Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673861
Cong Li, Honghong Yang, Xia Wu, Yumei Zhang
Motor imagery EEG (MI-EEG) is a subjective signal generated by testers, which is collected through brain-computer interface (BCI). With the characteristics of noninvasive, inexpensive, and easily applied to human beings, MI-EEG classification is a popular research area in recent years. Due to the low signal-to-noise ratio and incomplete EEG signals, high accuracy rate classification is still a challenging problem. Most existing works of deep learning only regard EEG signals as chain-like sequences data and use single neural network for classification. To solve the above issues, we propose an improved EEG signals classification method via a hybrid neural network (HNN). In our work, we first use the origin EEG signals without removing noise and any filtering process, to ensure real-time property. Then, the EEG signals are divided into some small segments, and we arrange the data by considering the spatial position of electrodes. Finally, we propose a hybrid neural network by combing CNN, DNN, LSTM network. Experimental results for two challenging EEG signal classification benchmark datasets show that the proposed method has a good classification performance compared with several state-of-the-art EEG signal classification algorithms. After multiple sample testing, the average experiment result is 75.52%, which is 7.32% higher than the latest method.
{"title":"Improving EEG-Based Motor Imagery Classification Using Hybrid Neural Network","authors":"Cong Li, Honghong Yang, Xia Wu, Yumei Zhang","doi":"10.1109/icicn52636.2021.9673861","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673861","url":null,"abstract":"Motor imagery EEG (MI-EEG) is a subjective signal generated by testers, which is collected through brain-computer interface (BCI). With the characteristics of noninvasive, inexpensive, and easily applied to human beings, MI-EEG classification is a popular research area in recent years. Due to the low signal-to-noise ratio and incomplete EEG signals, high accuracy rate classification is still a challenging problem. Most existing works of deep learning only regard EEG signals as chain-like sequences data and use single neural network for classification. To solve the above issues, we propose an improved EEG signals classification method via a hybrid neural network (HNN). In our work, we first use the origin EEG signals without removing noise and any filtering process, to ensure real-time property. Then, the EEG signals are divided into some small segments, and we arrange the data by considering the spatial position of electrodes. Finally, we propose a hybrid neural network by combing CNN, DNN, LSTM network. Experimental results for two challenging EEG signal classification benchmark datasets show that the proposed method has a good classification performance compared with several state-of-the-art EEG signal classification algorithms. After multiple sample testing, the average experiment result is 75.52%, which is 7.32% higher than the latest method.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130606472","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673826
Yifei Zhang, Pingguo Huang, Y. Ishibashi, T. Okuda, K. Psannis
In this paper, we focus on the application of a neural network model to QoS (Quality of Service) control for a remote robot system with force feedback. We have constructed the model to improve the efficiency of the robot position control using force information, which was proposed as QoS control previously. By experiment, we demonstrate that the model is effective.
{"title":"Effect of Neural Network on Robot Position Control Using Force Information","authors":"Yifei Zhang, Pingguo Huang, Y. Ishibashi, T. Okuda, K. Psannis","doi":"10.1109/icicn52636.2021.9673826","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673826","url":null,"abstract":"In this paper, we focus on the application of a neural network model to QoS (Quality of Service) control for a remote robot system with force feedback. We have constructed the model to improve the efficiency of the robot position control using force information, which was proposed as QoS control previously. By experiment, we demonstrate that the model is effective.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124335277","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9674019
Hua Xu, Jincan Xin, Sen Xu, Hua Zhang
The development of industrial automation processes enables new use cases that have stringent requirements for communication systems. The integration and deployment of time-sensitive networks (TSN) and fifth-generation mobile communication system (5G) is introduced to meet the high demands of industrial applications and achieve deterministic connection. Since time synchronization is the basis of both TSN and 5G system (5GS), R17 has enhanced time synchronization for the improved synchronization accuracy requirement. This paper introduces three baseline scenarios, Uu interface time synchronization budget, error terms, and two main propagation delay compensation options in R17.
