Internet of Things最新文献

A privacy-preserving LDA model training scheme based on federated learning 一种基于联邦学习的隐私保护LDA模型训练方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-29 DOI: 10.1016/j.iot.2025.101620

Hua Shen, Ying Cao, Bai Liu

引用次数: 0

SOLAR: Illuminating LLM performance in API discovery and service ranking for edge AI and IoT SOLAR：照亮LLM在边缘AI和IoT的API发现和服务排名方面的性能

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-26 DOI: 10.1016/j.iot.2025.101630

Eyhab Al-Masri, Ishwarya Narayana Subramanian

{"title":"SOLAR: Illuminating LLM performance in API discovery and service ranking for edge AI and IoT","authors":"Eyhab Al-Masri, Ishwarya Narayana Subramanian","doi":"10.1016/j.iot.2025.101630","DOIUrl":"10.1016/j.iot.2025.101630","url":null,"abstract":"<div><div>The growing complexity of web service and API discovery calls for robust methods to evaluate how well Large Language Models (LLMs) retrieve, rank, and assess APIs. However, current LLMs often produce inconsistent results, highlighting the need for structured, multi-dimensional evaluation. This paper introduces SOLAR (Systematic Observability of LLM API Retrieval), a framework that assesses LLM performance across three key dimensions: functional capability, implementation feasibility, and service sustainability. We evaluate four leading LLMs—GPT-4 Turbo (OpenAI), Claude 3.5 Sonnet (Anthropic), LLaMA 3.2 (Meta), and Gemini 2.0 Flash (Google)—on their ability to identify, prioritize, and evaluate APIs across varying query complexities. Results show GPT-4 Turbo and Claude 3.5 Sonnet achieve high functional alignment (FCA ≥ 0.75 for simple queries) and strong ranking consistency (Spearman’s ρ ≈ 0.95). However, all models struggle with implementation feasibility and long-term sustainability, with feasibility scores declining as complexity increases and sustainability scores remaining low (SSI ≈ 0.40), limiting deployment potential. Despite retrieving overlapping APIs, models often rank them inconsistently, raising concerns for AI-driven service selection. SOLAR identifies strong correlations between functional accuracy and ranking stability but weaker links to real-world feasibility and longevity. These findings are particularly relevant for Edge AI environments, where real-time processing, distributed intelligence, and reliable API integration are critical. SOLAR offers a comprehensive lens for evaluating LLM effectiveness in service discovery, providing actionable insights to advance robust, intelligent API integration across IoT and AI-driven systems. Our work aims to inform both future model development and deployment practices in high-stakes computing environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101630"},"PeriodicalIF":6.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891270","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

Reliability-oriented dynamic task offloading for time-varying satellite IoT: A lightweight multi-agent cooperative strategy optimization method 时变卫星物联网面向可靠性的动态任务卸载：一种轻量级多智能体协同策略优化方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-26 DOI: 10.1016/j.iot.2025.101603

Xin-tong Pei , Zhen-jiang Zhang , Qing-an Zeng , Ying-si Zhao

{"title":"Reliability-oriented dynamic task offloading for time-varying satellite IoT: A lightweight multi-agent cooperative strategy optimization method","authors":"Xin-tong Pei , Zhen-jiang Zhang , Qing-an Zeng , Ying-si Zhao","doi":"10.1016/j.iot.2025.101603","DOIUrl":"10.1016/j.iot.2025.101603","url":null,"abstract":"<div><div>The integration of satellite Internet of Things (IoT) with Mobile Edge Computing (MEC) has revolutionized global connectivity, enabling intelligent applications in remote regions while presenting distinctive reliability challenges stemming from intermittent satellite connections and spatio-temporal workload dynamic. However, traditional centralized approaches fail to address these challenges, as their reliance on stable connections and centralized control fundamentally conflicts with the dynamic nature of satellite networks. Motivated by these challenges, we propose a decentralized cooperative task offloading framework where satellite edge servers independently manage task oddloading, inter-satellite task migration, and resource allocation. To enhance offloading reliability in bursty traffic scenarios, we leverage Stochastic Network Calculus (SNC) to integrate communication and computation failure probabilities into our optimization framework. Aiming at coordinated decision-making and global optimization, this work presents a lightweight cooperative task offloading algorithm (MA-LWCTO) utilizing multi-agent soft actor–critic, where the satellites make decisions through local state and shared neighboring policy information. In response to the challenges of increasing computational complexity and state space expansion, we develop an information extraction mechanism based on long short-term memory variational auto-encoder with attention (VLAEA), facilitating efficient information while reducing communication overhead. Extensive simulations based on pre-obtained satellite trajectory data demonstrate that the proposed algorithm significantly enhances reliability while reducing system service costs in satellite–terrestrial MEC environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101603"},"PeriodicalIF":6.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881291","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

