Pub Date : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10201969
Praiwan Patcharabumrung, Y. Jewajinda, Kata Praditwong
This paper presents the effects of crossover and mutation operators on neural architecture search using a multi-objective genetic algorithm. The proposed algorithm employs a dual population approach with non-dominated sorting, namely, elite, and mixed population, to increase the diversity of the search. To evaluate the effect of genetic operators, we use a simple layer-based encoding for VGG-like convolution neural network models that resemble the model. We also present the effects of the initialized populations' diversity on the solutions' quality with three types of genetic operators: crossover, mutation, and crossover with a mutation in two groups of experiments initialized with low and high diversity. The experimental results are reported on the Cifar-10 dataset and compared to the state-of-the-art approach.
{"title":"Effects of Genetic Operators on Neural Architecture Search Using Multi-Objective Genetic Algorithm","authors":"Praiwan Patcharabumrung, Y. Jewajinda, Kata Praditwong","doi":"10.1109/JCSSE58229.2023.10201969","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10201969","url":null,"abstract":"This paper presents the effects of crossover and mutation operators on neural architecture search using a multi-objective genetic algorithm. The proposed algorithm employs a dual population approach with non-dominated sorting, namely, elite, and mixed population, to increase the diversity of the search. To evaluate the effect of genetic operators, we use a simple layer-based encoding for VGG-like convolution neural network models that resemble the model. We also present the effects of the initialized populations' diversity on the solutions' quality with three types of genetic operators: crossover, mutation, and crossover with a mutation in two groups of experiments initialized with low and high diversity. The experimental results are reported on the Cifar-10 dataset and compared to the state-of-the-art approach.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133706163","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}
Real estate appraisals are crucial in determining the value of properties. Condominium valuations, in particular, have mathematical formulas that are applied to determine their value. However, the use of machine learning in real estate appraisals, including condominium valuations, is still not widely trusted. This is because historical data is necessary for machine learning models to make accurate predictions. Additionally, the effectiveness of the most commonly used regression model in practice is limited, and most of the research conducted in this field focuses on appraisals of properties that have already been set a price. To increase the confidence in machine learning models used in real estate appraisals, we propose a modified method that involves feature engineering with a similar name and near area for new condominium appraisals. The goal of this method is to increase the capabilities of the machine learning model or reduce the Mean Absolute Percentage Error (MAPE).
{"title":"Machine Learning Models for Condominium Appraisal with Result Tuning","authors":"Sahassawat Posungnern, Sansiri Tanachutiwat, Thanit Anchaleechamaikorn, Taninnuch Lamjiak","doi":"10.1109/JCSSE58229.2023.10201968","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10201968","url":null,"abstract":"Real estate appraisals are crucial in determining the value of properties. Condominium valuations, in particular, have mathematical formulas that are applied to determine their value. However, the use of machine learning in real estate appraisals, including condominium valuations, is still not widely trusted. This is because historical data is necessary for machine learning models to make accurate predictions. Additionally, the effectiveness of the most commonly used regression model in practice is limited, and most of the research conducted in this field focuses on appraisals of properties that have already been set a price. To increase the confidence in machine learning models used in real estate appraisals, we propose a modified method that involves feature engineering with a similar name and near area for new condominium appraisals. The goal of this method is to increase the capabilities of the machine learning model or reduce the Mean Absolute Percentage Error (MAPE).","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"407 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131934727","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202043
Bhattarapong Somwong, Kritsana Kumphet, W. Massagram
This paper presents an audio classification system specifically created for Portenta H7, an Arduino-based microcontroller. The proposed model utilizes Edge Impulse AI platform, which allows the creation of accurate and efficient classification models optimized for embedded systems. To evaluate the system performance, a set of experiments was conducted on a dataset of audio samples from four classes: chainsaw, handsaw, gunshot, and laugh - each depicted sounds involving illegal logging and poaching threat in the forests. The results demonstrate that the proposed approach achieved high accuracy for gunshot, satisfying accuracy for chainsaw and laugh, and unacceptable accuracy for handsaw from our satellite-enabled system. The proposed system also has potential applications in forest protection as well as various domains, such as smart homes, security systems, and healthcare, where accurate audio classification can enable intelligent decision-making.