{"title":"RAN Enhancement to Support Propagation Delay Compensation of TSN","authors":"Hua Xu, Jincan Xin, Sen Xu, Hua Zhang","doi":"10.1109/icicn52636.2021.9674019","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9674019","url":null,"abstract":"The development of industrial automation processes enables new use cases that have stringent requirements for communication systems. The integration and deployment of time-sensitive networks (TSN) and fifth-generation mobile communication system (5G) is introduced to meet the high demands of industrial applications and achieve deterministic connection. Since time synchronization is the basis of both TSN and 5G system (5GS), R17 has enhanced time synchronization for the improved synchronization accuracy requirement. This paper introduces three baseline scenarios, Uu interface time synchronization budget, error terms, and two main propagation delay compensation options in R17.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124356479","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}
In accordance with the application demands and features of Internet of Things, analyzing and research has been done, and then a new design proposal of embedded smart home system is presented. The monitoring system was built based on ARM microprocessor Cortex-A8 and embedded Linux operating system, the smart home control system was compiled by Android through using its various sources. Besides, Android makes the design more reasonable and inter-module coupling less. Furthermore, the supervisory interface is designed based on ARM, which is functional, user-friendly, easier to upgrade and maintain. The system realizes the smart control of home appliances in reality experiment.
{"title":"On the Design of Embedded Smart Home System Based on Internet of Things","authors":"Wei Yang, Qiaojie Jiang, Dongliang Xie, Xiaojun Jing","doi":"10.1109/icicn52636.2021.9673964","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673964","url":null,"abstract":"In accordance with the application demands and features of Internet of Things, analyzing and research has been done, and then a new design proposal of embedded smart home system is presented. The monitoring system was built based on ARM microprocessor Cortex-A8 and embedded Linux operating system, the smart home control system was compiled by Android through using its various sources. Besides, Android makes the design more reasonable and inter-module coupling less. Furthermore, the supervisory interface is designed based on ARM, which is functional, user-friendly, easier to upgrade and maintain. The system realizes the smart control of home appliances in reality experiment.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125761202","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673812
Lian-ri Cong, Chengbin Huang, Chaochen Zhang, Jia Li, B. Liu, P. Yang
With the rapid development of edge computing and the demand for green, safe and efficient transportation system, edge intelligence has been widely used in various traffic scenarios. By collecting images and videos, vehicles can obtain basic data and traffic flow information, which can be used to predict future movement trends. In addition, different traffic participants and their surroundings can be distinguished by image segmentation technology. In this paper, considering the resource limitation and latency constraint on edge vehicles, we proposed an improved vehicle detection algorithm based on tailored YOLOv4(You Only Look Once). To further increase the detection accuracy and speed, we introduce the Efficient Channel Attention (ECA) mechanism and High-Resolution Network (HRNet) into improved YOLOv4. After that, based on collected and detected objects, we proposed an image segmentation algorithm based on the DeepLabv3+ network, in which the MobileNetv2 is taken as the backbone network and the Softpool pooling algorithm is adopted as the pooling method. Experimental results show that compared with other classic methods, our proposed model has a higher mean Average Precision (mAP) for object detection and can improve the accuracy of original YOLOv4 from 83.34% to 87.64%. For image segmentation, our model also outperform other models with the Mean Intersection over Union (mIOU) improved from 72.18% to 74.99%.