Responsible smart home technology adoption: exploring public perceptions and key adoption factors 负责任的智能家居技术采用：探索公众认知和关键采用因素

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-25 DOI: 10.1016/j.iot.2025.101622

Wenda Li , Tan Yigitcanlar , Alireza Nili , Will Browne , Fei Li

{"title":"Responsible smart home technology adoption: exploring public perceptions and key adoption factors","authors":"Wenda Li , Tan Yigitcanlar , Alireza Nili , Will Browne , Fei Li","doi":"10.1016/j.iot.2025.101622","DOIUrl":"10.1016/j.iot.2025.101622","url":null,"abstract":"<div><div>Initially, smart home technology gained traction for its aesthetic appeal, functionality, and allure to early tech enthusiasts. Today, it plays a crucial role in enhancing security, healthcare, and energy management. As AI becomes more integrated into daily life, smart homes offer increased convenience and automation. Yet, concerns over privacy, data security, and ethical use have grown. This study leverages social media analytics to analyze a longitudinal dataset of over 150,000 tweets from Australia between 2016 and 2023, using quantitative, sentiment, and content analysis. The goal is to investigate the evolving public discourse around smart home technologies, focusing on user key concerns. A novel insight from the study reveals a rising awareness and demand for responsible practices in the development and deployment of smart home technologies, which may influence user adoption intentions and behaviors. This suggests a potential shift in user priorities from seeking functionality and convenience to becoming more concerned with ethical standards and responsible use. The study makes a novel contribution as it identifies a new trend in public discourse that extends beyond the traditional drivers of smart home technology adoption. By capturing these dynamics, this paper provides critical insights for stakeholders—particularly in the smart home industry and regulatory sectors—to inform the development of more responsible, user-centered products and policies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101622"},"PeriodicalIF":6.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888155","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 efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network 一种有效的基于遗传算法的防御方法，以减轻rpl支持的物联网网络中的多重攻击

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-24 DOI: 10.1016/j.iot.2025.101614

Prajna Paramita Mohapatra , Bala Krishnan R. , Madhukrishna Priyadarsini

{"title":"An efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network","authors":"Prajna Paramita Mohapatra , Bala Krishnan R. , Madhukrishna Priyadarsini","doi":"10.1016/j.iot.2025.101614","DOIUrl":"10.1016/j.iot.2025.101614","url":null,"abstract":"<div><div>Internet of Things (IoT) is the interconnection of billions of devices over the Internet. It is an umbrella of various concepts, protocols, and technologies that are used to create numerous benefits in productivity and automation. Despite the benefits it provides, there are challenges such as high cost of IoT devices, time-constraints, and overuse of Internet protocols and technologies which attackers often take advantage. To address this, IoT networks must have secure routing protocols that can provide security to the network and also utilize the benefits of existing technologies. However the lack of infrastructure, dynamic topology changes, resource constraints, and unreliable links make even the best existing protocol “Routing Protocol for Low Power and Lossy Network (RPL)” to be vulnerable for various attacks. Besides the trust management, that ensures only the reliable and legitimate nodes to participate in routing decisions, is another critical aspect that many existing solutions fail to consider. Hence, in this research, we propose a novel secure routing technique “Genetic Algorithm-based Trusted framEwork for RPL (GATE-RPL)” that supports multi-topology routing and provides security to various devices in the IoT network. To overcome the security issues, the proposed work:(i) provides a “dynamic trust management technique” that maximizes the trust of nodes, links, and routing performance using a combination of K-means clustering and extended Genetic algorithm; and subsequently (ii) finds a trusted routing path between every node in the network. The experimental results indicate an average of 0.012% packet loss, 10.5 Mbps throughput, and 99% accuracy in identifying trustworthy routing paths.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101614"},"PeriodicalIF":6.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888156","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

Quantum cryptography as a solution for secure Wireless Sensor Networks: Roadmap, challenges and solutions 量子加密作为安全无线传感器网络的解决方案：路线图，挑战和解决方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-23 DOI: 10.1016/j.iot.2025.101610

