Journal on Internet of Things最新文献

An Optimization Scheme for Task Offloading and Resource Allocation in Vehicle Edge Networks 车辆边缘网络中任务卸载与资源分配的优化方案

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2020.011792

Yuxin Xu, Zilong Jin, Xiaorui Zhang, Lejun Zhang

引用次数: 1

Tibetan Multi-Dialect Speech Recognition Using Latent Regression Bayesian Network and End-To-End Mode 基于潜在回归贝叶斯网络和端到端模式的藏语多方言语音识别

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/JIOT.2019.05866

Yue Zhao, Jianjian Yue, Wei Song, Xiaona Xu, Xiali Li, Licheng Wu, Q. Ji

引用次数: 2

Internet of Things for in Home Health Based Monitoring System: Modern Advances, Challenges and Future Directions 物联网家庭健康监测系统:现代进展、挑战和未来方向

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.022256

Omer J. Iqbal, Tayyeba Iftakhar, Saleem Zubair Ahmad

引用次数: 1

Research on the Traveling Characteristics and Comparison of Bike Sharing in College Campus–A Case Study in Hangzhou 高校校园共享单车出行特征及对比研究——以杭州市为例

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2020.010107

Yang Tang, Weiwei Liu, Chennan Zhang, Y. He, Ning Ji, Xinyao Chen

引用次数: 0

Security Attacks on the IoT Network with 5G Wireless Communication 基于5G无线通信的物联网网络安全攻击

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.015900

Ghada Sultan Aljumaie, Ghada Hisham Alzeer, Sultan S. Alshamrani

引用次数: 0

Authentication in Wireless Body Area Network: Taxonomy and Open Challenges 无线体域网络中的身份验证:分类和开放挑战

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2021.018088

Abdullah M. Almuhaideb, Kawther S. Alqudaihi

{"title":"Authentication in Wireless Body Area Network: Taxonomy and Open Challenges","authors":"Abdullah M. Almuhaideb, Kawther S. Alqudaihi","doi":"10.32604/jiot.2021.018088","DOIUrl":"https://doi.org/10.32604/jiot.2021.018088","url":null,"abstract":": Wearable body area network (WBAN) aids the communication between the health providers and patients by supporting health monitoring services. It assists the users to maintain their health status records by collecting the body signals and transmitting them for further processing measurements. However, sensor data are publicly transferred through insecure network that facilitates the attacker malicious acts like performing masquerading attack, man in the middle, and snooping. Several authentication techniques were suggested to levitate the security of the communication channels to preserve the user data from exposure. Moreover, authentication schemes aid plenty of security issues related to user and data privacy, anonymity, repudiation, confidentiality, and integrity, but they lack performance efficiency. On the other hand, it is very hard to find the balance between security and efficiency in most of the authentication schemes, especially for the WBAN platform that consists of memory and processing constraint devices. Therefore, this paper surveys and discusses the latest authentication schemes types, techniques, and system features. Also, it highlights their strengths and weaknesses towards common knowingly attacks and provides a comparison between the popular scheme validation proofs and simulation tools. Thence, this paper draws a path for the new direction of the authentication technologies, the authentication schemes open issues, and the potential future evolution in this area.","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462544","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: 4

The Development of Generalized Public Bicycles in China and Its Role in the Urban Transportation System 通用公共自行车在中国的发展及其在城市交通系统中的作用

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2020.010108

Yang Tang, Weiwei Liu, Y. He, Yuelin Zhang, Fulong Zhang

引用次数: 1

Radiation Cross Calibration Based on GF-1 Side Swing Angle 基于GF-1侧摆角的辐射交叉标定

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/JIOT.2019.05859

Yong Xie, Z. Tao, Shao Wen, J. Qu, Huan Hai, Tian Chuanyang

引用次数: 0

Dynamic Automation of a Moving Vehicle at Real Time Along with Position and Condition Tracking 基于位置和状态跟踪的移动车辆实时动态自动化

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2022.022200

Shruti, Soumen Santra, A. Deyasi

引用次数: 0

Brain MRI Patient Identification Based on Capsule Network 基于胶囊网络的脑MRI患者识别

Journal on Internet of Things Pub Date : 1900-01-01 DOI: 10.32604/jiot.2020.09797

Shuqiao Liu, Junliang Li, Xiaojie Li

{"title":"Brain MRI Patient Identification Based on Capsule Network","authors":"Shuqiao Liu, Junliang Li, Xiaojie Li","doi":"10.32604/jiot.2020.09797","DOIUrl":"https://doi.org/10.32604/jiot.2020.09797","url":null,"abstract":": In the deep lea rning ﬁeld, “Capsule” structur e aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classiﬁcation network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classiﬁcation performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the di ﬀ erence between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not signiﬁcant. It is di fficult to achieve excellent results using a multi-layer CNN. However, CapsNet can be in the case of a shallower network, which can accommodate more useful feature information for identifying brain MRI. In this paper, we designed a shallow CapsNet to identify patients with brain MRI by binary classiﬁcation. Compared with VGG1 6, Resnet34, DenseNet121 and ResNeXt50. Experimental results illustrate that CapsNet is superior to CNN network in its accuracy and F1-score. The indicators were 86.67% and 83.33%, respectively. Furthermore, we show that the capsule network shows excellent performance in brain MRI recognition compared with those popular networks.","PeriodicalId":345256,"journal":{"name":"Journal on Internet of Things","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126917154","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