{"title":"Acoustic Monitoring System with AI Threat Detection System for Forest Protection","authors":"Bhattarapong Somwong, Kritsana Kumphet, W. Massagram","doi":"10.1109/JCSSE58229.2023.10202043","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202043","url":null,"abstract":"This paper presents an audio classification system specifically created for Portenta H7, an Arduino-based microcontroller. The proposed model utilizes Edge Impulse AI platform, which allows the creation of accurate and efficient classification models optimized for embedded systems. To evaluate the system performance, a set of experiments was conducted on a dataset of audio samples from four classes: chainsaw, handsaw, gunshot, and laugh - each depicted sounds involving illegal logging and poaching threat in the forests. The results demonstrate that the proposed approach achieved high accuracy for gunshot, satisfying accuracy for chainsaw and laugh, and unacceptable accuracy for handsaw from our satellite-enabled system. The proposed system also has potential applications in forest protection as well as various domains, such as smart homes, security systems, and healthcare, where accurate audio classification can enable intelligent decision-making.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114869423","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}
Marketing planning plays an important role in the success of any business in general. The ability to predict future earnings tends to allow us to make effective decisions and plan actions. These will help us work more smoothly in the future. However, traditional methods such as Linear Regression may limit the accuracy of predictions for various reasons. To fix this problem, we propose neural network regression with enhanced pseudo-input, working in tandem with business-predictive data models to fill in the missing information. The proposed approach involves training the model using datasets from 3 and 7 days with some missing data through deep learning regression to obtain more accurate prediction results. Comparing our proposed method with the classical linear regression method, our proposed method provided us with higher performance as evidenced by reduced losses.
{"title":"Maximizing Efficiency in Marketing Planning: Artificial Neural Network Regression and Data Imputation for Improving Business Forecasting","authors":"Panyanat Aonpong, Ratchai Thipbumrung, Weenawadee Muangon, Opas Wongtaweesap","doi":"10.1109/JCSSE58229.2023.10202144","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202144","url":null,"abstract":"Marketing planning plays an important role in the success of any business in general. The ability to predict future earnings tends to allow us to make effective decisions and plan actions. These will help us work more smoothly in the future. However, traditional methods such as Linear Regression may limit the accuracy of predictions for various reasons. To fix this problem, we propose neural network regression with enhanced pseudo-input, working in tandem with business-predictive data models to fill in the missing information. The proposed approach involves training the model using datasets from 3 and 7 days with some missing data through deep learning regression to obtain more accurate prediction results. Comparing our proposed method with the classical linear regression method, our proposed method provided us with higher performance as evidenced by reduced losses.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116481539","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202014
Wassakorn Sarakul, Attapol T. Rutherford
Word embeddings are useful for studying public opinions by summarizing opinions about a concept by finding the nearest neighbors in the word embedding space. Static word embeddings such as word2vec are powerful for handling large amounts of text, while contextualized word embeddings from transformer-based models yield better embeddings by some evaluation metrics. In this study, we explore the differences between static and contextualized embeddings for word-based analysis of opposing opinions. We find that pre-training is necessary for static embeddings when the corpus is small, but contextualized embeddings are superior. When the focus corpus is large, static embeddings reflect related concepts, while contextualized embeddings often show synonyms or cohypernyms. Static embeddings trained only on the focus corpus capture opposing opinions better than contextualized embeddings.
{"title":"Contextualized vs. Static Word Embeddings for Word-based Analysis of Opposing Opinions","authors":"Wassakorn Sarakul, Attapol T. Rutherford","doi":"10.1109/JCSSE58229.2023.10202014","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202014","url":null,"abstract":"Word embeddings are useful for studying public opinions by summarizing opinions about a concept by finding the nearest neighbors in the word embedding space. Static word embeddings such as word2vec are powerful for handling large amounts of text, while contextualized word embeddings from transformer-based models yield better embeddings by some evaluation metrics. In this study, we explore the differences between static and contextualized embeddings for word-based analysis of opposing opinions. We find that pre-training is necessary for static embeddings when the corpus is small, but contextualized embeddings are superior. When the focus corpus is large, static embeddings reflect related concepts, while contextualized embeddings often show synonyms or cohypernyms. Static embeddings trained only on the focus corpus capture opposing opinions better than contextualized embeddings.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114064373","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202087
Pranpreya Samasutthi, Chutiporn Anutariya
In June 2022, the Ministry of Public Health of Thailand announced that cannabis can be used in healthcare, medical, research, and commerce. This announcement led to a wide discussion in social media, TV broadcasts, and public media about how to use cannabis safely, the symptoms that cannabis can relieve, side effects, and precautions of cannabis. Prior to the Ministry's announcement, cannabis was illegal and forbidden to consume since it was considered as a kind of drugs and was dangerous to consume, even privately. Most people still lack of proper knowledge about safe cannabis consumption. Also, information provided on the Internet and social media about cannabis is now often redundant, inconsistent, and unreliable. Therefore, this study gathers official and reliable published knowledge about cannabis consumption specifically for general users. CannabisO is modeled as a formal, shareable, and reusable ontology-based knowledge model to be used for a Q&A system that can help answer questions about safe edible consumption of cannabis, precautions and side effects of cannabis consumption, and lastly, symptoms that can be alleviated by cannabis. Several interesting Q&As are demonstrated in this paper.
