Pub Date : 2022-10-14DOI: 10.1109/ICCIS56375.2022.9998155
Malak Abid Ali Khan, Hongbin Ma, Syed Muhammad Aamir, Anil Baris Cekderi, Mustak Ahamed, Abdulraman Abdo Ali Alsumeri
Slotted ALOHA implemented in the internet of things (IoT) uses the LoRaWAN media access control (MAC) protocol to improve its performance. Various backoff algorithms have been proposed in LoRaWAN to evaluate the delay, throughput, and packet loss rate (PLR). However, an adaptive backoff algorithm has been implemented in this paper to examine the performance metrics for electronic shelf labels (ESLs). The use of adaptive backoff optimizes the delay and the bandwidth (BW) for more efficient and meaningful communication with a certain degree of data loss. The results illustrate that the intra-slicing model and adaptive backoff estimate the optimal delay for each slice, starting with the best slicing priority for the end device (ED) which brings mobility into the network.
{"title":"Performance of Slotted ALOHA for LoRa-ESL Based on Adaptive Backoff and Intra Slicing","authors":"Malak Abid Ali Khan, Hongbin Ma, Syed Muhammad Aamir, Anil Baris Cekderi, Mustak Ahamed, Abdulraman Abdo Ali Alsumeri","doi":"10.1109/ICCIS56375.2022.9998155","DOIUrl":"https://doi.org/10.1109/ICCIS56375.2022.9998155","url":null,"abstract":"Slotted ALOHA implemented in the internet of things (IoT) uses the LoRaWAN media access control (MAC) protocol to improve its performance. Various backoff algorithms have been proposed in LoRaWAN to evaluate the delay, throughput, and packet loss rate (PLR). However, an adaptive backoff algorithm has been implemented in this paper to examine the performance metrics for electronic shelf labels (ESLs). The use of adaptive backoff optimizes the delay and the bandwidth (BW) for more efficient and meaningful communication with a certain degree of data loss. The results illustrate that the intra-slicing model and adaptive backoff estimate the optimal delay for each slice, starting with the best slicing priority for the end device (ED) which brings mobility into the network.","PeriodicalId":398546,"journal":{"name":"2022 6th International Conference on Communication and Information Systems (ICCIS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125942490","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 : 2022-10-14DOI: 10.1109/ICCIS56375.2022.9998164
Xiaohan Zhang, Shufeng Li, Feng Hu
Cloud performing arts businesses has been accelerated by the advent of the 5G era and the COVID-19 pandemic, so there is a growing demand for a quality of experience (QoE) predictive model. However, QoE is a time series factor with nonlinear relationship influence, including subjective and objective factors named Quality of Service(QoS), which leads to a high complex prediction. To solve this problem, existing studies have utilized Long Short-term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) to effectively capture this kind of complex dependency, respectively, to obtain excellent QoE prediction accuracy. However, they can not take into account the accuracy and computational efficiency at the same time. So we proposes CGRU-QoE, that is, using CNN to extract global information, using the variant of LSTM--Gate Recurrent Unit (GRU) to extract context information, and then following the Attention Mechanism. In addition, we introduced a new input factor representing bitrate. The proposed method is mainly validated in the LFOVIA database and is superior to the baseline method in terms of prediction accuracy and computational complexity.
{"title":"Convolutional Recurrent Neural Networks with Attention Mechanism for Streaming QoE Prediction","authors":"Xiaohan Zhang, Shufeng Li, Feng Hu","doi":"10.1109/ICCIS56375.2022.9998164","DOIUrl":"https://doi.org/10.1109/ICCIS56375.2022.9998164","url":null,"abstract":"Cloud performing arts businesses has been accelerated by the advent of the 5G era and the COVID-19 pandemic, so there is a growing demand for a quality of experience (QoE) predictive model. However, QoE is a time series factor with nonlinear relationship influence, including subjective and objective factors named Quality of Service(QoS), which leads to a high complex prediction. To solve this problem, existing studies have utilized Long Short-term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) to effectively capture this kind of complex dependency, respectively, to obtain excellent QoE prediction accuracy. However, they can not take into account the accuracy and computational efficiency at the same time. So we proposes CGRU-QoE, that is, using CNN to extract global information, using the variant of LSTM--Gate Recurrent Unit (GRU) to extract context information, and then following the Attention Mechanism. In addition, we introduced a new input factor representing bitrate. The proposed method is mainly validated in the LFOVIA database and is superior to the baseline method in terms of prediction accuracy and computational complexity.","PeriodicalId":398546,"journal":{"name":"2022 6th International Conference on Communication and Information Systems (ICCIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126005419","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 : 2022-10-14DOI: 10.1109/ICCIS56375.2022.9998132
Chao Jiang, Wei Wang, Dewei Yang, Yan Yang, Huayun Mao
The alignment of inertial measurement units(IMUs) to segment is an important step in inertial motion capture, which directly affects whether the imu data can fully represent the motion of the segment. Inspired by the gene crossover and mutation of Genetic Algorithm(GA), we propose a dynamic inertial weighted particle swarm optimization algorithm with cross factor to solve the joint constraint problem, and compared our algorithm with Particle Swarm Optimization(PSO) and Dynamic Inertial Weighted Particle Swarm Optimization(DPSO) algorithms to show the superiority of our algorithm during human lower limb movements. The experiment shows that introduced the random cross mechanism between particles with larger fitness and only the effective cross retained, makes the new algorithm show better search ability and convergence effect in this project, the stability and effectiveness are also improved. Our current work provides a good support for accurate calculation of joint angles in the future.
