed five-layer QML-Blockchain integration framework and a comparative analysis against existing reviews. Quantitative performance metrics indicate that QML can improve anomaly detection accuracy by 5–9% over classical models, while advanced consensus mechanisms like PoA<sup>2</sup> can reduce transaction latency by 35%. However, significant challenges persist, including quantum hardware limitations (e.g., qubit coherence  < 100 <em>μ</em>s), scalability challenges in achieving consensus across massive IIoT device densities, and a critical lack of empirical testbeds. This research provides a foundational roadmap, emphasizing the urgent need for standardized benchmarks, hybrid orchestration models, and quantum-resistant cryptography to realize secure, intelligent, and autonomous IIoT ecosystems in the 6G era.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101872"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077991","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

FedWKD: Federated learning weighted aggregation with knowledge distillation for IoT forecasting FedWKD：基于知识蒸馏的物联网预测的联邦学习加权聚合

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.iot.2025.101849

Bouchra Fakher , Mohamed El Amine Brahmia , Ismail Bennis , Abdelhafid Abouaissa

{"title":"FedWKD: Federated learning weighted aggregation with knowledge distillation for IoT forecasting","authors":"Bouchra Fakher , Mohamed El Amine Brahmia , Ismail Bennis , Abdelhafid Abouaissa","doi":"10.1016/j.iot.2025.101849","DOIUrl":"10.1016/j.iot.2025.101849","url":null,"abstract":"<div><div>Federated Learning (FL) has emerged as a promising solution for decentralized Machine Learning (ML) that does not have direct access to datasets in a centralized manner. However, the traditional FL methods are prone to overfitting and model drift at the client level and server divergence during classic aggregation in case of heterogeneous, non-independent and identically distributed (non-IID) time-series sensor data. In this paper, we propose a novel approach that integrates bidirectional Knowledge Distillation (KD) by using distilled soft predictions of each client model, called logits, as well as server model distilled logits. Specifically, clients use KD regularization techniques using the received server logits during model training, while the server uses received client logits to build a score for weighted global aggregation each round. Thus, we avoid local training overhead for clients, while also improving global aggregation using weighting on the server-side for each training round for non-IID data. Experimental results highlight its ability to improve forecasting metrics compared to other methods such as CADIS and FEDGKD, using loss, error, and execution time metrics, hence bettering generalization and minimizing client drift and bias.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101849"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791909","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

An edge-intelligent three-tier framework for real-time forest fire detection, integrating WSNs, WMSNs, and UAVs 一种边缘智能三层框架，用于实时森林火灾探测，集成了wsn、wmsn和无人机

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.iot.2025.101861

Hamzeh Abu Ali , Enver Ever , Burak Kizilkaya , Muhammad Toaha Raza Khan , Masood Ur Rehman , Shuja Ansari , Muhammad Ali Imran , Adnan Yazici

{"title":"An edge-intelligent three-tier framework for real-time forest fire detection, integrating WSNs, WMSNs, and UAVs","authors":"Hamzeh Abu Ali , Enver Ever , Burak Kizilkaya , Muhammad Toaha Raza Khan , Masood Ur Rehman , Shuja Ansari , Muhammad Ali Imran , Adnan Yazici","doi":"10.1016/j.iot.2025.101861","DOIUrl":"10.1016/j.iot.2025.101861","url":null,"abstract":"<div><div>Forest fires are becoming prevalent, threatening ecosystems, economies, and public safety while creating an urgent demand for rapid and reliable detection systems. Conventional approaches such as watchtowers, manual patrols, and satellite imaging suffer from limited coverage, delays, and inadequate precision. To address these challenges, we propose a three-tier, edge-centric framework that integrates wireless sensor networks (WSNs), wireless multimedia sensor networks (WMSNs), unmanned aerial vehicles (UAVs), and lightweight machine learning (ML) and deep learning (DL) models for efficient detection. In the first tier, scalar sensors provide early hazard identification; in the second, smart sensors execute a lightweight ML model for intermediate verification, achieving a 94% F1-score with a minimal feature set; and in the third, UAVs equipped with sensors, cameras, and a compact convolutional neural network (CNN) deliver final confirmation. The CNN achieves state-of-the-art results with a 100% F1 score on the FireMan-UAV-RGBT dataset and 99.5% on UAV-FFDB while remaining compact (1.6 MB) and efficient (157 ms inference on Raspberry Pi 5), enabling real-time edge deployment. Simulations show reduced end-to-end delay (813.59 ms) compared to WSN-only (865.84 ms) and WMSN (1066.18 ms) baselines, improved throughput (7.05 kbps vs 3.80 kbps and 3.06 kbps), and a 100% delivery ratio. Real-world WSN testbed experiments further validate the framework, achieving a 97% delivery ratio, 144.39 ms latency (vs. 258.37 ms in simulations), and energy consumption of 0.0559 J/s (closely matching 0.0442 J/s in simulations). These results collectively demonstrate the practicality and effectiveness of the framework for real-time forest fire monitoring and rapid emergency response.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101861"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885164","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