随着边缘计算的快速发展和人们对绿色、安全、高效的交通系统的需求，边缘智能在各种交通场景中得到了广泛的应用。车辆通过采集图像和视频，可以获得基础数据和交通流量信息，用于预测未来的运动趋势。此外，利用图像分割技术可以区分不同的交通参与者及其周围环境。本文考虑到边缘车辆的资源限制和时延约束，提出了一种基于定制化YOLOv4(You Only Look Once)的改进车辆检测算法。为了进一步提高检测精度和速度，我们在改进的YOLOv4中引入了高效通道注意(ECA)机制和高分辨率网络(HRNet)。之后，基于采集和检测的目标，我们提出了一种基于DeepLabv3+网络的图像分割算法，该算法以MobileNetv2为骨干网络，采用Softpool池化算法作为池化方法。实验结果表明，与其他经典方法相比，我们提出的模型具有更高的目标检测平均精度(mAP)，可以将原始YOLOv4的精度从83.34%提高到87.64%。在图像分割方面，我们的模型也优于其他模型，mIOU均值从72.18%提高到74.99%。
{"title":"Object Detection and Image Segmentation for Autonomous Vehicles","authors":"Lian-ri Cong, Chengbin Huang, Chaochen Zhang, Jia Li, B. Liu, P. Yang","doi":"10.1109/icicn52636.2021.9673812","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673812","url":null,"abstract":"With the rapid development of edge computing and the demand for green, safe and efficient transportation system, edge intelligence has been widely used in various traffic scenarios. By collecting images and videos, vehicles can obtain basic data and traffic flow information, which can be used to predict future movement trends. In addition, different traffic participants and their surroundings can be distinguished by image segmentation technology. In this paper, considering the resource limitation and latency constraint on edge vehicles, we proposed an improved vehicle detection algorithm based on tailored YOLOv4(You Only Look Once). To further increase the detection accuracy and speed, we introduce the Efficient Channel Attention (ECA) mechanism and High-Resolution Network (HRNet) into improved YOLOv4. After that, based on collected and detected objects, we proposed an image segmentation algorithm based on the DeepLabv3+ network, in which the MobileNetv2 is taken as the backbone network and the Softpool pooling algorithm is adopted as the pooling method. Experimental results show that compared with other classic methods, our proposed model has a higher mean Average Precision (mAP) for object detection and can improve the accuracy of original YOLOv4 from 83.34% to 87.64%. For image segmentation, our model also outperform other models with the Mean Intersection over Union (mIOU) improved from 72.18% to 74.99%.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132122953","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673900
Sotirios Kontogiannis, Anestis Kastellos, G. Kokkonis, Theodosios Gkamas, C. Pikridas
This paper presents the architecture of a distributed system based on Bluetooth detectors for locating vehicles and people trapped in a motorway tunnel accident. The distributed architecture of the proposed system includes an automated dynamic way of adding or removing detectors from the system, a non-relational database for storing sensory measurements and an-easy-to-use graphical interface for displaying real-time or close to real-time detection information. The installation of the proposed system detectors is installed on the escape exits inside tunnels. The proposed system has been experimentally placed to both directions of a tunnel at the EGNATIA motorway in Greece and its functionality has been validated. Then, with the use of existing tunnel TMS inductive loop detectors, the accuracy of the detector measurements has been evaluated.
{"title":"Proposed Distributed System Architecture and Preliminary Measurements for the Detection of Trapped Individuals Inside Motorway Tunnels","authors":"Sotirios Kontogiannis, Anestis Kastellos, G. Kokkonis, Theodosios Gkamas, C. Pikridas","doi":"10.1109/icicn52636.2021.9673900","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673900","url":null,"abstract":"This paper presents the architecture of a distributed system based on Bluetooth detectors for locating vehicles and people trapped in a motorway tunnel accident. The distributed architecture of the proposed system includes an automated dynamic way of adding or removing detectors from the system, a non-relational database for storing sensory measurements and an-easy-to-use graphical interface for displaying real-time or close to real-time detection information. The installation of the proposed system detectors is installed on the escape exits inside tunnels. The proposed system has been experimentally placed to both directions of a tunnel at the EGNATIA motorway in Greece and its functionality has been validated. Then, with the use of existing tunnel TMS inductive loop detectors, the accuracy of the detector measurements has been evaluated.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130746978","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673953
Chunfeng Wang, Naijin Liu
Although unlicensed bands are used more and more, but licensed bands are not used enough. This brings challenges to the concepts of open spectrum and dynamic spectrum access. Dynamic spectrum access is an effective method of spectrum sharing, which can realize the effective utilization of spectrum resources. Cognitive radio technology can achieve effective utilization of wireless bandwidth. In this paper, the application of software radio technology in satellite network system is discussed. The problem of spectrum mobility and handover in cognitive satellite network system is studied. An efficient spectrum mobility and handover method for cognitive satellite network is proposed. The cross-layer handover protocol with minimum expected transmission time in cognitive satellite network system is studied, instead of maximum idle time of spectrum hole. performance analysis and simulation are carried out. The proposed spectrum mobility and handover scheme can reduce the expected transmission time and probability of spectrum mobility, and improve the overall performance of the system.
