Internet of Things最新文献

{"title":"Invertible generative speech hiding with normalizing flow for secure IoT voice","authors":"Xiaoyi Ge,&nbsp;Xiongwei Zhang,&nbsp;Meng Sun,&nbsp;Kunkun SongGong,&nbsp;Xia Zou","doi":"10.1016/j.iot.2025.101606","DOIUrl":"10.1016/j.iot.2025.101606","url":null,"abstract":"<div><div>Speech-based control is widely used for remotely operating the Internet of Things (IoT) devices, but it risks eavesdropping and cyberattacks. Speech hiding enhances security by embedding secret speech in a cover speech to conceal communication behavior. However, existing methods are limited by the extracted secret speech’s poor intelligibility and the stego speech’s insufficient security. To address these challenges, we propose a novel invertible generative speech hiding framework that integrates the embedding process into the speech synthesis pipeline. Our method establishes a bijective mapping between secret speech inputs and stego speech outputs, conditioned on text-derived Mel-spectrograms. The embedding process employs a normalizing flow-based SecFlow module to map secret speech into Gaussian-distributed latent codes, which are subsequently synthesized into stego speech through a flow-based vocoder. Crucially, the invertibility of both SecFlow and the vocoder enables precise secret speech extraction during extraction. Extensive evaluation demonstrated the generated stego speech achieves high quality with a Perceived Evaluation of Speech Quality (PESQ) score of 3.40 and a Short-Term Objective Intelligibility (STOI) score of 0.96. Extracted secret speech exhibits high quality and intelligibility with a character error rate (CER) of 0.021. In addition, the latent codes of secret speech mapped and randomly sampled Gaussian noise are very close to each other, effectively guaranteeing security. The framework achieves real-time performance with 1.28s generation latency for 2.22s speech segment embedding(achieving a real-time factor (RTF) of 0.577), which ensures efficient covert communication for latency-sensitive IoT applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101606"},"PeriodicalIF":6.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DI4IoT: A comprehensive framework for IoT device-type identification through network flow analysis","authors":"Saurav Kumar,&nbsp;Manoj Das,&nbsp;Sukumar Nandi,&nbsp;Diganta Goswami","doi":"10.1016/j.iot.2025.101599","DOIUrl":"10.1016/j.iot.2025.101599","url":null,"abstract":"<div><div>The rapid growth of the Internet of Things (IoT) necessitates an effective Device-Type Identification System to monitor resource-constrained devices and mitigate potential security risks. Most Machine Learning (ML) based approaches for IoT Device-Type Identification utilize behavior-based, packet-based, flow-based characteristics, or a combination of these. Packet and behavior-based characteristics require analysis of individual packets. Furthermore, behavior-based characteristics need the analysis of application layer data (payloads), which may not be practical in case of encrypted traffic. Moreover, the existing approaches do not handle the mixed traffic (IoT and non-IoT) in an appropriate manner, suffer from frequent misclassification of closely related devices, and do not maintain performance when tested in different network environments. In contrast, flow-based characteristics neither require per-packet analysis nor the inspection of payloads. However, the existing flow-based approaches underperform as they consider a limited set of appropriate characteristics. To address these challenges, we propose DI4IoT, a two-stage flow-based Device-Type Identification framework using ML. The first stage categorizes the traffic into IoT and non-IoT, and the second stage identifies the device type from the categorized traffic. We create labeled flow-based characteristics and provide a methodology to select a minimal set of appropriate flow characteristics. We evaluate different ML algorithms to identify the suitable model for our proposed framework. The results demonstrate that our framework outperforms the state-of-the-art flow-based methods by over 10%. Furthermore, we evaluate and validate the performance gains in terms of Generalizability with complex network traffic compared to not only flow-based but also combined feature-type approaches.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101599"},"PeriodicalIF":6.