Internet of Things最新文献

{"title":"Responsible smart home technology adoption: exploring public perceptions and key adoption factors","authors":"Wenda Li ,&nbsp;Tan Yigitcanlar ,&nbsp;Alireza Nili ,&nbsp;Will Browne ,&nbsp;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}

{"title":"An efficient Genetic algorithm-based defensive method to mitigate multiple attacks in RPL-enabled IoT network","authors":"Prajna Paramita Mohapatra ,&nbsp;Bala Krishnan R. ,&nbsp;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}

{"title":"Quantum cryptography as a solution for secure Wireless Sensor Networks: Roadmap, challenges and solutions","authors":"Diksha Chawla ,&nbsp;Khushboo Jain ,&nbsp;Pawan Singh Mehra ,&nbsp;Ashok Kumar Das ,&nbsp;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}

{"title":"Advanced security frameworks for UAV and IoT: A deep learning approach","authors":"Nordine Quadar ,&nbsp;Abdellah Chehri ,&nbsp;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}

{"title":"H-TERF: A hybrid approach combining fuzzy multi-criteria decision-making techniques and enhanced random forest to improve WBAN-IoT","authors":"Parisa Khoshvaght ,&nbsp;Jawad Tanveer ,&nbsp;Amir Masoud Rahmani ,&nbsp;Mohammad Mohammadi ,&nbsp;Amin Mehranzadeh ,&nbsp;Jan Lansky ,&nbsp;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}

{"title":"NIDS-CNNRF integrating CNN and random forest for efficient network intrusion detection model","authors":"Kai Yang ,&nbsp;JiaMing Wang ,&nbsp;GeGe Zhao ,&nbsp;XuAn Wang ,&nbsp;Wei Cong ,&nbsp;ManZheng Yuan ,&nbsp;JiaXiong Luo ,&nbsp;XiaoFang Dong ,&nbsp;JiaRui Wang ,&nbsp;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}

{"title":"THE-TAFL: Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning and Learning Rate Optimization","authors":"Farhan Ullah ,&nbsp;Nazeeruddin Mohammad ,&nbsp;Leonardo Mostarda ,&nbsp;Diletta Cacciagrano ,&nbsp;Shamsher Ullah ,&nbsp;Yue Zhao","doi":"10.1016/j.iot.2025.101605","DOIUrl":"10.1016/j.iot.2025.101605","url":null,"abstract":"<div><div>The healthcare industry is becoming more vulnerable to privacy violations and cybercrime due to the pervasive dissemination and sensitivity of medical data. Advanced data security systems are needed to protect privacy, data integrity, and dependability as confidentiality breaches increase across industries. Decentralized healthcare networks face challenges in feature extraction during local training, hindering effective federated averaging and learning rate optimization, which affects data processing and model training efficiency. This paper introduces a novel approach of Transforming Healthcare Edge with Transformer-based Adaptive Federated Learning (THE-TAFL) and Learning Rate Optimization. In this paper, we combine Transformer-based Adaptive Federated Learning (TAFL) with learning rate optimization to improve the privacy and security of healthcare information on edge devices. We used data augmentation approaches that generate robust and generalized input datasets for deep learning models. Next, we use the Vision Transformer (ViT) model for local training, generating Local Model Weights (LMUs) that enhance feature extraction and learning. We designed a training optimization method that improves model performance and stability by combining a loss function with weight decay for regularization, learning rate scheduling, and gradient clipping. This ensures effective training across decentralized clients in a Federated Learning (FL) framework. The FL server receives LMUs from many clients and aggregates them. The aggregation procedure utilizes adaptive federated averaging to aggregate the LMUs based on the performance of each client. This adaptive method ensures that high-performing clients contribute more to the Global Model Update (GMU). Following aggregation, clients receive the GMU to continue training with the updated parameters, ensuring collaborative and dynamic learning. The proposed method provides better performance on two standard datasets using various numbers of clients.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101605"},"PeriodicalIF":6.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855921","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":"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}