Pub Date : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345007
Shan Liu, Yue Tian, Jianping Chai
The occurrence of emergencies and hot events often becomes the focus of attention in a short period of time, which brings massive Internet public opinions. How to correctly understand the mechanism of public opinion evolution is of great significance for ensuring objective public opinion orientation. In this paper, we build an opinion evolution model of heterogeneous individuals, combined with the interaction process of user opinions in the real network. Through simulation experiments, we discuss the impact of various parameters and different cases. The results show that compared with the same trust threshold, it is more difficult for the whole group to reach consensus when the trust threshold is different; the acceptance of opinion can have a significant effect on the number of opinion clusters and the convergence time; heterogeneity in individuals will promote better aggregation of group opinions. This model can explain more conscious opinion evolution in real life and has a significance that effectively guides public opinion.
{"title":"Group Opinion Evolution Model and Analysis Based on Heterogeneous Individuals","authors":"Shan Liu, Yue Tian, Jianping Chai","doi":"10.1109/ICCC51575.2020.9345007","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345007","url":null,"abstract":"The occurrence of emergencies and hot events often becomes the focus of attention in a short period of time, which brings massive Internet public opinions. How to correctly understand the mechanism of public opinion evolution is of great significance for ensuring objective public opinion orientation. In this paper, we build an opinion evolution model of heterogeneous individuals, combined with the interaction process of user opinions in the real network. Through simulation experiments, we discuss the impact of various parameters and different cases. The results show that compared with the same trust threshold, it is more difficult for the whole group to reach consensus when the trust threshold is different; the acceptance of opinion can have a significant effect on the number of opinion clusters and the convergence time; heterogeneity in individuals will promote better aggregation of group opinions. This model can explain more conscious opinion evolution in real life and has a significance that effectively guides public opinion.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114405494","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344971
Mufeng Zhang, Yining Wang, T. Luo
In this paper, a distributed arrhythmia detection algorithm based on electrocardiogram (ECG) is proposed for auxiliary diagnosis and treatment. ECG that contains tremendous cardiac rhythm information plays an important role in clinical treatment. Machine learning (ML) algorithms can effectively build the relationship between ECG and the underlying arrhythmia in it. Due to the privacy sensitivity of the ECG, we introduced a federated learning (FL)-based distributed algorithm that enables each medical institution to cooperatively train a arrhythmia detection algorithm locally. Compared with the traditional centralized ML algorithms, the use of FL-based algorithm does not need to collect all the local ECG of each medical institution to an external platform to perform centralized learning, and hence preventing the privacy from leakage. However, ECG collected from different medical institution is non-independent and identically distributed (non-IID) in reality, which will lead to non convergence of the FL-based algorithm. To address this challenge, we optimize the FL-based algorithm using a sharing strategy for partial ECG data of each medical institution combined with elastic weight consolidation (EWC) algorithm. Here, the sharing strategy, which makes each medical institution share ECG data to the central server while not share to other clients, could help build an initial FL model and EWC algorithm make the accuracy of the model trained by each medical institution not decline, therefore the proposed FL algorithm can achieve a trade-off between the privacy and model performance. The experiment results show that, compared with baseline FedAvg algorithm and FedCurv algorithm, the optimized FL-based algorithm is faster in convergence for IID ECG and achieves signicant improvement in terms of both recall and precision for non-IID ECG.