Diksha Chawla , Khushboo Jain , Pawan Singh Mehra , Ashok Kumar Das , Basudeb Bera

{"title":"Quantum cryptography as a solution for secure Wireless Sensor Networks: Roadmap, challenges and solutions","authors":"Diksha Chawla , Khushboo Jain , Pawan Singh Mehra , Ashok Kumar Das , Basudeb Bera","doi":"10.1016/j.iot.2025.101610","DOIUrl":"10.1016/j.iot.2025.101610","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSN) can collect information from almost everywhere, so they can be deployed ubiquitously. Nonetheless, data transmission within the WSN communication framework is vulnerable to interception. Therefore, it is necessary to create a secure WSN communication environment. The idea of cryptography has been used for years for secure data communication. The popular schemes are RSA and hash functions. However, with the significant advancement in the field of Quantum Computing, there is a need for unconditional security in data generated and distributed from sensor nodes. Quantum computing is in its early stages, but we can evaluate its impact on conventional cryptographic techniques by analyzing the Quantum Shor’s algorithm. Motivated by the aforementioned issues, this paper sheds light on Quantum Cryptography by analyzing various security concerns in WSN. In our work, Quantum Cryptography-based secure key agreement, Quantum Mistrustful communication, Quantum Entanglement and Quantum Teleportation are reviewed. The roles of each technique in association with WSN are analyzed. We proposed a novel Quantum WSN (QWSN) architecture to achieve high data security and effective data transmission. We analyze Quantum simulation tools, security attacks, and the influence of Quantum technology on traditional cryptographic methods. We have also included the demonstrations of quantum circuits on IBM Quantum Composer (IQC). The benefits and results are also discussed. In addition, we also discussed the open issues and challenges in implementing Quantum-based security for WSN. The article identifies and emphasizes several unresolved research challenges and future directions for advancing research and innovation in the domain of Quantum Cryptography.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101610"},"PeriodicalIF":6.0,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878908","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

Advanced security frameworks for UAV and IoT: A deep learning approach 无人机和物联网的高级安全框架：一种深度学习方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-22 DOI: 10.1016/j.iot.2025.101594

Nordine Quadar , Abdellah Chehri , Benoit Debaque

{"title":"Advanced security frameworks for UAV and IoT: A deep learning approach","authors":"Nordine Quadar , Abdellah Chehri , Benoit Debaque","doi":"10.1016/j.iot.2025.101594","DOIUrl":"10.1016/j.iot.2025.101594","url":null,"abstract":"<div><div>The integration of unmanned aerial vehicles (UAVs) has opened new avenues for enhanced security and functionality. The security of UAVs through the detection and analysis of unique signal patterns is a critical aspect of this technological advancement. This approach leverages intrinsic signal characteristics to distinguish between UAVs of identical models, providing a robust layer of security at the communication level. The application of artificial intelligence in UAV signal analysis has shown significant potential in improving UAV identification and authentication. Recent advancements utilize deep learning techniques with raw In-phase and Quadrature (I/Q) data to achieve high-precision UAV signal recognition. However, existing deep learning models face challenges with unfamiliar data scenarios involving I/Q data. This work explores alternative transformations of I/Q data and investigates the integration of statistical features such as mean, median, and mode across these transformations. It also evaluates the generalization capability of the proposed methods in various environments and examines the impact of signal-to-noise ratio (SNR) on recognition accuracy. Experimental results underscore the promise of our approach, establishing a solid foundation for practical deep-learning-based UAV security solutions and contributing to the field of IoT.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101594"},"PeriodicalIF":6.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864456","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

NIDS-CNNRF integrating CNN and random forest for efficient network intrusion detection model NIDS-CNNRF集成了CNN和随机森林的高效网络入侵检测模型

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-17 DOI: 10.1016/j.iot.2025.101607

Kai Yang , JiaMing Wang , GeGe Zhao , XuAn Wang , Wei Cong , ManZheng Yuan , JiaXiong Luo , XiaoFang Dong , JiaRui Wang , Jing Tao

