Article
Automation & Control Systems
Anas Bushnag
Summary: This study proposes a portable electrocardiogram monitoring system that connects patients and doctors through an IoT cloud server, enabling remote diagnosis and monitoring of heart diseases. The system provides reliable analysis and alerts to reduce hospital visits.
INTELLIGENT AUTOMATION AND SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Wenhan Liu, Qianxi Guo, Xinwei Gao, Sheng Chang, Hao Wang, Jin He, Qijun Huang
Summary: This article proposes a new unsupervised feature learning method for processing unlabeled 12-lead electrocardiogram signals. The method takes into account the characteristics of 12-lead ECGs and utilizes lead separation and combination to learn feature representations. Experimental results demonstrate that the method achieves good accuracy in myocardial infarction and atrial fibrillation detection.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Materials Science, Multidisciplinary
Bani Gandhi, N. S. Raghava
Summary: Highly conductive electrodes were fabricated using MWCNT-COOH and PDMS, with a two-step dispersion method used to create thirteen electrodes with varying CNT concentrations and a low percolation threshold. These electrodes were able to be monitored in real-time and remotely through an IoT system.
Article
Computer Science, Information Systems
Muhammad Irfan, Peng Shun, Barkoum Betra Felix, Noman Mustafa, Saadullah Farooq Abbasi, Abdelwahed Nahli, Abdulhamit Subasi, Tomi Westerlund, Wei Chen
Summary: In this research, an IoT-based noncontact ECG measurement system is proposed using fabric-based flexible electrodes. The system accurately measures ECG signals, and an adaptive coding algorithm reduces the amount of transmitted data. The results are validated with ground truth polysomnography data, showing accurate measurements and minimal errors.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Federico Montori, Kewen Liao, Matteo De Giosa, Prem Prakash Jayaraman, Luciano Bononi, Timos Sellis, Dimitrios Georgakopoulos
Summary: Public Internet of Things (IoT) platforms, such as Thingspeak, have increased the availability of open IoT data and facilitated the development of novel IoT applications. However, the heterogeneous and lacking metadata description of open IoT data hinders its integration and efficient use. This article proposes a metadata-assisted cascading ensemble classification framework (MACE) that can automatically annotate IoT data and improve classification accuracy by 10%.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Gawsalyan Sivapalan, Koushik Kumar Nundy, Alex James, Barry Cardiff, Deepu John
Summary: This article proposes an explainable rule-mining strategy for prioritizing abnormal class detection in ECG data using an artificial neural network and rule-based system. The proposed model achieves high accuracy and sensitivity in detecting abnormal heartbeats through a comprehensive offline rule-mining process. It is suitable for healthcare applications due to its explainability, lower complexity, and real-time flexibility when deployed in IoT-enabled wearable edge sensors.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Multidisciplinary Sciences
Jiewei Lai, Huixin Tan, Jinliang Wang, Lei Ji, Jun Guo, Baoshi Han, Yajun Shi, Qianjin Feng, Wei Yang
Summary: Cardiovascular disease is a global public health problem, and intelligent diagnostic approaches are important in ECG analysis. Convenient wearable ECG devices can detect transient arrhythmias and enable intervention during continuous monitoring. The researchers collected a large dataset of wearable 12-lead ECGs and developed a model that can classify 60 ECG diagnostic terms using self-supervised learning.