{"title":"Federated Learning for Arrhythmia Detection of Non-IID ECG","authors":"Mufeng Zhang, Yining Wang, T. Luo","doi":"10.1109/ICCC51575.2020.9344971","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344971","url":null,"abstract":"In this paper, a distributed arrhythmia detection algorithm based on electrocardiogram (ECG) is proposed for auxiliary diagnosis and treatment. ECG that contains tremendous cardiac rhythm information plays an important role in clinical treatment. Machine learning (ML) algorithms can effectively build the relationship between ECG and the underlying arrhythmia in it. Due to the privacy sensitivity of the ECG, we introduced a federated learning (FL)-based distributed algorithm that enables each medical institution to cooperatively train a arrhythmia detection algorithm locally. Compared with the traditional centralized ML algorithms, the use of FL-based algorithm does not need to collect all the local ECG of each medical institution to an external platform to perform centralized learning, and hence preventing the privacy from leakage. However, ECG collected from different medical institution is non-independent and identically distributed (non-IID) in reality, which will lead to non convergence of the FL-based algorithm. To address this challenge, we optimize the FL-based algorithm using a sharing strategy for partial ECG data of each medical institution combined with elastic weight consolidation (EWC) algorithm. Here, the sharing strategy, which makes each medical institution share ECG data to the central server while not share to other clients, could help build an initial FL model and EWC algorithm make the accuracy of the model trained by each medical institution not decline, therefore the proposed FL algorithm can achieve a trade-off between the privacy and model performance. The experiment results show that, compared with baseline FedAvg algorithm and FedCurv algorithm, the optimized FL-based algorithm is faster in convergence for IID ECG and achieves signicant improvement in terms of both recall and precision for non-IID ECG.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"472 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115040296","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345304
Chao Wang, Na Wang, Sian-Jheng
In this paper, we focus on the extensively utilized algorithm for split radix FFT. It proposes two the 6mpoint split radix fast Fourier transform (SRFFT), where the complex numbers are represented in a special basis (1, μ) and μ is the complex cube root of unity. Two SRFFTs, termed radix-2/6 and radix-3/6, are proposed and both algorithms are based on radix 2 and radix 3 FFT. Furthermore, we utilize them to design appropriate algorithm structure for length 6m• In addition, fast multiplication in (1, μ) is also proposed. Compared with prior results, the proposed SRFFT requires fewer real multiplications. To our knowledge, this is the first SRFFTs over the basis (1, μ) and this work achieves better specifications for area use and delay. Meanwhile, the occupied resources are approximately same. Moreover, the performance of different FFT length is analyzed.
{"title":"Radix-2/6 and Radix-3/6 FFTs for a Length 6m","authors":"Chao Wang, Na Wang, Sian-Jheng","doi":"10.1109/ICCC51575.2020.9345304","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345304","url":null,"abstract":"In this paper, we focus on the extensively utilized algorithm for split radix FFT. It proposes two the 6mpoint split radix fast Fourier transform (SRFFT), where the complex numbers are represented in a special basis (1, μ) and μ is the complex cube root of unity. Two SRFFTs, termed radix-2/6 and radix-3/6, are proposed and both algorithms are based on radix 2 and radix 3 FFT. Furthermore, we utilize them to design appropriate algorithm structure for length 6m• In addition, fast multiplication in (1, μ) is also proposed. Compared with prior results, the proposed SRFFT requires fewer real multiplications. To our knowledge, this is the first SRFFTs over the basis (1, μ) and this work achieves better specifications for area use and delay. Meanwhile, the occupied resources are approximately same. Moreover, the performance of different FFT length is analyzed.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116245991","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344999
J. Shi, Pin Wang, Hanxi Li, Long Shuai
Bearing plays decisive roles in modern industrial and electrical foundations. For authentic situation, immensely streaming and distributed data are congregated by Prognostics and Health Management (PHM) systems. The massive rigid data conduces the following puzzle: comparable huge excesses for PHM system, which is bounded on the whole huge sets. For this task, we employ active learning framework. In this paper, we firstly propose a novel nonparametric active learning (NAL) method and prove that NAL acquisition function is a tightly upper-bound of naive form. We validate our method on TCN (Temporal Convolutional Network) and achieve the state of the art performance on CWRU benchmark, providing mighty data effectiveness enhancement on industrial field.