{"title":"NIDS-CNNRF integrating CNN and random forest for efficient network intrusion detection model","authors":"Kai Yang , JiaMing Wang , GeGe Zhao , XuAn Wang , Wei Cong , ManZheng Yuan , JiaXiong Luo , XiaoFang Dong , JiaRui Wang , Jing Tao","doi":"10.1016/j.iot.2025.101607","DOIUrl":"10.1016/j.iot.2025.101607","url":null,"abstract":"<div><div>Network intrusion detection is crucial for enhancing network security; however, existing models face three prominent challenges. First, many models place too much emphasis on overall accuracy, often neglecting the accurate distinction between different types of attacks. Second, due to feature redundancy in complex high-dimensional attack traffic, these models struggle to extract key information from large feature sets. Lastly, when dealing with imbalanced datasets, models tend to focus on learning from classes with larger sample sizes, thus overlooking those with fewer instances. To address these issues, this paper proposes a novel network intrusion detection model, NIDS-CNNRF. This model integrates Convolutional Neural Networks (CNN) for feature extraction and Random Forest (RF) for classifying attack traffic, enabling precise identification of various attack types. The Adaptive Synthetic Sampling (ADASYN) algorithm is employed to mitigate the bias toward classes with larger sample sizes, while Principal Component Analysis (PCA) is used to address feature redundancy, allowing the model to effectively extract key information. Experimental results demonstrate that the NIDS-CNNRF model significantly outperforms traditional intrusion detection models in enhancing network security, with superior performance observed on the KDD CUP99, NSL_KDD, CIC-IDS2017, and CIC-IDS2018 datasets.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101607"},"PeriodicalIF":6.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842735","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

H-TERF: A hybrid approach combining fuzzy multi-criteria decision-making techniques and enhanced random forest to improve WBAN-IoT H-TERF：一种将模糊多准则决策技术与增强随机森林相结合的改进wlan - iot的混合方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-17 DOI: 10.1016/j.iot.2025.101613

Parisa Khoshvaght , Jawad Tanveer , Amir Masoud Rahmani , Mohammad Mohammadi , Amin Mehranzadeh , Jan Lansky , Mehdi Hosseinzadeh

{"title":"H-TERF: A hybrid approach combining fuzzy multi-criteria decision-making techniques and enhanced random forest to improve WBAN-IoT","authors":"Parisa Khoshvaght , Jawad Tanveer , Amir Masoud Rahmani , Mohammad Mohammadi , Amin Mehranzadeh , Jan Lansky , Mehdi Hosseinzadeh","doi":"10.1016/j.iot.2025.101613","DOIUrl":"10.1016/j.iot.2025.101613","url":null,"abstract":"<div><div>The Internet of Things (IoT) technology today has grown rapidly compared to the last few years, and the use of this technology has increased the quality of service to users day by day. The various applications of IoT have caused the attention of this innovation to enhance among different organizations. One of the important challenges of the IoT is routing, which can affect having a stable network. In this research, a hybrid approach called H-TERF (Hybrid TOPSIS and Enhanced Random Forest) is proposed for achieving efficient routing in IoT networks, specifically in Wireless Body Area Networks (WBAN). This method initially cluster nodes by using the DBSCAN clustering algorithm to optimize intra-cluster communication. Then, for routing, the nodes are ranked using the Fuzzy TOPSIS and Fuzzy AHP. This ranking is determined by several criteria, including the remaining energy of nodes, node memory, and throughput. Additionally, to manage more complex criteria such as node historical records and traffic rate, the initial ranking by the TOPSIS approach, along with the other mentioned criteria, is fed into an enhanced random forest model to identify the optimal path. This hybrid method enhances network performance in terms of lifespan, efficiency, delay, and packet delivery ratio. The outcomes of the simulation show that the suggested method surpasses existing approaches and is highly effective for application in IoT and WBAN networks. For example, the performance improvement of the proposed approach over the F-EVM, DECR, and DHH-EFO approaches in energy consumption was 20.62%, 25.85%, and 32.57%, respectively.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101613"},"PeriodicalIF":6.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855922","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

Invertible generative speech hiding with normalizing flow for secure IoT voice 具有归一化流的可逆生成语音隐藏用于安全物联网语音

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-04-17 DOI: 10.1016/j.iot.2025.101606

Xiaoyi Ge, Xiongwei Zhang, Meng Sun, Kunkun SongGong, Xia Zou

{"title":"Invertible generative speech hiding with normalizing flow for secure IoT voice","authors":"Xiaoyi Ge, Xiongwei Zhang, Meng Sun, Kunkun SongGong, 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}

引用次数: 0