NATURE COMMUNICATIONS
(2023)
Article
Computer Science, Information Systems
Berken Utku Demirel, Islam Abdelsalam Bayoumy, Mohammad Abdullah Al Faruque
Summary: The article presents a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices, utilizing three different layers for detection and classification. The methodology achieves high accuracy and energy efficiency compared to state-of-the-art works.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Multidisciplinary Sciences
Yasir Ali, Habib Ullah Khan
Summary: The supply chain management of COVID-19 vaccine is a complex task, and IoT technology is a suitable solution. This study proposes a decision making model to select the right IoT platform for the logistics and transportation process of COVID-19 vaccine. The model is validated and tested through surveys and shows high accuracy and reliability.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Eunchan Kim, Jaehyuk Kim, Juyoung Park, Haneul Ko, Yeunwoong Kyung
Summary: Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. This paper proposes a tiny machine learning (TinyML)-based classification (TinyCES) to reduce memory and network resource usages for classification. The performance results show that TinyCES has great potential to be a lightweight and resource-efficient ECG monitoring system, with an approximately 97% detection ratio.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Engineering, Biomedical
Abhishek Kumar, Swarn Avinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, Vicente Garcia-Diaz
Summary: This study proposes an automatic detection and classification method for arrhythmia using an optimized deep learning classifier. By collecting ECG signals using IoT nodes and establishing feature vectors through Coy-GWO-based Deep CNN classifier, anomalies in the ECG signal can be detected, achieving a classification accuracy of 95%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Telecommunications
Samayveer Singh, Mohit Kumar, Om Prakash Verma, Rajeev Kumar, Sukhpal Singh Gill
Summary: Industrial Internet of Things (IIoT) is an effective way to track products and monitor their real-time conditions during production, packaging, storage, and shipments. However, most supply chain management systems currently face issues such as inefficient tracking, delays, lost identification, and uneconomical storage. To address these problems, a secure and sustainable smart supply chain system using IoT and sensor networks is proposed. It provides real-time shipment tracking, storage, delay, and lost identification. The proposed method improves stability and network lifespan compared to existing methods.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2023)
Article
Multidisciplinary Sciences
Marius Reto Bigler, Christian Seiler
Summary: In this study, pre-trained CNNs were used to detect myocardial ischemia with similar accuracy to manual icECG ST-segment shift measurements, showing promising results for using deep learning methods in the diagnosis of ischemic heart disease. The CNNs focused on ST-segment and T-wave morphology for ischemia detection, providing insightful information for future research on ECG-based diagnostic tools.
Article
Engineering, Electrical & Electronic
Bardia Baraeinejad, Masood Fallah Shayan, Amir Reza Vazifeh, Diba Rashidi, Mohammad Saberi Hamedani, Hamed Tavolinejad, Pouya Gorji, Parsa Razmara, Kiarash Vaziri, Daryoosh Vashaee, Mohammad Fakharzadeh
Summary: This article reports the development of a new smart electrocardiogram (ECG) monitoring system with AI-assisted arrhythmia detection. The system includes hardware, firmware, an IoT-based web service, and an Android application for data streaming. It achieves high accuracy in arrhythmia detection using innovative algorithms.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Electrical & Electronic
Saifur Rahman, Chandan Karmakar, John Yearwood, Marimuthu Palaniswami
Summary: This study proposes a tunable ECG noise localization system to detect noisy ECG segments in real-time at the IoT-enabled gateway, aiming to improve communication quality and clinical decision-making. Evaluation using publicly available and real-time ECG datasets confirms the effectiveness of the system in reducing data drop rate and increasing R-Peak detection.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Theory & Methods
Kuanishbay Sadatdiynov, Laizhong Cui, Lei Zhang, Joshua Zhexue Huang, Neal N. Xiong, Chengwen Luo
Summary: This paper proposes an intelligent two-stage computation offloading scheme to handle the large number of smart mobile devices in the IoE system. In the first stage, tasks are categorized and early offloading decisions are made based on offloading preferences. In the second stage, a multi-objective optimization problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II) for the remaining tasks. The simulation results show a 10% performance improvement in terms of latency and energy consumption compared to existing methods.
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Jing Wang, Wei Ding, Man He, Jinbin Hu, Neal Xiong
Summary: CRPS is a coding-based random packet spraying scheme that reduces tail latency caused by retransmission and improves the performance of cloud computing applications through coding and parallel transmission.
JOURNAL OF SYSTEMS ARCHITECTURE
(2023)
Article
Computer Science, Information Systems
Le Sun, Jin Wu
Summary: With the development of Internet of Medical Things, healthcare sensor data (HSD) faces security problems and automatic classification of HSD is important for patient privacy protection. This paper proposes SCALT, a scalable and transferable classification system based on federated learning, which achieves high accuracy on different physiological signal datasets.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Chemistry, Multidisciplinary
Chunfeng Lv, Jianping Zhu, Naixue Xiong, Zhengsu Tao
Summary: This paper proposes an improved multitarget tracking method based on a PMBM filter with adaptive detection probability and adaptive newborn distribution to address the problems of unknown detection probability, random target newborn distribution, and high energy consumption in limited computational and processing capacity in sensor networks. The proposed method introduces the gamma distribution to represent the augmented state of unknown and changing target detection probability. The intensity of newborn targets is adaptively derived from the inverse gamma distribution based on this augmented state. The effectiveness of this IGGM-PMBM method is verified through comprehensive experiments, and comparisons with other multitarget tracking filters demonstrate significant improvements in tracking behaviors, especially in reducing tracking energy consumption and enhancing tracking accuracy.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Xiaoheng Deng, Jian Yin, Peiyuan Guan, Neal N. Xiong, Lan Zhang, Shahid Mumtaz
Summary: The development of Industrial Internet of Things (IIoT) and Industry 4.0 has transformed the traditional manufacturing industry. With the mobile-edge computing (MEC) system, computation-intensive tasks can be offloaded from resource-constrained IIoT devices to nearby MEC servers, resulting in lower delay and energy consumption for better Quality of Service (QoS).