{"title":"Nonparametric Active Learning on Bearing Fault Diagnosis","authors":"J. Shi, Pin Wang, Hanxi Li, Long Shuai","doi":"10.1109/ICCC51575.2020.9344999","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344999","url":null,"abstract":"Bearing plays decisive roles in modern industrial and electrical foundations. For authentic situation, immensely streaming and distributed data are congregated by Prognostics and Health Management (PHM) systems. The massive rigid data conduces the following puzzle: comparable huge excesses for PHM system, which is bounded on the whole huge sets. For this task, we employ active learning framework. In this paper, we firstly propose a novel nonparametric active learning (NAL) method and prove that NAL acquisition function is a tightly upper-bound of naive form. We validate our method on TCN (Temporal Convolutional Network) and achieve the state of the art performance on CWRU benchmark, providing mighty data effectiveness enhancement on industrial field.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687762","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345229
Bojun Xia, Zhongyue Chen, Xiaoping Chen
In recent years, regression-based scene text detection methods have achieved great success. However, because the network has a limited receptive field, the predicted bounding boxes cannot enclose the entire text instance when dealing with the long text instance. In this paper, we propose a multi-oriented scene text detection method based on location-sensitive segmentation. The main idea is that we divide the whole text instance detection into three sub-text instances (left part, middle part, and right part) detection. To form the final detection bounding box, we get three candidate bounding boxes from three sub-text instances and then merge them by getting the minimum rectangular area. Finally, the pixel-level score maps are used to filter false positives. Experiments on ICDAR2015 and MSRA-TD500 demonstrate that the proposed method achieves great performance. For ICDAR2015 Dataset, the method achieves an F-measure of 0.822 and a precision rate of 0.876.
{"title":"A Multi-Oriented Scene Text Detection Method Based on Location-Sensitive Segmentation","authors":"Bojun Xia, Zhongyue Chen, Xiaoping Chen","doi":"10.1109/ICCC51575.2020.9345229","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345229","url":null,"abstract":"In recent years, regression-based scene text detection methods have achieved great success. However, because the network has a limited receptive field, the predicted bounding boxes cannot enclose the entire text instance when dealing with the long text instance. In this paper, we propose a multi-oriented scene text detection method based on location-sensitive segmentation. The main idea is that we divide the whole text instance detection into three sub-text instances (left part, middle part, and right part) detection. To form the final detection bounding box, we get three candidate bounding boxes from three sub-text instances and then merge them by getting the minimum rectangular area. Finally, the pixel-level score maps are used to filter false positives. Experiments on ICDAR2015 and MSRA-TD500 demonstrate that the proposed method achieves great performance. For ICDAR2015 Dataset, the method achieves an F-measure of 0.822 and a precision rate of 0.876.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124014718","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345302
Yichen Zhang, Jeong Hoon Han, Y. Kwon, Y. Moon
In recent years, object detectors generally use the feature pyramid network (FPN) to solve the problem of scale variation in object detection. In this paper, we propose a new architecture of feature pyramid network which combines a top-down feature pyramid network and a bottom-up feature pyramid network. The main contributions of the proposed method are two-fold: (1) We design a more complex feature pyramid network to get the feature maps for object detection. (2) By combining these two architectures, we can get the feature maps with richer semantic information to solve the problem of scale variation better. The proposed method experiments on PASCAL VOC2007 dataset. Experimental results show that the proposed method can improve the accuracy of detectors using the FPN by about 1.67%.