{"title":"An Efficient Spectrum Mobility and Handover Method for Cognitive Satellite Network","authors":"Chunfeng Wang, Naijin Liu","doi":"10.1109/icicn52636.2021.9673953","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673953","url":null,"abstract":"Although unlicensed bands are used more and more, but licensed bands are not used enough. This brings challenges to the concepts of open spectrum and dynamic spectrum access. Dynamic spectrum access is an effective method of spectrum sharing, which can realize the effective utilization of spectrum resources. Cognitive radio technology can achieve effective utilization of wireless bandwidth. In this paper, the application of software radio technology in satellite network system is discussed. The problem of spectrum mobility and handover in cognitive satellite network system is studied. An efficient spectrum mobility and handover method for cognitive satellite network is proposed. The cross-layer handover protocol with minimum expected transmission time in cognitive satellite network system is studied, instead of maximum idle time of spectrum hole. performance analysis and simulation are carried out. The proposed spectrum mobility and handover scheme can reduce the expected transmission time and probability of spectrum mobility, and improve the overall performance of the system.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955130","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673990
Xiaojun Yu, Zeming Fan, M. Jamil, Muhammad Zulkifal Aziz, Yiyan Hou, Haopeng Li, Jialin Lv
Epileptic electroencephalogram (EEG) is one of the most adopted schemes to localize epileptiform discharge via brain signal recordings during seizure, and neurologists typically derive conjectures via ocular assessment. However, such a scheme is time-consuming with immense dependency on scrutinizer’s expertise, and thus, automated models are deemed to be the most feasible solutions to this predicament. This paper studies, for the first time, on the impact of transacting multiple mother wavelets (TMMW) on a benchmark signal decomposition algorithm known as Continuous Wavelet Transform (CWT). 1D signals are transformed into 2D scalograms discretely for three mother wavelets, namely ‘amor’, ‘bump’, and ‘mores’ first, and then, the such images are categorized with a pre-trained alexnet for classifications. The configured approach finally capitalizes on the repercussions of directing variables, which are adam, rmsprop, sgdm, and four learning rates, i.e., $10^{-3}, 10^{-4}, 10^{-5}$, and $10^{-6}$. Simulations are trialed on the renowned Bern-Barcelona dataset for verification. Results imply that deep learning classifier yields better results on morse based images, while the highest segregation is achieved when alexnet is operated on adam at $10^{-5}$, where classification mark up secures 90.4% with parametric values of 87.6%, 84.3%, and 85.5% for sensitivity, specificity, and specificity f1-score, respectively. This study offers an expanded understanding of the feasibility of mother wavelets on the skeleton of CWT for the classification of epileptic seizures via Convolutional Neural Network (CNN) classifier.
{"title":"Transacting Multiple Mother Wavelets in Continuous Wavelet Transform for Epilepsy EEG Classification via CNN","authors":"Xiaojun Yu, Zeming Fan, M. Jamil, Muhammad Zulkifal Aziz, Yiyan Hou, Haopeng Li, Jialin Lv","doi":"10.1109/icicn52636.2021.9673990","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673990","url":null,"abstract":"Epileptic electroencephalogram (EEG) is one of the most adopted schemes to localize epileptiform discharge via brain signal recordings during seizure, and neurologists typically derive conjectures via ocular assessment. However, such a scheme is time-consuming with immense dependency on scrutinizer’s expertise, and thus, automated models are deemed to be the most feasible solutions to this predicament. This paper studies, for the first time, on the impact of transacting multiple mother wavelets (TMMW) on a benchmark signal decomposition algorithm known as Continuous Wavelet Transform (CWT). 1D signals are transformed into 2D scalograms discretely for three mother wavelets, namely ‘amor’, ‘bump’, and ‘mores’ first, and then, the such images are categorized with a pre-trained alexnet for classifications. The configured approach finally capitalizes on the repercussions of directing variables, which are adam, rmsprop, sgdm, and four learning rates, i.e., $10^{-3}, 10^{-4}, 10^{-5}$, and $10^{-6}$. Simulations are trialed on the renowned Bern-Barcelona dataset for verification. Results imply that deep learning classifier yields better results on morse based images, while the highest segregation is achieved when alexnet is operated on adam at $10^{-5}$, where classification mark up secures 90.4% with parametric values of 87.6%, 84.3%, and 85.5% for sensitivity, specificity, and specificity f1-score, respectively. This study offers an expanded understanding of the feasibility of mother wavelets on the skeleton of CWT for the classification of epileptic seizures via Convolutional Neural Network (CNN) classifier.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201274","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673933
Lei Guowei, Liao Wenliang
Continuous phase modulation (CPM) has many desirable features such as power efficiency and spectrum efficiency. This paper considers continuous phase modulation in massive MIMO system. Especially in cellular wireless system, we can obtain more accurate channel state information in uplink time division duplexing (TDD) transmission via employing different CPM modulation index h. On the basis of that scheme, the method of linear precoding can achieve better performance in the reverse link. Finally, simulated results have verified the proposed system.
