Internet of Things最新文献_第4页

Physical unclonable functions and QKD-based authentication scheme for IoT devices using blockchain 使用区块链的物联网设备的物理不可克隆功能和基于 QKD 的身份验证方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-25 DOI: 10.1016/j.iot.2024.101404

Tyson Baptist D Cunha , Kiran M. , Ritik Ranjan , Athanasios V. Vasilakos

{"title":"Physical unclonable functions and QKD-based authentication scheme for IoT devices using blockchain","authors":"Tyson Baptist D Cunha , Kiran M. , Ritik Ranjan , Athanasios V. Vasilakos","doi":"10.1016/j.iot.2024.101404","DOIUrl":"10.1016/j.iot.2024.101404","url":null,"abstract":"<div><div>As the number of Internet of Things (IoT) devices is increasing exponentially, strong security measures are needed to guard against different types of cyberattacks. This research offers a novel IoT device authentication technique to mitigate these challenges by integrating three cutting-edge technologies namely blockchain technology, Quantum Key Distribution (QKD), and Physically Unclonable Functions (PUFs). By utilizing the distinctive qualities of PUFs for device identification and the unrivaled security of QKD for key exchange, the proposed approach seeks to address the significant security issues present in IoT environments. Adopting blockchain technology ensures transparency and verifiability of the authentication process across distributed IoT networks by adding an unchangeable, decentralized layer of trust. An examination of the computing and communication costs reveals that the proposed protocol is effective, necessitating low computational resources that are critical for IoT devices with limited resources. The protocol’s resistance against a variety of attacks is demonstrated by formal proofs based on the Real-Or-Random (ROR) model and security evaluations using the Scyther tool, ensuring the integrity and secrecy of communications. Various threats are analyzed, and the protocol is proven to be secure and efficient from all forms of attacks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101404"},"PeriodicalIF":6.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586765","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

IoT-driven wearable devices enhancing healthcare: ECG classification with cluster-based GAN and meta-features 物联网驱动的可穿戴设备促进医疗保健：利用基于聚类的 GAN 和元特征进行心电图分类

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-25 DOI: 10.1016/j.iot.2024.101405

Constantino Msigwa , Denis Bernard , Jaeseok Yun

{"title":"IoT-driven wearable devices enhancing healthcare: ECG classification with cluster-based GAN and meta-features","authors":"Constantino Msigwa , Denis Bernard , Jaeseok Yun","doi":"10.1016/j.iot.2024.101405","DOIUrl":"10.1016/j.iot.2024.101405","url":null,"abstract":"<div><div>Wearable devices in medical technology promise advancements in healthcare but face challenges like limited data use and delayed analysis, hindering their real-time effectiveness. Enabling wearable devices with edge computing maximizes their potential, allowing real-time tasks like ECG classification to be performed intelligently at the device level. We propose the Wearable IoT Edge, a computing device that empowers wearable health devices with real-time data insights and IoT capabilities, facilitated by the Wearable Interworking Proxy and compliant with oneM2M standard-based server. We demonstrate the application of a proposed Wearable IoT Edge by addressing ECG classification challenges. Our approach addresses data imbalance by integrating a Cluster-Based Generative Adversarial Network (GAN) with meta-features derived from Convolutional Neural Networks (CNNs) and Transformers to enhance ECG classification accuracy. Experimental results demonstrate a 3.18% improvement in the F1 score for ECG classification validating the effectiveness of the approach. These findings highlight the Wearable IoT Edge’s potential to improve real-time healthcare monitoring and diagnostics.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101405"},"PeriodicalIF":6.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702984","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

Analyzing common lexical features of fake news using multi-head attention weights 利用多头注意力权重分析假新闻的共同词汇特征

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-24 DOI: 10.1016/j.iot.2024.101409

Mamoru Mimura , Takayuki Ishimaru

{"title":"Analyzing common lexical features of fake news using multi-head attention weights","authors":"Mamoru Mimura , Takayuki Ishimaru","doi":"10.1016/j.iot.2024.101409","DOIUrl":"10.1016/j.iot.2024.101409","url":null,"abstract":"<div><div>Numerous approaches have been developed to identify fake news through machine learning; however, these methods are predominantly assessed using singular datasets specific to certain fields, leading to a scarcity of research on versatile models adaptable to a range of domains. This study evaluates the adaptability of a fake news detection model across diverse fields, employing three distinct datasets. Furthermore, the study leverages the multi-head attention feature of bidirectional encoder representations from transformers (BERT) to scrutinize the feature extraction process in the model. In our analysis, we focused on words that are commonly emphasized by machine learning in fake news detection. The dataset comprised 27,442 instances of genuine news and 28,359 instances of fabricated news, each distinctly labeled. To examine the focal words, we utilized multi-head attention, a component of BERT. This mechanism assigns greater weight to words that receive more attention. Our investigation aimed to identify which words were assigned higher weights in each article. The findings indicate that while representing a minor percentage, a common characteristic of fake news is the heightened attention to words that influence the credibility of the article. To assess the versatility of the model, we applied the model trained on one dataset to classify other datasets. The results demonstrate a notable decline in accuracy, attributable to the distinctive characteristics of the training data. These observations suggest that common features among fake news, which could be extracted using the fine-tuned BERT model, are limited.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101409"},"PeriodicalIF":6.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540364","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