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intelligent plant watering decision support system for drought monitoring & analysis based on AIoT and an LSTM time-series framework","authors":"Yao-Cheng Lin ,&nbsp;Tin-Yu Wu ,&nbsp;Chu-Fu Wang ,&nbsp;Jheng-Yang Ou ,&nbsp;Te-Chang Hsu ,&nbsp;Shiyang Lyu ,&nbsp;Ling Cheng ,&nbsp;Yu-Xiu Lin ,&nbsp;David Taniar","doi":"10.1016/j.iot.2025.101617","DOIUrl":"10.1016/j.iot.2025.101617","url":null,"abstract":"<div><div>Climate change has increased the severity of droughts, threatening global agricultural productivity. The implementation of information technology for enhancing smart agriculture has proven its great potential for supporting precision agriculture that can provide crops with the ability to defend themselves against environmental threats. Rice, which is a staple food crop in tropical and subtropical regions, is particularly sensitive to water stress during its critical growth stages. This study therefore focused on Tainung No. 67 rice, known for its drought resistance, to develop an intelligent AIoT-based plant watering decision support system. The proposed system aims to optimise water use and enhance agricultural resilience by integrating real-time monitoring, AI-driven analysis, and automated irrigation. Data were collected using hyperspectral imaging, point cloud analysis, and physiological indicators (measured by the LI-600 device), providing a comprehensive time-series dataset for model training. Principal component analysis (PCA) was used to reduce data dimensionality, and an LSTM-based AI framework was used to predict water stress severity. Experimental results showed high accuracy for all datasets, with the AI model achieving 97 % accuracy for point cloud data and 98 % accuracy for hyperspectral imagery. Scenarios with mixed missing data further validated the practicality and robustness of the system. This research highlights the potential to address drought-related challenges in agriculture through the integration of IoT, AI and advanced sensing technologies. The system not only optimises irrigation strategies but also contributes to sustainable farming practices through the preservation of water resources.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101617"},"PeriodicalIF":6.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel hybrid metaheuristic method for efficient decentralized IoT network layouts","authors":"Ferzat Anka","doi":"10.1016/j.iot.2025.101612","DOIUrl":"10.1016/j.iot.2025.101612","url":null,"abstract":"<div><div>This paper introduces a Hybrid Genetic Particle Swarm Optimization (HGPSO) method focusing on optimal and efficient sensor deployment in Wireless Sensor Networks (WSNs) and Decentralized IoT (DIoT) networks. Effective sensor placement in these networks necessitates the simultaneous optimization of numerous conflicting goals, such as maximizing coverage, ensuring connectivity, minimizing redundancy, and improving energy economy. Traditional optimization techniques and single metaheuristic algorithms frequently encounter these difficulties, demonstrating premature convergence or inadequately balancing exploration and exploitation phases. The suggested HGPSO effectively combines the advantageous features of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to overcome these limitations. The strong global exploration capabilities of GA, which successfully preserve variety and avert premature convergence, are integrated with the swift local exploitation and convergence attributes of PSO. A new multi-objective fitness function specifically designed for sensor deployment issues is created, facilitating the effective handling of trade-offs between conflicting objectives. The efficacy of the HGPSO approach is meticulously assessed in seven consistent situations and practical applications, encompassing environments with intricate impediments. A comparative examination is performed against six prominent metaheuristic algorithms acknowledged in literature. Results indicate that HGPSO regularly surpasses these competing methods across all assessment categories. Regarding average fitness values, HGPSO exceeds POHBA by 14 %, MAOA by 20 %, IDDT-GA by 21 %, EFSSA by 29 %, CFL-PSO by 35 %, and OBA by 45 %. These findings underscore HGPSO's exceptional theoretical framework and validate its practical relevance for extensive, real-world IoT implementations. By adeptly utilizing the exploration capabilities of GA and the exploitation strengths of PSO, HGPSO becomes a highly versatile and resilient optimization framework, making substantial contributions to addressing the deployment issues of next-generation IoT and WSN.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101612"},"PeriodicalIF":6.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Invisible eyes: Real-time activity detection through encrypted Wi-Fi traffic without machine learning","authors":"Muhammad Bilal Rasool ,&nbsp;Uzair Muzamil Shah ,&nbsp;Mohammad Imran ,&nbsp;Daud Mustafa Minhas ,&nbsp;Georg Frey","doi":"10.1016/j.iot.2025.101602","DOIUrl":"10.1016/j.iot.2025.101602","url":null,"abstract":"<div><div>Wi-Fi camera-based home monitoring systems are increasingly popular for improving security and real-time observation. However, reliance on Wi-Fi introduces privacy vulnerabilities, as sensitive activities within monitored areas can be inferred from encrypted traffic. This paper presents a lightweight, non-ML attack model that analyzes Wi-Fi traffic metadata—such as packet size variations, serial number sequences, and transmission timings—to detect live streaming, motion detection, and person detection. Unlike machine learning-based approaches, our method requires no training data or feature extraction, making it computationally efficient and easily scalable. Empirical testing at varying distances (10 m, 20 m, and 30 m) and under different environmental conditions shows accuracy rates of up to 90% at close range and 72% at greater distances, demonstrating its robustness. Compared to existing ML-based techniques, which require extensive retraining for different camera manufacturers, our approach provides a universal and adaptable attack model. This research underscores significant privacy risks in Wi-Fi surveillance systems and emphasizes the urgent need for stronger encryption mechanisms and obfuscation techniques to mitigate unauthorized activity inference.