{"title":"Application of Dynamic Weight Particle Swarm Optimization with Cross Factor in Joint Calibration","authors":"Chao Jiang, Wei Wang, Dewei Yang, Yan Yang, Huayun Mao","doi":"10.1109/ICCIS56375.2022.9998132","DOIUrl":"https://doi.org/10.1109/ICCIS56375.2022.9998132","url":null,"abstract":"The alignment of inertial measurement units(IMUs) to segment is an important step in inertial motion capture, which directly affects whether the imu data can fully represent the motion of the segment. Inspired by the gene crossover and mutation of Genetic Algorithm(GA), we propose a dynamic inertial weighted particle swarm optimization algorithm with cross factor to solve the joint constraint problem, and compared our algorithm with Particle Swarm Optimization(PSO) and Dynamic Inertial Weighted Particle Swarm Optimization(DPSO) algorithms to show the superiority of our algorithm during human lower limb movements. The experiment shows that introduced the random cross mechanism between particles with larger fitness and only the effective cross retained, makes the new algorithm show better search ability and convergence effect in this project, the stability and effectiveness are also improved. Our current work provides a good support for accurate calculation of joint angles in the future.","PeriodicalId":398546,"journal":{"name":"2022 6th International Conference on Communication and Information Systems (ICCIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074674","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 : 2022-10-14DOI: 10.1109/ICCIS56375.2022.9998150
I. V. R. Domingo, Christian James Sunga, Miguell Comia
The researchers aimed at creating an App-Based Gym Workout Instructor using Image Recognition via Artificial Neural Network which can recognize the body type of a male person using images and show the workout for the body type. The input is a whole-body image of a male person and the output is the workout for the detected body type. Using MATLAB, the researchers created an Artificial Neural Network that is trained to recognize body types and C# platform to implement the ANN. The results of the study showed that the developed system was able to determine the body type of the user. In terms of the over-all accuracy of the developed igym instructor for all of the body type defined, it was fairly moderate with an average of 64.38%. The effectivity and accuracy of the iGYM does not only depend on the number of training data but also with the quality of the data set.
{"title":"iGYM: Implementation of Image Recognition Using Silhouette Extraction and Artificial Neural Network as Gym Instructor","authors":"I. V. R. Domingo, Christian James Sunga, Miguell Comia","doi":"10.1109/ICCIS56375.2022.9998150","DOIUrl":"https://doi.org/10.1109/ICCIS56375.2022.9998150","url":null,"abstract":"The researchers aimed at creating an App-Based Gym Workout Instructor using Image Recognition via Artificial Neural Network which can recognize the body type of a male person using images and show the workout for the body type. The input is a whole-body image of a male person and the output is the workout for the detected body type. Using MATLAB, the researchers created an Artificial Neural Network that is trained to recognize body types and C# platform to implement the ANN. The results of the study showed that the developed system was able to determine the body type of the user. In terms of the over-all accuracy of the developed igym instructor for all of the body type defined, it was fairly moderate with an average of 64.38%. The effectivity and accuracy of the iGYM does not only depend on the number of training data but also with the quality of the data set.","PeriodicalId":398546,"journal":{"name":"2022 6th International Conference on Communication and Information Systems (ICCIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122714699","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 : 2022-10-14DOI: 10.1109/ICCIS56375.2022.9998148
Jin Li, Peng Liu
For the bilinear communication networked control system based on active PID sequence, a new method is proposed to approach the controller continuously. According to the continuous approximation method, the initial optimal control becomes a series of non-uniform optimization rules for linear problems composed of accurate linear feedback and nonlinear time compensation, which limits the solution sequence of conjugate vector differential equations. The cut-off time of nonlinear compensation is superimposed on the optimal order to obtain the ranking rule. The simulation example shows the effectiveness of continuous approximation.
{"title":"Congestion Control of Bilinear Continuous Communication Network Control System Based on PID Active Queue Management","authors":"Jin Li, Peng Liu","doi":"10.1109/ICCIS56375.2022.9998148","DOIUrl":"https://doi.org/10.1109/ICCIS56375.2022.9998148","url":null,"abstract":"For the bilinear communication networked control system based on active PID sequence, a new method is proposed to approach the controller continuously. According to the continuous approximation method, the initial optimal control becomes a series of non-uniform optimization rules for linear problems composed of accurate linear feedback and nonlinear time compensation, which limits the solution sequence of conjugate vector differential equations. The cut-off time of nonlinear compensation is superimposed on the optimal order to obtain the ranking rule. The simulation example shows the effectiveness of continuous approximation.","PeriodicalId":398546,"journal":{"name":"2022 6th International Conference on Communication and Information Systems (ICCIS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576609","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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