{"title":"CannabisO: The Ontology-based Knowledge Model for Safe Cannabis Consumption in Thailand","authors":"Pranpreya Samasutthi, Chutiporn Anutariya","doi":"10.1109/JCSSE58229.2023.10202087","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202087","url":null,"abstract":"In June 2022, the Ministry of Public Health of Thailand announced that cannabis can be used in healthcare, medical, research, and commerce. This announcement led to a wide discussion in social media, TV broadcasts, and public media about how to use cannabis safely, the symptoms that cannabis can relieve, side effects, and precautions of cannabis. Prior to the Ministry's announcement, cannabis was illegal and forbidden to consume since it was considered as a kind of drugs and was dangerous to consume, even privately. Most people still lack of proper knowledge about safe cannabis consumption. Also, information provided on the Internet and social media about cannabis is now often redundant, inconsistent, and unreliable. Therefore, this study gathers official and reliable published knowledge about cannabis consumption specifically for general users. CannabisO is modeled as a formal, shareable, and reusable ontology-based knowledge model to be used for a Q&A system that can help answer questions about safe edible consumption of cannabis, precautions and side effects of cannabis consumption, and lastly, symptoms that can be alleviated by cannabis. Several interesting Q&As are demonstrated in this paper.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832445","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202072
Mark Anthony A. Ozaeta, Arnel C. Fajardo, Felimon Brazas, Jed Allan M. Cantal
Seagrasses are among the most ecologically significant and diverse ecosystems on Earth, playing a crucial role in maintaining the health and productivity of coastal environments. However, these important habitats are threatened by various human activities, including pollution, habitat destruction, and climate change. To address these challenges, it is essential to develop effective conservation and management strategies that protect seagrass ecosystems and the species that depend on them. Accurately identifying various seagrass species is essential to understanding their habitat and overall health. The researchers have developed a seagrass species identification model to address this challenge using a differentiable architecture search with an early stopping strategy. This model achieved an impressive overall accuracy of 93.3% within a relatively short training time of 4 hours and 11 minutes using a commercially-available Apple MacBook device. This model has the potential to greatly improve the efficiency and accuracy of seagrass species identification, providing valuable insights for conservation efforts and supporting the conservation of these vital ecosystems.
{"title":"Seagrass Classification Using Differentiable Architecture Search","authors":"Mark Anthony A. Ozaeta, Arnel C. Fajardo, Felimon Brazas, Jed Allan M. Cantal","doi":"10.1109/JCSSE58229.2023.10202072","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202072","url":null,"abstract":"Seagrasses are among the most ecologically significant and diverse ecosystems on Earth, playing a crucial role in maintaining the health and productivity of coastal environments. However, these important habitats are threatened by various human activities, including pollution, habitat destruction, and climate change. To address these challenges, it is essential to develop effective conservation and management strategies that protect seagrass ecosystems and the species that depend on them. Accurately identifying various seagrass species is essential to understanding their habitat and overall health. The researchers have developed a seagrass species identification model to address this challenge using a differentiable architecture search with an early stopping strategy. This model achieved an impressive overall accuracy of 93.3% within a relatively short training time of 4 hours and 11 minutes using a commercially-available Apple MacBook device. This model has the potential to greatly improve the efficiency and accuracy of seagrass species identification, providing valuable insights for conservation efforts and supporting the conservation of these vital ecosystems.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"61 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114039015","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10201941
Nichaphan Noiyoo, Jessada Thutkawkornpin
Checking the quality of essay writing in Thai language is still a complicated task because Thai language is very complex language in terms of punctuation, sentence structure, word repetition, spelling, commenting, and reasoning in content. Therefore, checking the quality of an essay and scoring require the reviewer's skills in reading and interpreting that make long time to review. In addition, if in reviewing process using more than one reviewer, it might affect different quality checking standards. We collected essay in Thai language which is written by student who registered paragraph writing course from The Sirindhorn Thai Language Institute of Chulalongkorn University. This work implemented LSTM model, CNN model, BERT model and WangchanBERTa model to compare the effectiveness of checking the quality of Thai essay writing. Our experimental result shows that classification analysis compiled with WangchanBERTa can achieve high accuracy up to 90%. However, CNN model compiled with classification analysis can achieve high accuracy up to 87% while compiled with regression analysis can achieve high accuracy in the range 90%. In conclusion, the system that we proposed can predict the quality of Thai essays with high accuracy. Therefore, we recommended Wangchanberta model for classification problem and CNN model for regression problem.