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101602"},"PeriodicalIF":6.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FogScheduler: A resource optimization framework for energy-efficient computing in fog environments","authors":"Eyhab Al-Masri,&nbsp;Sri Vibhu Paruchuri","doi":"10.1016/j.iot.2025.101609","DOIUrl":"10.1016/j.iot.2025.101609","url":null,"abstract":"<div><div>The rapid growth of Internet of Things (IoT) devices has created a pressing demand for fog computing, offering an effective alternative to the inherent constraints imposed by traditional cloud computing. Efficient resource management in fog environments remains challenging due to device heterogeneity, dynamic workloads, and conflicting performance objectives. This paper introduces FogScheduler, an innovative resource allocation algorithm that optimizes performance and energy efficiency in IoT-fog ecosystems using the TOPSIS method to rank resources based on attributes like MIPS, Thermal Design Power (TDP), memory bandwidth, and network latency. Experiments highlight FogScheduler's notable achievements, including a 46.1 % reduction in energy consumption in the best case compared to the Greedy Algorithm (GA) and a 45.6 % reduction in makespan compared to the First-Fit Algorithm (FFA). On average, FogScheduler achieves a 27 % reduction in energy consumption compared to FFA, demonstrating its consistent ability to optimize resource allocation. Even in worst-case scenarios, FogScheduler outperforms traditional algorithms, underscoring its robustness across varying resource contention levels. Results from our experiments demonstrate that FogScheduler is a highly effective solution for energy-aware and performance-optimized resource management, offering significant potential for IoT-fog-cloud ecosystems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101609"},"PeriodicalIF":6.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Belief-MobileNet1D: A novel deep learning approach for anomaly detection in industrial big data","authors":"Tzu-Chia Chen","doi":"10.1016/j.iot.2025.101593","DOIUrl":"10.1016/j.iot.2025.101593","url":null,"abstract":"<div><div>Early fault or unusual behavior detection can reduce the risk of equipment failure improve performance and increase safety. Anomaly detection in industrial big data involves identifying deviations from normal patterns in large-scale datasets. This method assists in preventing equipment failures optimizing maintenance schedules and raising overall operational efficiency in industrial settings by identifying anomalous behaviors or outliers. Through the utilization of deep learning procedures, this investigation endeavours to apply are fined procedure for anomaly detection in industrial big data. Pre-processing, feature selection and Anomaly detection are three steps of a process that are used. The input data is first fed into MapReduce framework where it is divided and pre-processed. Imputation of missing data and Yeo-Jhonson transformation are then applied to eliminate noise from data. After pre-processed data is generated, it is put through a feature selection phase using Serial Exponential Lotus Effect Optimization Algorithm (SELOA). The algorithm is created newly by combining Lotus Effect Optimization Algorithm (LOA) with Exponential Weighted Moving Average (EWMA). Finally, anomaly detection is done using the features that are selected by means of Deep Belief-MobileNet1D, which combines MobileNet1D and Deep Belief Network (DBN). With a recall of 96.2 %, precision of 92.8 %, F1 score of 94.5 % and accuracy of 95.9 %, results show that the proposed strategy surpasses standard approaches. These findings demonstrate Deep Belief-MobileNet1D model's ability to detect anomalies in industrial big data.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101593"},"PeriodicalIF":6.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Edge-based physical asset and digital twin virtualization framework to support cognitive digital twins","authors":"Rolando Herrero ,&nbsp;Mallesham Dasari","doi":"10.1016/j.iot.2025.101601","DOIUrl":"10.1016/j.iot.2025.101601","url":null,"abstract":"<div><div>In <em>Cyber-Physical Systems</em> (CPSs), devices interact with smart applications by relying on <em>Internet of Things</em> (IoT) protocols to transmit sensor readings from monitoring <em>Physical Assets</em> (PAs). The applications support mapping mechanisms that constitute <em>Digital Twins</em> (DTs) to mimic the behavior of the actual PAs. Changes in PAs are mirrored in the DTs, and vice versa. This duality enables the creation of <em>Cognitive Digital Twins</em> (CDTs), where the readings generated by PAs enable