Internet of Things最新文献_第3页

Scalable post-quantum non-interactive multi-signature for IoT-enabled blockchain systems 支持物联网的区块链系统的可扩展后量子非交互式多重签名

IF 7.1 3区计算机科学

Internet of Things Pub Date : 2026-07-01 Epub Date: 2026-06-15 DOI: 10.1016/j.iot.2026.101998

Jumei Zhang, Zhenhua Liu, Dongdong Yao, Jiaolian Zhao

{"title":"Scalable post-quantum non-interactive multi-signature for IoT-enabled blockchain systems","authors":"Jumei Zhang, Zhenhua Liu, Dongdong Yao, Jiaolian Zhao","doi":"10.1016/j.iot.2026.101998","DOIUrl":"10.1016/j.iot.2026.101998","url":null,"abstract":"<div><div>Multi-signature mechanisms play a critical role in enabling coordinated actions in decentralized systems such as Ethereum. However, most existing constructions require multiple rounds of interaction among signers, leading to significant communication overhead and limited scalability in large-scale IoT environments.</div><div>In this paper, we propose NI-SDVMS, a lattice-based non-interactive strong designated-verifier multi-signature scheme. In NI-SDVMS, each signer independently generates a signature that can only be verified by a designated verifier, thereby eliminating inter-signer communication and aggregation phases. To further enhance efficiency, we introduce a deterministic seed-based key derivation mechanism that enables local key reconstruction and reduces both communication and storage overhead.</div><div>The proposed scheme is instantiated using CRYSTALS-Dilithium and CRYSTALS-Kyber, and its security is based on the hardness of the MLWE and MSIS problems. Extensive experimental results demonstrate that NI-SDVMS achieves sub-millisecond signing latency and approximately 2.4 KB signature size per signer under ML-DSA Level II security. Moreover, the scheme exhibits linear verification scalability for up to 1024 signers without synchronization overhead, and similar scalability trends are observed at higher security levels. We further implement NI-SDVMS on Ethereum, demonstrating its practical feasibility.</div><div>Our work provides a scalable and post-quantum secure multi-signature framework that integrates non-interactivity, strong designated verification, and efficient deployment, making it well-suited for large-scale IoT-enabled blockchain applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"38 ","pages":"Article 101998"},"PeriodicalIF":7.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148499378","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

PQ-FLCom: Post-quantum secure communication for industrial federated learning PQ-FLCom：工业联合学习的后量子安全通信

IF 7.1 3区计算机科学

Internet of Things Pub Date : 2026-07-01 Epub Date: 2026-06-10 DOI: 10.1016/j.iot.2026.101994

Aniello Castiglione, Vincenzo Loia, Michele Nappi, Chiara Pero, Matteo Polsinelli

{"title":"PQ-FLCom: Post-quantum secure communication for industrial federated learning","authors":"Aniello Castiglione, Vincenzo Loia, Michele Nappi, Chiara Pero, Matteo Polsinelli","doi":"10.1016/j.iot.2026.101994","DOIUrl":"10.1016/j.iot.2026.101994","url":null,"abstract":"<div><div>Industrial innovations, driven by technologies such as the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), have revolutionized various operational domains, including supply chain management, equipment monitoring, predictive maintenance, and quality control. Deep Learning (DL) has become instrumental in addressing complex, data-driven challenges within industrial settings, relying on large datasets collected from heterogeneous sources. However, centralizing these datasets on cloud platforms poses significant privacy and security risks. Federated Learning (FL) offers a promising solution by enabling distributed model training across multiple nodes while preventing the exchange of raw data. Despite its advantages, FL introduces new vulnerabilities, especially related to the security of communication channels between participating entities. Traditional cryptographic mechanisms, such as digital signatures and model encryption, can mitigate these risks, but the emergence of quantum computing threatens the robustness of conventional solutions. This work explores the integration of Post-Quantum Cryptography (PQC) into FL to enhance security without incurring significant performance degradation. A modular FL architecture with three security levels is proposed: (i) an unprotected baseline; (ii) authenticated communication using ML-DSA digital signatures; and (iii) full protection combining ML-DSA and ML-KEM-512-based key encapsulation. Implemented within the Flower framework, the architecture is evaluated under simulated MitM attacks. Experimental results demonstrate that PQC-enhanced schemes effectively mitigate quantum-resistant threats while maintaining acceptable computational overhead, thereby ensuring model integrity and data confidentiality.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"38 ","pages":"Article 101994"},"PeriodicalIF":7.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148499380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated IoT-enabled greenhouse microclimate prediction using multivariate sequence-to-sequence LSTM-based encoder-decoder 使用多变量序列对序列lstm编码器-解码器的自动化物联网温室小气候预测

IF 7.1 3区计算机科学

Internet of Things Pub Date : 2026-07-01 Epub Date: 2026-06-26 DOI: 10.1016/j.iot.2026.102015

Yibin Wang, Mike Ojo, Azlan Zahid

{"title":"Automated IoT-enabled greenhouse microclimate prediction using multivariate sequence-to-sequence LSTM-based encoder-decoder","authors":"Yibin Wang, Mike Ojo, Azlan Zahid","doi":"10.1016/j.iot.2026.102015","DOIUrl":"10.1016/j.iot.2026.102015","url":null,"abstract":"<div><div>Precision agriculture implemented in controlled environment agriculture (CEA) can facilitate sustainable farming by providing accurately managed environments for crop cultivation. However, effectiveness relies on the timely prediction and management of internal microclimates. This research presents a novel framework for multi-step ahead greenhouse microclimate prediction that integrates the Internet of Things (IoT) sensing and machine learning for smart agriculture applications using sequence-to-sequence (Seq2Seq) multivariate encoder-decoder long short-term memory (MED-LSTM) networks, forecasting the dynamic and interdependent environmental factors including temperature, humidity, CO₂ concentration, and light condition. The proposed approach employs both internal and external temporal information to capture complex dynamics and variable interdependencies, generating accurate 24 h-ahead forecasts at a 1-hour scale. The proposed MED-LSTM model achieved MAEs of 0.89, 8.31, 9.33, and 131.45 for room temperature, humidity, CO₂, and PAR, respectively. Prediction error distribution on a scale of 24 h was also discussed. The results demonstrate that the proposed method significantly enhances the precision of microclimate forecasting against baseline models, enabling more proactive and efficient greenhouse management. Practical implications include improved energy efficiency, resource conservation, and crop quality optimization, contributing to sustainable agricultural practice. This research advances the application of IoT-enabled sensing and machine learning in smart farming and highlights the potential for innovative solutions to global food production challenges.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"38 ","pages":"Article 102015"},"PeriodicalIF":7.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148499383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in Internet-of-Things Systems and Services: Extended Best Papers from AINA 2025 物联网系统和服务的进展：aina2025最佳论文扩展

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-05-01 Epub Date: 2026-01-31 DOI: 10.1016/j.iot.2026.101883

Prof. Santi Caballé , Prof. Leonard Barolli

引用次数: 0

Corrigendum to “ResSqueezeXNet: A lightweight deep learning framework for real-time agricultural logistics optimization in IoT environments” ResSqueezeXNet：物联网环境下用于实时农业物流优化的轻量级深度学习框架的勘误表

IF 7.6 3区计算机科学

Internet of Things Pub Date : 2026-05-01 Epub Date: 2026-03-23 DOI: 10.1016/j.iot.2026.101928

Abdulwahab Ali Almazroi , Nasir Ayub

引用次数: 0

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

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

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

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