{"title":"A New Architecture of Feature Pyramid Network for Object Detection","authors":"Yichen Zhang, Jeong Hoon Han, Y. Kwon, Y. Moon","doi":"10.1109/ICCC51575.2020.9345302","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345302","url":null,"abstract":"In recent years, object detectors generally use the feature pyramid network (FPN) to solve the problem of scale variation in object detection. In this paper, we propose a new architecture of feature pyramid network which combines a top-down feature pyramid network and a bottom-up feature pyramid network. The main contributions of the proposed method are two-fold: (1) We design a more complex feature pyramid network to get the feature maps for object detection. (2) By combining these two architectures, we can get the feature maps with richer semantic information to solve the problem of scale variation better. The proposed method experiments on PASCAL VOC2007 dataset. Experimental results show that the proposed method can improve the accuracy of detectors using the FPN by about 1.67%.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"9 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125707599","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}
Fingerprint based localization is an effective positioning method for rich scattering environments, which has attracted the enormous attention in recent years. In this paper, we propose a novel fingerprint positioning method for cell-free massive multiple-input multiple-output (MIMO) systems. The angle-domain channel power matrix with lots of angle information can be extracted as the arrival-of-angle (AOA) fingerprint by exploiting discrete Fourier transform (DFT) operation. Then we also propose the angle similarity coefficient and the Euclidean distance as the AOA and received signal strength (RSS) fingerprint similarity criterions respectively to evaluate the distance between two fingerprints. Moreover, the K-means clustering algorithm is performed for improving the efficiency of fingerprint matching. Finally, we utilize the weighted K-nearest neighbor (WKNN) algotithm to estimate the location of the user, whose weight can be constructed according to the above fingerprint similarity criterions. The simulation results demonstrate that our proposed joint AOA-RSS fingerprint based location method has the better positioning performance than the methods only consider AOA or RSS fingerprint.
{"title":"Joint AOA-RSS Fingerprint Based Localization for Cell-Free Massive MIMO Systems","authors":"Chen Wei, Kui Xu, Zhexian Shen, Xiaochen Xia, Wei Xie, Lihua Chen, Jianhui Xu","doi":"10.1109/ICCC51575.2020.9344979","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344979","url":null,"abstract":"Fingerprint based localization is an effective positioning method for rich scattering environments, which has attracted the enormous attention in recent years. In this paper, we propose a novel fingerprint positioning method for cell-free massive multiple-input multiple-output (MIMO) systems. The angle-domain channel power matrix with lots of angle information can be extracted as the arrival-of-angle (AOA) fingerprint by exploiting discrete Fourier transform (DFT) operation. Then we also propose the angle similarity coefficient and the Euclidean distance as the AOA and received signal strength (RSS) fingerprint similarity criterions respectively to evaluate the distance between two fingerprints. Moreover, the K-means clustering algorithm is performed for improving the efficiency of fingerprint matching. Finally, we utilize the weighted K-nearest neighbor (WKNN) algotithm to estimate the location of the user, whose weight can be constructed according to the above fingerprint similarity criterions. The simulation results demonstrate that our proposed joint AOA-RSS fingerprint based location method has the better positioning performance than the methods only consider AOA or RSS fingerprint.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125776258","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9344911
Liang Zhang, Hao Yan, Qingyi Zhu
The characteristics of high network traffic dimension and large data volume make the traditional network intrusion detection model have a longer response time, lower detection accuracy, and seriously endanger the data security of network entities. In order to solve this problem, this paper studies the improved LSTM intrusion detection algorithm model, and uses Quantum Particle Swarm Optimization (QPSO) to select the network traffic data to reduce the feature dimension. The dimensionality-reduced network traffic is classified to detect network intrusion behavior. After testing on the KDDCup99 data set, the experimental results show that the QPSO feature selection algorithm can select the optimal feature subset, and the improved LSTM network can effectively improve the accuracy and F1-Score of intrusion detection.
{"title":"An Improved LSTM Network Intrusion Detection Method","authors":"Liang Zhang, Hao Yan, Qingyi Zhu","doi":"10.1109/ICCC51575.2020.9344911","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9344911","url":null,"abstract":"The characteristics of high network traffic dimension and large data volume make the traditional network intrusion detection model have a longer response time, lower detection accuracy, and seriously endanger the data security of network entities. In order to solve this problem, this paper studies the improved LSTM intrusion detection algorithm model, and uses Quantum Particle Swarm Optimization (QPSO) to select the network traffic data to reduce the feature dimension. The dimensionality-reduced network traffic is classified to detect network intrusion behavior. After testing on the KDDCup99 data set, the experimental results show that the QPSO feature selection algorithm can select the optimal feature subset, and the improved LSTM network can effectively improve the accuracy and F1-Score of intrusion detection.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125918960","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345059
Liu Mingtai, Lin Jiarui
According to the test requirements, this paper presents an implementation scheme of the internal module automatic test system of vector network analyzer. The test system software consists of six sub-test systems. This test system supports the import of customer-defined program control commands, so that the test system can be adapted to different manufacturers and different types of test instruments to complete the test task. This test system has been widely used in the 3672 series vector network analyzer production line, saving about 60,000 man-hours per year for Instruments Co., Ltd, reducing the production cost of the instrument by 30%.
