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Int. J. Distributed Syst. Technol.最新文献

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Enhancing Network Lifetime of Duty Cycle-Based WSN With Mobile Sink Using Ambient Energy Harvesting 利用环境能量收集提高占空比无线传感器网络的网络寿命
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317413
Dalila Boulerial, Kechar Bouabdellah, Ali Benzerbadj
{"title":"Enhancing Network Lifetime of Duty Cycle-Based WSN With Mobile Sink Using Ambient Energy Harvesting","authors":"Dalila Boulerial, Kechar Bouabdellah, Ali Benzerbadj","doi":"10.4018/ijdst.317413","DOIUrl":"https://doi.org/10.4018/ijdst.317413","url":null,"abstract":"In order to guarantee a successful data collection process in wireless sensor networks with mobile sinks (WSN-MS), two primary objectives must be reached: 1) enabling the mobile sink to retrieve the maximum amount of data and 2) making sure that the network operates as long as possible. The first problem has been solved previously by proposing an innovative solution HXMAC. To address the second problem, on which this paper focuses, ambient energy harvesting is used to continuously supply power to each sensor node. Thus, this paper's main contribution is to propose EH-HXMAC (HXMAC with energy harvesting), which is based on all these improvements: seamless handover, duty cycling optimization, and mainly energy harvesting capability. EH-HXMAC has been evaluated using Cooja Contiki simulator. Obtained results based on the evaluation of the proposal EH-HXMAC clearly show its suitability as a good solution to promote unlimited lifetime for WSN-MS.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129530629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Smart Sports Outward Bound Training Assistant System Based on WSNs 基于无线传感器网络的智能体育拓展训练辅助系统
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317939
Jiali Zang
{"title":"Smart Sports Outward Bound Training Assistant System Based on WSNs","authors":"Jiali Zang","doi":"10.4018/ijdst.317939","DOIUrl":"https://doi.org/10.4018/ijdst.317939","url":null,"abstract":"The outward-bound training has been a popular manner to exercise in daily life. However, there lacks an intelligent assistant system to monitor the real-time status of users to avoid accidents during training. In order to fill this gap, this paper established an intelligent system to monitor fatigue status during outward-bound training by using surface electromyography (sEMG) signals. The system consists of three parts: a wearable device, edge node, and cloud server. First, the wearable device collects sEMG signals. Second, the edge node processes the collected sEMG signals and sends the sEMG signal features to the cloud server. Finally, the cloud server returns the results to edge node according to a stored classification model that learnt from massive historical sEMG signals. The experimental results show the effectiveness of the proposed system.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124782082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors 基于多源传感器的运动疲劳监测与分析
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317941
Jiya Wang, Huan Meng
{"title":"Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors","authors":"Jiya Wang, Huan Meng","doi":"10.4018/ijdst.317941","DOIUrl":"https://doi.org/10.4018/ijdst.317941","url":null,"abstract":"During the process of daily training or competition, athletes may suffer the situation that the load exceeds the body's bearing capacity, which makes the body's physiological function temporarily decline. It is one of the characteristics of sports fatigue. Continuous sports fatigue may incur permanent damage to the athletes if they cannot timely get enough rest to recover. In order to solve this issue and improve the quality of athlete's daily training, this paper establish a fatigue monitoring system by using multi-source sensors. First, the sEMG signals of athlete are collected by multi-source sensors which are installed in a wearable device. Second, the collected sEMG signals are segmented by using fixed window to be converted as Mel-frequency cepstral coefficients (MFCCs). Third, the MFCC features are used learn a Gaussian processing model which is used to monitor future muscle fatigue status. The experiments show that the proposed system can recognize more than 90% muscle fatigue states.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126162146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Kinect Body Sensor Technology-Based Quantitative Assessment Method for Basketball Teaching 基于Kinect体感技术的篮球教学定量评估方法
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317935
Youyang Wang
{"title":"Kinect Body Sensor Technology-Based Quantitative Assessment Method for Basketball Teaching","authors":"Youyang Wang","doi":"10.4018/ijdst.317935","DOIUrl":"https://doi.org/10.4018/ijdst.317935","url":null,"abstract":"Emphasizing the process and neglecting the end is the core idea of the research and implementation of college physical education learning and assessment, while the performance is the main form of evaluation results. This paper takes the quantitative assessment of basketball teaching as an example and proposes a new Kinect body sensor technology-based quantitative assessment method for basketball teaching. Specifically, for basketball technology recognition and assessment tasks, the Kinect body sensor is first used to collect volunteer's 3D skeleton motion data, then feeding the collected skeleton sequence to the vision transformer network to model the long-distance dependency. And based on this, the skeleton motion recognition network and skeleton motion assessment network are developed. The experimental results show that the proposed networks can well recognize and quantitatively assess the standard and non-standard basketball skill motions.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125233505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Dynamic Interoperability Model for an Emergent Middleware Framework 一个紧急中间件框架的动态互操作性模型
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317420
Vatsala Nundloll, G. Blair
{"title":"A Dynamic Interoperability Model for an Emergent Middleware Framework","authors":"Vatsala Nundloll, G. Blair","doi":"10.4018/ijdst.317420","DOIUrl":"https://doi.org/10.4018/ijdst.317420","url":null,"abstract":"Standard middleware platforms are unable to cope with extreme heterogeneity and dynamicity of distributed systems. With new trends in mobile/pervasive applications, distributed systems are required to connect to one another at run time, implying that heterogeneities arising in systems need to be resolved on the fly. This ability of a system to interact with a different system is known as interoperability. More advanced solutions, which exceed the state-of-the-art in middleware, are required to handle interoperability on the fly. This paper investigates the challenges of enabling dynamic interoperability for the domain of vehicular ad-hoc networks (VANETs). The paper uses semantic web technologies to help devise an emergent middleware to enable different VANETs to interact with each other at runtime. An ontology-based framework coupled with an experimental evaluation of the framework is presented. The need for linguistic techniques in assisting ontologies is also emphasized in the framework.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114495276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation 基于可穿戴设备的智能巡检系统设计与实现