{"title":"A Comparison of Machine Learning and Neural Network Algorithms for An Automated Thai Essay Quality Checking","authors":"Nichaphan Noiyoo, Jessada Thutkawkornpin","doi":"10.1109/JCSSE58229.2023.10201941","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10201941","url":null,"abstract":"Checking the quality of essay writing in Thai language is still a complicated task because Thai language is very complex language in terms of punctuation, sentence structure, word repetition, spelling, commenting, and reasoning in content. Therefore, checking the quality of an essay and scoring require the reviewer's skills in reading and interpreting that make long time to review. In addition, if in reviewing process using more than one reviewer, it might affect different quality checking standards. We collected essay in Thai language which is written by student who registered paragraph writing course from The Sirindhorn Thai Language Institute of Chulalongkorn University. This work implemented LSTM model, CNN model, BERT model and WangchanBERTa model to compare the effectiveness of checking the quality of Thai essay writing. Our experimental result shows that classification analysis compiled with WangchanBERTa can achieve high accuracy up to 90%. However, CNN model compiled with classification analysis can achieve high accuracy up to 87% while compiled with regression analysis can achieve high accuracy in the range 90%. In conclusion, the system that we proposed can predict the quality of Thai essays with high accuracy. Therefore, we recommended Wangchanberta model for classification problem and CNN model for regression problem.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125264732","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202033
M. M. Syeed, Md. Rajaul Karim, Md. Shakhawat Hossain, K. Fatema, Mohammad Faisal Uddin, R. Khan
Surface water is heavily exposed to contamination as this water is the ubiquitous source for the majority of water needs. This situation is exaggerated by excessive population, heavy industrialization, rapid urbanization, and ad-hoc monitoring. Comprehensive measurement and knowledge extraction of surface water pollution is therefore pivotal for ensuring safe and hygienic water use. However, current processes of surface water quality profiling involve laboratory-based manual sample collection and testing, which is tardy, expensive, error-prone, and untraceable. This paper, therefore presents the design and development of an IoT integrated water quality profiling system with a novel plug-and-play physical layer for the sensor actuation, and an AI powered fog computing based cloud application layer for remote water quality parameter measurement and data acquisition, remote data logging, monitoring and control, with data analytic for critical reasoning and decision making.
{"title":"An IoT Intensive AI-integrated System for Optimized Surface Water Quality Profiling","authors":"M. M. Syeed, Md. Rajaul Karim, Md. Shakhawat Hossain, K. Fatema, Mohammad Faisal Uddin, R. Khan","doi":"10.1109/JCSSE58229.2023.10202033","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202033","url":null,"abstract":"Surface water is heavily exposed to contamination as this water is the ubiquitous source for the majority of water needs. This situation is exaggerated by excessive population, heavy industrialization, rapid urbanization, and ad-hoc monitoring. Comprehensive measurement and knowledge extraction of surface water pollution is therefore pivotal for ensuring safe and hygienic water use. However, current processes of surface water quality profiling involve laboratory-based manual sample collection and testing, which is tardy, expensive, error-prone, and untraceable. This paper, therefore presents the design and development of an IoT integrated water quality profiling system with a novel plug-and-play physical layer for the sensor actuation, and an AI powered fog computing based cloud application layer for remote water quality parameter measurement and data acquisition, remote data logging, monitoring and control, with data analytic for critical reasoning and decision making.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131588475","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 : 2023-06-28DOI: 10.1109/JCSSE58229.2023.10202040
Muhammad Zain ul Abideen, Aung Myo Htut, C. Aswakul
Nowadays, the goal of making the intelligent transportation system smart and efficient is a much more challenging aspect. The target is to optimize the transportation system and provide real-time traffic information to travelers. Currently, in the software-defined vehicular network, multiple domains are involved, and to enhance modern ITS systems to work efficiently, t he collaboration of such distributed domains is required. The challenge is to address the network overheads and congestion problems when considering vehicular mobility. This paper focuses on developing a control-plane switch migration testbed using the Mininet-WiFi emulation platform instead of the NS-2 simulator, which can later be helpful for evaluation of data-plane by considering multiple performance metrics.
{"title":"Development of Control-Plane Switch Migration Testbed Using Mininet-WiFi for Software-Defined Vehicular Network","authors":"Muhammad Zain ul Abideen, Aung Myo Htut, C. Aswakul","doi":"10.1109/JCSSE58229.2023.10202040","DOIUrl":"https://doi.org/10.1109/JCSSE58229.2023.10202040","url":null,"abstract":"Nowadays, the goal of making the intelligent transportation system smart and efficient is a much more challenging aspect. The target is to optimize the transportation system and provide real-time traffic information to travelers. Currently, in the software-defined vehicular network, multiple domains are involved, and to enhance modern ITS systems to work efficiently, t he collaboration of such distributed domains is required. The challenge is to address the network overheads and congestion problems when considering vehicular mobility. This paper focuses on developing a control-plane switch migration testbed using the Mininet-WiFi emulation platform instead of the NS-2 simulator, which can later be helpful for evaluation of data-plane by considering multiple performance metrics.","PeriodicalId":298838,"journal":{"name":"2023 20th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611699","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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