{"title":"Implementation Scheme of The Internal Module Automatic Test System of Vector Network Analyzer","authors":"Liu Mingtai, Lin Jiarui","doi":"10.1109/ICCC51575.2020.9345059","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345059","url":null,"abstract":"According to the test requirements, this paper presents an implementation scheme of the internal module automatic test system of vector network analyzer. The test system software consists of six sub-test systems. This test system supports the import of customer-defined program control commands, so that the test system can be adapted to different manufacturers and different types of test instruments to complete the test task. This test system has been widely used in the 3672 series vector network analyzer production line, saving about 60,000 man-hours per year for Instruments Co., Ltd, reducing the production cost of the instrument by 30%.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970185","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 : 2020-12-11DOI: 10.1109/ICCC51575.2020.9345311
Fang Sun, Zhijun Deng, Changqing Wang, Zhe Li
High-speed space vehicles realize interconnection, coordinated operations and precise strikes through Flying Ad hoc Network (FANET), which has the characteristics of high-speed node, highly dynamic network topology, and multiple priority tasks guarantee requirements. First, a five-layer protocol architecture networking scheme suitable for FANET is proposed, which adopts Statistical Priority-based Multiple Access (SPMA) as data link layer multiple access protocol. Then, SPMA algorithm flow is designed, and three key algorithms, namely, channel occupancy statistics algorithm, priority threshold algorithm and back off time algorithm are briefly analyzed and feasible solutions are given. Finally, simulation modeling of FANET basing on SPMA is conducted on OPNET platform. Through theoretical analysis and simulation experiments, it can be proved that SPMA protocol designed in this paper can realize multiple priority differentiated services, ensure low latency and high reliability of high-priority packets, and meet the QoS requirements of FANET. Moreover, compared to the traditional CSMA/CA protocol, the SPMA protocol has better performance in all aspects.
{"title":"A Networking Scheme for FANET Basing on SPMA Protocol","authors":"Fang Sun, Zhijun Deng, Changqing Wang, Zhe Li","doi":"10.1109/ICCC51575.2020.9345311","DOIUrl":"https://doi.org/10.1109/ICCC51575.2020.9345311","url":null,"abstract":"High-speed space vehicles realize interconnection, coordinated operations and precise strikes through Flying Ad hoc Network (FANET), which has the characteristics of high-speed node, highly dynamic network topology, and multiple priority tasks guarantee requirements. First, a five-layer protocol architecture networking scheme suitable for FANET is proposed, which adopts Statistical Priority-based Multiple Access (SPMA) as data link layer multiple access protocol. Then, SPMA algorithm flow is designed, and three key algorithms, namely, channel occupancy statistics algorithm, priority threshold algorithm and back off time algorithm are briefly analyzed and feasible solutions are given. Finally, simulation modeling of FANET basing on SPMA is conducted on OPNET platform. Through theoretical analysis and simulation experiments, it can be proved that SPMA protocol designed in this paper can realize multiple priority differentiated services, ensure low latency and high reliability of high-priority packets, and meet the QoS requirements of FANET. Moreover, compared to the traditional CSMA/CA protocol, the SPMA protocol has better performance in all aspects.","PeriodicalId":386048,"journal":{"name":"2020 IEEE 6th International Conference on Computer and Communications (ICCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126107104","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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