Powerful graph neural network for node classification of the IoT network 用于物联网网络节点分类的强大图神经网络

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-24 DOI: 10.1016/j.iot.2024.101410

Mohammad Abrar Shakil Sejan , Md Habibur Rahman , Md Abdul Aziz , Rana Tabassum , Jung-In Baik , Hyoung-Kyu Song

{"title":"Powerful graph neural network for node classification of the IoT network","authors":"Mohammad Abrar Shakil Sejan , Md Habibur Rahman , Md Abdul Aziz , Rana Tabassum , Jung-In Baik , Hyoung-Kyu Song","doi":"10.1016/j.iot.2024.101410","DOIUrl":"10.1016/j.iot.2024.101410","url":null,"abstract":"<div><div>Internet of Things (IoT) devices are increasingly used in various applications in our daily lives. The network structure for IoT is heterogeneous and can create a complex architecture depending on the application and geographical structure. To efficiently process the information within this diverse and complex relationship, a robust data structure is needed for network operations. Graph neural network (GNN) technology is emerging as a capable tool for predicting complex data structures, such as graphs. Graphs can be employed to mimic the structure of IoT network and process information from IoT nodes using GNN techniques. In this paper, our goal is to explore the effectiveness of GNN in performing the node classification task for a given IoT network. We have generated three different IoT networks with varying network sizes, number of nodes, and feature sizes. We then test 12 different GNN algorithms to evaluate their performance in IoT node classification. Each method is examined in detail to observe its training behavior, testing behavior, and resilience against noise. In addition, time complexity and generalization ability of each model have also been studied. The experimental results show that some methods exhibit high resilience against noisy data for IoT node classification accuracy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101410"},"PeriodicalIF":6.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572179","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

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications 车联网 (IoV) 边缘计算中基于模糊的任务卸载，适用于延迟敏感型应用

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-24 DOI: 10.1016/j.iot.2024.101392

Zouheir Trabelsi , Muhammad Ali , Tariq Qayyum

{"title":"Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications","authors":"Zouheir Trabelsi , Muhammad Ali , Tariq Qayyum","doi":"10.1016/j.iot.2024.101392","DOIUrl":"10.1016/j.iot.2024.101392","url":null,"abstract":"<div><div>As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101392"},"PeriodicalIF":6.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577837","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

A combination learning framework to uncover cyber attacks in IoT networks 揭示物联网网络攻击的组合学习框架

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-23 DOI: 10.1016/j.iot.2024.101395

Arati Behera , Kshira Sagar Sahoo , Tapas Kumar Mishra , Monowar Bhuyan

{"title":"A combination learning framework to uncover cyber attacks in IoT networks","authors":"Arati Behera , Kshira Sagar Sahoo , Tapas Kumar Mishra , Monowar Bhuyan","doi":"10.1016/j.iot.2024.101395","DOIUrl":"10.1016/j.iot.2024.101395","url":null,"abstract":"<div><div>The Internet of Things (IoT) is rapidly expanding, connecting an increasing number of devices daily. Having diverse and extensive networking and resource-constrained devices creates vulnerabilities to various cyber-attacks. The IoT with the supervision of Software Defined Network (SDN) enhances the network performance through its flexibility and adaptability. Different methods have been employed for detecting security attacks; however, they are often computationally efficient and unsuitable for such resource-constraint environments. Consequently, there is a significant requirement to develop efficient security measures against a range of attacks. Recent advancements in deep learning (DL) models have paved the way for designing effective attack detection methods. In this study, we leverage Genetic Algorithm (GA) with a correlation coefficient as a fitness function for feature selection. Additionally, mutual information (MI) is applied for feature ranking to measure their dependency on the target variable. The selected optimal features were used to train a hybrid DNN model to uncover attacks in IoT networks. The hybrid DNN integrates Convolutional Neural Network, Bi-Gated Recurrent Units (Bi-GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) for training the input data. The performance of our proposed model is evaluated against several other baseline DL models, and an ablation study is provided. Three key datasets InSDN, UNSW-NB15, and CICIoT 2023 datasets, containing various types of attacks, were used to assess the performance of the model. The proposed model demonstrates an impressive accuracy and detection time over the existing model with lower resource consumption.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101395"},"PeriodicalIF":6.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553076","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

Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention 确保受限物联网系统的安全：异常检测和预防的轻量级机器学习方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-21 DOI: 10.1016/j.iot.2024.101398