{"title":"Performance Analysis of CPM in Multi-Cell Massive MIMO System","authors":"Lei Guowei, Liao Wenliang","doi":"10.1109/icicn52636.2021.9673933","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673933","url":null,"abstract":"Continuous phase modulation (CPM) has many desirable features such as power efficiency and spectrum efficiency. This paper considers continuous phase modulation in massive MIMO system. Especially in cellular wireless system, we can obtain more accurate channel state information in uplink time division duplexing (TDD) transmission via employing different CPM modulation index h. On the basis of that scheme, the method of linear precoding can achieve better performance in the reverse link. Finally, simulated results have verified the proposed system.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132699435","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}
Pub Date : 2021-11-25DOI: 10.1109/icicn52636.2021.9673959
Ru Xiang, Sha Ma, Xinjie Liu
Group public key encryption with equality test supports group granularity authorization, which allows a tester to perform equality test only on ciphertexts of group users. If two ciphertexts are encrypted by users from two different groups, they cannot be performed the equality test. Indeed, most of the existing works either only support users to join the group but do not support group members to leave the group, or are inefficient for all the ciphertexts need to be re-encrypted after each revocation operation. In this paper, we propose a group public key encryption with equality test for dynamic membership. Compared with the existing works, (1) our scheme supports efficient member revocation function. Once a group member has malicious behavior, it can be withdrawn from the group, which improves the security of the scheme, (2) our scheme supports the verification of ciphertext encryptors, that is, it verifies whether the ciphertext encryptors have been revoked and come from the same group before an equality test, which improves the efficiency of our scheme. Besides, we prove our scheme can achieve OW-CCA security against the Type-I adversary and IND-CCA security against the Type-II adversary in the random oracle model.
{"title":"Group Public Key Encryption With Equality Test for Dynamic Membership","authors":"Ru Xiang, Sha Ma, Xinjie Liu","doi":"10.1109/icicn52636.2021.9673959","DOIUrl":"https://doi.org/10.1109/icicn52636.2021.9673959","url":null,"abstract":"Group public key encryption with equality test supports group granularity authorization, which allows a tester to perform equality test only on ciphertexts of group users. If two ciphertexts are encrypted by users from two different groups, they cannot be performed the equality test. Indeed, most of the existing works either only support users to join the group but do not support group members to leave the group, or are inefficient for all the ciphertexts need to be re-encrypted after each revocation operation. In this paper, we propose a group public key encryption with equality test for dynamic membership. Compared with the existing works, (1) our scheme supports efficient member revocation function. Once a group member has malicious behavior, it can be withdrawn from the group, which improves the security of the scheme, (2) our scheme supports the verification of ciphertext encryptors, that is, it verifies whether the ciphertext encryptors have been revoked and come from the same group before an equality test, which improves the efficiency of our scheme. Besides, we prove our scheme can achieve OW-CCA security against the Type-I adversary and IND-CCA security against the Type-II adversary in the random oracle model.","PeriodicalId":231379,"journal":{"name":"2021 IEEE 9th International Conference on Information, Communication and Networks (ICICN)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151969","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}
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