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Lizhi Xiong, Xiao Han, Xinwei Zhong, Ching-Nung Yang, Neal N. Xiong
Summary: In the industrial Internet of Things (IoT), a secure and reliable secret image-sharing system based on extended Hamming codes (RSIS) is proposed to address the unreliability and data loss caused by tampering with IoT data. The RSIS scheme uses a distributed IoT architecture and stego-images distributed among edge servers to hide the shared secret image and identify tampered places. Theoretical analysis and experiments show that the proposed scheme achieves high authentication capability and accurate error detection and correction, making it effective and practical.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Multidisciplinary Sciences
Xin Liu, Yang Xu, Dan Luo, Gang Xu, Neal Xiong, Xiu-Bo Chen
Summary: This paper studies the similarity problem of geometric graphics and proposes a graphic similarity security decision protocol, which has wide application value in various fields.
SCIENTIFIC REPORTS
(2023)
Editorial Material
Physics, Mathematical
Bin Wang, Naixue Xiong, Fengming Xin
ADVANCES IN MATHEMATICAL PHYSICS
(2023)
Article
Computer Science, Information Systems
Xuezheng Yang, Zhiwen Zeng, Anfeng Liu, Neal N. Xiong, Tian Wang, Shaobo Zhang
Summary: In this paper, a decentralized trust inference approach is proposed to improve the data collection quality for mobile crowd sensing. The approach includes trust evaluation and data filling components, which assess the trust level of workers and fill missing data using Bayesian probabilistic matrix factorization. Furthermore, a worker recruitment method based on trust prioritization and bid ratio is proposed to preferentially select reliable and low-bid workers, thereby improving data quality and reducing costs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Fuhu Wu, Jian Sun, Shun Zhang, Neal Xiong, Hong Zhong
Summary: This paper proposes an efficient reversible data hiding scheme through a double-peak two-layer embedding and prediction error expansion. By utilizing the higher significant bit (HSB) plane and double prediction error peaks, the redundancy space of images can be fully utilized. Moreover, the size of the auxiliary information is reduced through pre-processing. Experimental results demonstrate that this scheme performs better in high capacity embedding scenarios and achieves a 83% higher embedding rate on real-world datasets, such as BOSSbase, BOWS2, and UCID, compared to state-of-the-art approaches.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Guoxiang Tong, Yueyang Li, Haoyu Zhang, Naixue Xiong
Summary: This paper proposes a dynamic CNN-GRU-Attention (CGA) model based on fine-grained channel state information (CSI) in Wi-Fi for gesture recognition system. The influence of gestures on CSI's amplitude and phase difference is studied, and data processing methods are used to extract valid data for the model. Experimental results show that the system has high accuracy.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yuntian Zheng, Zeyuan Li, Zhiwen Zeng, Shaobo Zhang, Neal N. Xiong, Anfeng Liu
Summary: This article proposes a Content-based Intelligent Trust Evaluation (CITE) scheme for large-scale sensor-cloud systems, which employs unmanned mobile electric vehicles and a delicate comparison algorithm to enhance the trustworthiness of data collection and evaluation.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Ziqing Xia, Zhangyang Gao, Anfeng Liu, Neal N. Xiong
Summary: In this paper, an asymmetric quorum-based neighbor discovery (AQND) protocol is proposed to reduce delay, improve energy utilization and lifetime, and outperform previous strategies in main performance indicators.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiaohuan Liu, Anfeng Liu, Shaobo Zhang, Tian Wang, Neal N. Xiong
Summary: This paper proposes a delay differentiated services routing (DDSR) scheme to reduce the deployment costs for wireless sensor networks (WSNs) with wake-up radio (WuR) functionality, while meeting the delay requirement of forwarding urgent data and maintaining a long lifetime.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Qinghua Gu, Yan Wang, Peipei Wang, Xuexian Li, Lu Chen, Neal N. Xiong, Di Liu
Summary: This paper proposes a new ensemble clustering method that combines the influence of cluster level and the base clustering level in a unified framework. The method inserts a global weighting strategy into a local ensemble cluster learning framework, improving the robustness and stability of clustering.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)