Zainab Alwaisi , Tanesh Kumar , Erkki Harjula , Simone Soderi

{"title":"Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention","authors":"Zainab Alwaisi , Tanesh Kumar , Erkki Harjula , Simone Soderi","doi":"10.1016/j.iot.2024.101398","DOIUrl":"10.1016/j.iot.2024.101398","url":null,"abstract":"<div><div>With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101398"},"PeriodicalIF":6.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531738","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

Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts 通过物联网合作竞争提高客户满意度：战略见解和影响

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-20 DOI: 10.1016/j.iot.2024.101408

Agostinho da Silva , Antonio J. Marques Cardoso

{"title":"Enhancing customer satisfaction through IIoT-Enabled coopetition: Strategic insights and impacts","authors":"Agostinho da Silva , Antonio J. Marques Cardoso","doi":"10.1016/j.iot.2024.101408","DOIUrl":"10.1016/j.iot.2024.101408","url":null,"abstract":"<div><div>This research investigates the significant role of Industrial Internet of Things (IIoT) to enable Coopetition Network Practices (CNPs) in enhancing the performance of Small and Medium Enterprises (SMEs) within the context of global digital supply chains. Employing a quantitative approach, our study reveals that CNPs contribute to a noTable 51.0 % improvement in factors determining customer satisfaction. This underscores the strategic importance of blending competition with collaboration to refine production processes and align with consumer expectations. Additionally, the research presents a remarkable 69.1 % boost in operational consistency and reports substantial progress in manufacturing flexibility and the value-to-weight ratio, witnessing increases of 125.8 % and 33.2 %, respectively. These improvements are pivotal in optimizing production resources, which in turn, have led to a 29.3 % decrease in customer complaints and a 15.6 % rise in on-time delivery rates. Conversely, a slight decline in the consistency of the value-to-weight ratio was observed, pointing to potential areas for future research. The findings decisively show that CNPs offer concrete advantages by enhancing customer satisfaction determinants and operational efficiency in SMEs. The paper advocates for future studies to directly measure customer satisfaction and to formulate actionable guidelines for the effective implementation of coopetition strategies. This proposed research direction aims to provide solutions to the manufacturing sector's emerging challenges, thereby promoting competitive advantage and growth in the digital era.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101408"},"PeriodicalIF":6.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531782","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

Design of a turbo-based deep semantic autoencoder for marine Internet of Things 为海洋物联网设计基于涡轮的深度语义自动编码器

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-20 DOI: 10.1016/j.iot.2024.101393

Xiaoling Han , Bin Lin , Nan Wu , Ping Wang , Zhenyu Na , Miyuan Zhang

{"title":"Design of a turbo-based deep semantic autoencoder for marine Internet of Things","authors":"Xiaoling Han , Bin Lin , Nan Wu , Ping Wang , Zhenyu Na , Miyuan Zhang","doi":"10.1016/j.iot.2024.101393","DOIUrl":"10.1016/j.iot.2024.101393","url":null,"abstract":"<div><div>With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101393"},"PeriodicalIF":6.0,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572263","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 IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management 支持物联网的脑电图耳机，可为慢性耳鸣评估和症状管理定制音乐

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-19 DOI: 10.1016/j.iot.2024.101411

Nguyen-Ngan-Ha Lam , Chiao-Hsin Lin , Yi-Lu Li , Wei-Siang Ciou , Yi-Chun Du

{"title":"An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management","authors":"Nguyen-Ngan-Ha Lam , Chiao-Hsin Lin , Yi-Lu Li , Wei-Siang Ciou , Yi-Chun Du","doi":"10.1016/j.iot.2024.101411","DOIUrl":"10.1016/j.iot.2024.101411","url":null,"abstract":"<div><div>Chronic tinnitus often affects elderly or hearing-impaired individuals, which can disturb their daily lives by disrupting concentration and limiting communication. Clinically, sound masking using external sounds like white noise (WN) aims to mask tinnitus and relieve secondary symptoms. Even when symptoms are relieved, tinnitus often requires long-term management, and for patients to visit healthcare professionals regularly. Generally, it could make maintaining symptom management challenging due to the time and effort required for consistent follow-ups. EEG is considered as one of the objective marker for assessing tinnitus symptoms. In this study, we designed IoT-enabled EEG sensing (IEES) headphones, an innovative IoT device that provided customized music (CM) and EEG measurement. The headphones employed a pitch-matching (PM) method to create CM tailored to each patient at specific frequencies for tinnitus patients. To collect EEG measurements, the device incorporated OpenBCI electrodes and a sensing chip to monitor brain waves and evaluate the outcomes.. After 30 days of experiment, participants showed significant reductions in both tinnitus handicap inventory (THI) scores and visual analog scale for annoyance (VAS-A) scores. In comparison, tinnitus frequency showed a slight reduction. EEG measurements demonstrated an increase in alpha band activity. In questionnaires, patients reported high satisfaction with their experiences. These findings highlight the potential of the proposed method for chronic tinnitus assessment and symptom management.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101411"},"PeriodicalIF":6.0,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531739","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