Medvault: Privacy-enhancing medical record retrieval for ioMT-Enabled healthcare with query-pattern protection Medvault：为支持iomt的医疗保健提供具有查询模式保护的增强隐私的医疗记录检索

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-02-11 DOI: 10.1016/j.iot.2026.101890

Yuxi Li , Dong Ji , Fa Zhu , Xingchi Chen , Athanasios V. Vasilakos , Francesco Piccialli , David Camacho

{"title":"Medvault: Privacy-enhancing medical record retrieval for ioMT-Enabled healthcare with query-pattern protection","authors":"Yuxi Li , Dong Ji , Fa Zhu , Xingchi Chen , Athanasios V. Vasilakos , Francesco Piccialli , David Camacho","doi":"10.1016/j.iot.2026.101890","DOIUrl":"10.1016/j.iot.2026.101890","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT) increasingly relies on remote EHR repositories to answer contextual clinical queries that combine structured signals, free-text notes, and medical images. Outsourcing retrieval to a cloud provider, however, risks exposing not only query content but also query patterns—which index regions and records are accessed and how often—enabling sensitive inference even if payloads are encrypted. We present <span>MedVault</span>, a privacy-enhancing multimodal EHR retrieval system that protects both query content and query pattern under a split-trust two-server model. <span>MedVault</span> computes multimodal embeddings at the authorized client boundary, secret-shares embeddings and record payloads across two non-colluding servers, privately selects candidate clusters via distributed point functions (DPF), and retrieves padded top-<em>k</em> results so that transferred volumes are independent of the target. A clustering index confines secure scoring to a sublinear candidate set and enables a constant-round (1-RTT) query protocol. On de-identified MIMIC-IV datasets, <span>MedVault</span> not only achieves stable retrieval quality with R@10  ≈  0.83–0.84 and nDCG@10  ≈  0.85–0.86, but also improves over a keyword baseline by about 18%–19%. These results suggest that <span>MedVault</span> offers a deployable building block for privacy-preserving clinical retrieval in bandwidth- and latency-sensitive IoMT settings.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101890"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173165","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

Transformer-based classification of IoT network traffic with flow-to-window aggregation 基于变压器的物联网网络流量分类与流到窗口聚合

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.iot.2026.101879

Sergio Martin-Reizabal , Adrian Caballero-Quiroga , Beatriz Gil-Arroyo , Nuño Basurto , Ruben Ruiz-Gonzalez

{"title":"Transformer-based classification of IoT network traffic with flow-to-window aggregation","authors":"Sergio Martin-Reizabal , Adrian Caballero-Quiroga , Beatriz Gil-Arroyo , Nuño Basurto , Ruben Ruiz-Gonzalez","doi":"10.1016/j.iot.2026.101879","DOIUrl":"10.1016/j.iot.2026.101879","url":null,"abstract":"<div><div>The explosive growth of the IoT has led to an increasingly complex and heterogeneous network traffic, posing major challenges for intrusion detection. Most existing machine learning and deep learning approaches model network traffic at the level of individual flows, which limits their ability to capture contextual relationships among concurrent communications. This paper introduces a Transformer-based framework for IoT intrusion detection that aggregates network flows into fixed-duration windows and treats each flow as a token within the input sequence. The self-attention mechanism captures contextual relationships among concurrent flows, enabling effective modeling of temporal dependencies without recurrence. Experiments conducted on the CICIoT2023 dataset show that the proposed model achieves a weighted F1-score of 97.9% and a macro ROC–AUC of 99.6% under temporally blocked cross-validation, while maintaining high computational efficiency. These results demonstrate that flow-to-window aggregation combined with self-attention provides a robust and scalable foundation for IoT network security, suitable for deployment in edge and smart-home environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101879"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077990","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

Neuromorphic solar edge AI for sustainable wildfire detection 用于可持续野火探测的神经形态太阳边缘AI

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.iot.2025.101862

Raúl Parada

引用次数: 0

Stability analysis for fog computing via Lyapunov function 基于Lyapunov函数的雾计算稳定性分析

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.iot.2026.101871

Shih-Yu Chang , Mayank Kapadia , Peishun Yan , Wei Duan

{"title":"Stability analysis for fog computing via Lyapunov function","authors":"Shih-Yu Chang , Mayank Kapadia , Peishun Yan , Wei Duan","doi":"10.1016/j.iot.2026.101871","DOIUrl":"10.1016/j.iot.2026.101871","url":null,"abstract":"<div><div>Fog computing is an emerging paradigm for the Internet of Things (IoT), where system stability directly impacts reliability, performance, and user experience. Existing stability models often ignore application-level service completion or fail to capture dynamic interactions among sensors and fog nodes. This study addresses these gaps by establishing necessary conditions for fog nodes to process application services within finite time. First, we introduce a fluid model based on a partial differential equation (PDE) to quantify the dynamics of service counts for each sensor when fog nodes are shared. Second, we design a Lyapunov function derived from the PDE solution to analyze system stability and convergence. Third, we apply this Lyapunov function to derive conditions that guarantee timely service completion. Finally, numerical experiments validate the fluid model, investigate PDE solution behavior, and assess the convergence speed of the Lyapunov function under various system parameters. These results provide actionable insights for ensuring stability and efficiency in fog computing systems for IoT applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101871"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023231","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