the extraction of knowledge to support actuation through AI models on the PAs. This paper introduces a generic <em>PA and DT Virtualization</em> (PDV) framework that leverages the Internet Engineering Task Force layered architecture through an application layer smart sublayer that learns from the interaction between PAs and DTs to enable edge-based CDT support. Although this framework is agnostic of the CPS under consideration, its focus in this paper is on Industry 5.0 applications. In this context, a new IoT protocol is proposed to enable a PDV scheme that provides the reliable automation of the interaction between PAs and DTs even in the presence of different levels of wireless network layer impairments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101601"},"PeriodicalIF":6.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel federated learning-based IDS for enhancing UAVs privacy and security","authors":"Ozlem Ceviz ,&nbsp;Pinar Sadioglu ,&nbsp;Sevil Sen ,&nbsp;Vassilios G. Vassilakis","doi":"10.1016/j.iot.2025.101592","DOIUrl":"10.1016/j.iot.2025.101592","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies focused predominantly on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices. However, these approaches face challenges including computation and storage costs, along with a single point of failure risk, threatening data privacy and availability. The widespread dispersion of data across interconnected devices underscores the need for decentralized approaches. This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs. FL-IDS reduces computation and storage costs for both clients and the central server, which is crucial for resource-constrained UAVs. Operating in a decentralized manner, FL-IDS enables UAVs to collaboratively train a global intrusion detection model without sharing raw data, thus avoiding delay in decisions based on collected data, as is often the case with traditional methods. Experimental results demonstrate FL-IDS’s competitive performance with Central IDS (C-IDS) while mitigating privacy concerns, with the Bias Towards Specific Clients (BTSC) method further enhancing FL-IDS performance even at lower attacker ratios. Comparative analysis with traditional intrusion detection methods, including Local IDS (L-IDS), sheds light on the strengths of FL-IDS. This study significantly contributes to UAV security by introducing a privacy-aware, decentralized intrusion detection approach tailored to UAV networks. Moreover, by introducing a realistic dataset for FANETs and federated learning, our approach differs from others lacking high dynamism and 3D node movements or accurate federated data federations.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101592"},"PeriodicalIF":6.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting latent topics and trends in IoT and e-commerce using BERTopic modeling","authors":"Yu-Sheng Su ,&nbsp;Jun-qing Wang ,&nbsp;Shou-Hsi Tu ,&nbsp;Kuo-Ti Liao ,&nbsp;Chien-Liang Lin","doi":"10.1016/j.iot.2025.101604","DOIUrl":"10.1016/j.iot.2025.101604","url":null,"abstract":"<div><div>The rapid development of the Internet of Things (IoT) is reshaping e-commerce, driving business model innovation and enhancing operational efficiency. However, existing research primarily focuses on specific application scenarios of IoT, while lacking a systematic exploration of its overall development trends, core research topics, and challenges. To address this gap, this study employed BERTopic topic modeling to systematically analyze key research themes and evolutionary trends of IoT in the e-commerce domain, based on 169 highly relevant papers from the Web of Science database (2010–2024). The findings revealed four core themes: (1) the transformation of e-commerce business models driven by IoT technologies, (2) the role of blockchain in data security and trust mechanisms, (3) the synergy between smart logistics and e-commerce, and (4) privacy protection and personal data management in the IoT ecosystem. Additionally, this study identified a shift in IoT applications from an initial focus on supply chain optimization to an increasing emphasis on data-driven decision-making, intelligent business models, and data privacy protection. By conducting an in-depth analysis of the dynamic evolution of these themes, this research not only fills the knowledge gap regarding the current state and trends of IoT research in e-commerce, but also provides the academic community with an innovative method applicable to large-scale text data analysis. Furthermore, for businesses and policymakers, strengthening cross-sectoral technological integration, improving privacy protection mechanisms, and enhancing policy support are suggested to promote the sustainable development of IoT and e-commerce. This research enriches the academic discourse on the synergy between IoT and e-commerce in the context of digital transformation, and provides strategic guidance for practitioners.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101604"},"PeriodicalIF":6.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842803","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}