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317938
Chengming Jin, Donghui Tong
{"title":"Wearable Device-Based Intelligent Patrol Inspection System Design and Implementation","authors":"Chengming Jin, Donghui Tong","doi":"10.4018/ijdst.317938","DOIUrl":"https://doi.org/10.4018/ijdst.317938","url":null,"abstract":"The traditional on-site operation of power stations includes inspection and maintenance. However, it heavily relies on experience for maintenance. Most on-site operation and maintenance data are text records. On the one hand, the data processing is tedious for experience to affect the safe on-site operation. On the other hand, we usually cannot give full consideration to the value of maintenance experience, so that the corresponding efficiency is very low. Therefore, this paper proposes a wearable device based remote and intelligent patrol inspection system that uses the cloud video transmission mode of both public and private clouds to realize the video connection between the power stations and the remote diagnosis center and uses the wearable devices for real experience. In this way, the authors can simulate real operation guidance and safety supervision, etc. so as to realize the remote management patrol operations, improve the fault detection efficiency, and improve equipment reliability.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127333763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks 基于机器学习和无线传感器网络的生理大数据挖掘
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317942
Qianlin Tan, Xinyou Xu, Hongjia Liang
{"title":"Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks","authors":"Qianlin Tan, Xinyou Xu, Hongjia Liang","doi":"10.4018/ijdst.317942","DOIUrl":"https://doi.org/10.4018/ijdst.317942","url":null,"abstract":"With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121569090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analysis on the Steps of Physical Education Teaching Based on Deep Learning 基于深度学习的体育教学步骤分析
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317937
Ai-hu Dong
{"title":"Analysis on the Steps of Physical Education Teaching Based on Deep Learning","authors":"Ai-hu Dong","doi":"10.4018/ijdst.317937","DOIUrl":"https://doi.org/10.4018/ijdst.317937","url":null,"abstract":"The rapid progress of the internet of things and artificial intelligence has brought new opportunities for the construction and development of intelligent sports. This paper designs an analysis and evaluation system of physical education teaching steps based on deep learning technology. The intelligent wearable devices are used to conduct real-time dynamic monitoring of students' exercise steps and heart rate in class so as to build a sports teaching activity data set. The authors analyze the time step sequence based on transformer deep model to realize the estimation of motion effect. In addition, they propose a hierarchical fusion model based on transformer, which makes full use of the steps and heart rate information to predict the abnormal situation in physical education. The experimental results show the effectiveness of the system.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An Effective Volleyball Trajectory Estimation and Analysis Method With Embedded Graph Convolution 一种有效的基于嵌入图卷积的排球轨迹估计与分析方法
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317936
G. Huang
{"title":"An Effective Volleyball Trajectory Estimation and Analysis Method With Embedded Graph Convolution","authors":"G. Huang","doi":"10.4018/ijdst.317936","DOIUrl":"https://doi.org/10.4018/ijdst.317936","url":null,"abstract":"Volleyball trajectory prediction and analysis based on deep learning has become a hot topic in sports video research. However, due to a large amount of calculation in video processing and the fast speed of volleyball movement with the target scale changing rapidly, these challenges lead to low performance. To this end, this paper proposes an effectively variant YOLOv4 framework to predict and analyze the volleyball trajectory based on video sequences. In the proposed framework, the authors adopt the pre-trained YOLOv4 to select some proposal regions with a high confidence score. Then, the authors embed graph convolution to effectively aggregate deep features. Moreover, to improve the detection and localization capacity of small targets, they introduce a new loss function by modeling the target area with Gaussian distribution. The experimental results show that the proposed framework can effectively prompt the performance of volleyball detection.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130456224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Supervised Learning-Based Framework for Predicting COVID-19 in Patients 基于监督学习的患者COVID-19预测框架
Int. J. Distributed Syst. Technol. Pub Date : 2023-02-10 DOI: 10.4018/ijdst.317412
Ankit Songara, Pankaj Dhiman, Vipul Sharma, K. Kumar
{"title":"A Supervised Learning-Based Framework for Predicting COVID-19 in Patients","authors":"Ankit Songara, Pankaj Dhiman, Vipul Sharma, K. Kumar","doi":"10.4018/ijdst.317412","DOIUrl":"https://doi.org/10.4018/ijdst.317412","url":null,"abstract":"The integration of ML and loT can provide insightful details for critical decision making, automated responses, etc. Predicting future trends and detecting anomalies are some of the areas where loT and ML are being used at a rapid rate. Machine learning can help decode the hidden patterns in IoT data. It may complement or replace manual processes in critical areas with automated systems that use statistically derived behavior. In healthcare, wearable sensors used for tracking patient activity have been continuously producing a staggering amount of data. This paper proposes an IoT-based scalable architecture for detecting COVID-19-positive patients and storing and processing such massive amount of data on the cloud. The proposed architecture also employs machine learning algorithms for correct classification of patients. The proposed architecture employs gradient boosting classifier method for early detection of COVID-19 in the patient's body. In order to make the architecture scalable and faster in terms of computational power, the architecture employs cloud computing for data storage.","PeriodicalId":118536,"journal":{"name":"Int. J. Distributed Syst. Technol.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116344967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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