Classifying user-created passwords using machine learning and natural language processing techniques 使用机器学习和自然语言处理技术对用户创建的密码进行分类

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.iot.2025.101854

Binh Le Thanh Thai, Tsubasa Takii, Hidema Tanaka

引用次数: 0

FedMamba: Robust multimodal federated intrusion detection for heterogeneous IoT systems FedMamba：针对异构物联网系统的鲁棒多模态联邦入侵检测

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.iot.2026.101877

Hafiz Bilal Ahmad , Haichang Gao , Naila Latif , Tanjeena Manzoor

{"title":"FedMamba: Robust multimodal federated intrusion detection for heterogeneous IoT systems","authors":"Hafiz Bilal Ahmad , Haichang Gao , Naila Latif , Tanjeena Manzoor","doi":"10.1016/j.iot.2026.101877","DOIUrl":"10.1016/j.iot.2026.101877","url":null,"abstract":"<div><div>The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 produces diverse data streams, such as system logs, sensor readings, and network traffic, which are vital for industrial security. However, existing security analytics are siloed by modality and rely on centralized processing, raising concerns regarding privacy, latency, and scalability. Although Federated Learning (FL) mitigates privacy risks, most frameworks remain unimodal, lack support for non-IID data distributions, and face adversarial evasion challenges. We propose FedMamba, a novel multimodal Federated Learning (MMFL) framework that creates a unified Mamba-based model to address these issues via (i) efficient cross-modal learning, (ii) a FedProx-based protocol for stable non-IID training that remains compatible with secure aggregation, and (iii) modality-specific adversarial training for robustness. Experiments on HDFS, SWaT, and CICIoMT-2024 datasets show that the standard FedMamba achieved competitive macro F1-scores of 0.9584, 0.9795, and 0.9665 relative to centralized baselines, but degraded on HDFS and SWaT under PGD attack (0.3791 and 0.5147), whereas CICIoMT-2024 remained robust under the same attack (0.9665). The adversarially trained FedMamba-AT sustains robust F1-scores (0.9480, 0.8357, 0.9645). FedMamba offers a robust and scalable solution for secure IIoT monitoring.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101877"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077994","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

HiFEL-OCKT: Hierarchical federated edge learning with objective congruence and multi-level knowledge transfer for IoT ecosystems HiFEL-OCKT：物联网生态系统中具有客观同余和多层次知识转移的分层联邦边缘学习

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-03-01 Epub Date: 2026-01-03 DOI: 10.1016/j.iot.2025.101868

Ahmed-Rafik Baahmed, Jean-François Dollinger, Mohamed El Amine Brahmia, Mourad Zghal

{"title":"HiFEL-OCKT: Hierarchical federated edge learning with objective congruence and multi-level knowledge transfer for IoT ecosystems","authors":"Ahmed-Rafik Baahmed, Jean-François Dollinger, Mohamed El Amine Brahmia, Mourad Zghal","doi":"10.1016/j.iot.2025.101868","DOIUrl":"10.1016/j.iot.2025.101868","url":null,"abstract":"<div><div>The explosive growth of Internet of Things (IoT) data and the demand for real-time decisions necessitate edge intelligence to overcome the latency and bandwidth limitations of cloud-only processing. Real-world IoT ecosystems are characterized by their high heterogeneity, which results from a wide variety of devices, sensors, environments, data, tasks, and resources, posing significant communication and computation efficiency challenges, scalability issues, and privacy concerns for edge intelligence. We propose HiFEL-OCKT, a novel hierarchical federated edge learning methodology for addressing the realistic high heterogeneity of IoT ecosystems, while enabling efficient edge intelligence. The key novelty of our proposed HiFEL-OCKT methodology is the efficient and scalable deployment of temporal intelligence at the edge by exploiting the valuable knowledge flowing at this level, which we define with the learning objective evolution, to ensure robust edge personalization through objective congruent collaboration and multi-level knowledge transfer between IoT devices. Through extensive experiments on multiple IoT domains, including smart buildings and industrial IoT with heterogeneous real-world datasets, our HiFEL-OCKT approach uncovered the novel ability in collaborating various highly heterogeneous IoT devices from different ecosystem settings. Our approach demonstrates superior performance and efficiency compared to the state-of-the-art approaches, with an improvement rate as high as 87.57 % in the edge knowledge personalization, while achieving significant speedups as high as 4.38 ×  in local training.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"36 ","pages":"Article 101868"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926730","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