Pub Date : 2022-01-01DOI: 10.13052/jicts2245-800X.1031
Mohammed Kasri;Marouane Birjali;Mohamed Nabil;Abderrahim Beni-Hssane;Anas El-Ansari;Mohamed El Fissaoui
Natural Language Processing problems generally require the use of pretrained distributed word representations to be solved with deep learning models. However, distributed representations usually rely on contextual information which prevents them from learning all the important word characteristics. The task of sentiment analysis suffers from such a problem because sentiment information is ignored during the process of learning word embeddings. The performance of sentiment analysis can be affected since two words with similar vectors may have opposite sentiment orientations. The present paper introduces a novel model called Continuous Sentiment Contextualized Vectors (CSCV) to address this problem. The proposed model can learn word sentiment embedding using its surrounding context words. It uses Continuous Bag-of-Words (CBOW) model to deal with the context and sentiment lexicons to identify sentiment. Existing pre-trained vectors are combined then with the obtained sentiment vectors using Principal component analysis (PCA) to enhance their quality. The experiments show that: (1) CSCV vectors can be used to enhance any pre-trained word vectors; (2) The result vectors strongly alleviate the problem of similar words with opposite polarities; (3) The performance of sentiment classification is improved by applying this approach.
{"title":"Refining Word Embeddings with Sentiment Information for Sentiment Analysis","authors":"Mohammed Kasri;Marouane Birjali;Mohamed Nabil;Abderrahim Beni-Hssane;Anas El-Ansari;Mohamed El Fissaoui","doi":"10.13052/jicts2245-800X.1031","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1031","url":null,"abstract":"Natural Language Processing problems generally require the use of pretrained distributed word representations to be solved with deep learning models. However, distributed representations usually rely on contextual information which prevents them from learning all the important word characteristics. The task of sentiment analysis suffers from such a problem because sentiment information is ignored during the process of learning word embeddings. The performance of sentiment analysis can be affected since two words with similar vectors may have opposite sentiment orientations. The present paper introduces a novel model called Continuous Sentiment Contextualized Vectors (CSCV) to address this problem. The proposed model can learn word sentiment embedding using its surrounding context words. It uses Continuous Bag-of-Words (CBOW) model to deal with the context and sentiment lexicons to identify sentiment. Existing pre-trained vectors are combined then with the obtained sentiment vectors using Principal component analysis (PCA) to enhance their quality. The experiments show that: (1) CSCV vectors can be used to enhance any pre-trained word vectors; (2) The result vectors strongly alleviate the problem of similar words with opposite polarities; (3) The performance of sentiment classification is improved by applying this approach.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 3","pages":"353-382"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10255395/10255396.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67890239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a new MAC (Medium Access Control) protocol, called Hybrid ALOHA (H-ALOHA), which is a combination of two existing protocols: Pure ALOHA (P-ALOHA) protocol and Slotted ALOHA (S-ALOHA) protocol. The idea behind it is to design a MAC protocol that could meet some specific requirements in wireless networks, such as reducing energy consumption, delay minimization, and increasing the throughput. To the best of our knowledge, the S-ALOHA protocol is an improved version of P-ALOHA. However, during one single transmission scenario, P-ALOHA works better than S-ALOHA in terms of energy consumption and packet delivery. Motivated by that fact, we combine these two protocols, resulting in a hybrid ALOHA. A finite-state Markovian model is proposed to study the steady-state performance of H-ALOHA including normalized throughput, backlogged throughput, access delay, backlogged delay, and energy consumption. The proposed hybrid protocol has been compared with the S-ALOHA protocol. The simulation results show that the proposed hybrid protocol outperforms all ALOHA protocols. On average, the proposed protocol outperforms the S-ALOHA protocol by 60% in terms of normalized throughput, by 15% in terms of access delay, and by 23% in terms of total energy consumed during the transmission process.
{"title":"Random Access Mechanism Enhancement Based on a Hybrid ALOHA Protocol Using an Analytical Model","authors":"Abdessamad Bellouch;Abdellah Zaaloul;Abdelkrim Haqiq","doi":"10.13052/jicts2245-800X.1032","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1032","url":null,"abstract":"In this paper, we present a new MAC (Medium Access Control) protocol, called Hybrid ALOHA (H-ALOHA), which is a combination of two existing protocols: Pure ALOHA (P-ALOHA) protocol and Slotted ALOHA (S-ALOHA) protocol. The idea behind it is to design a MAC protocol that could meet some specific requirements in wireless networks, such as reducing energy consumption, delay minimization, and increasing the throughput. To the best of our knowledge, the S-ALOHA protocol is an improved version of P-ALOHA. However, during one single transmission scenario, P-ALOHA works better than S-ALOHA in terms of energy consumption and packet delivery. Motivated by that fact, we combine these two protocols, resulting in a hybrid ALOHA. A finite-state Markovian model is proposed to study the steady-state performance of H-ALOHA including normalized throughput, backlogged throughput, access delay, backlogged delay, and energy consumption. The proposed hybrid protocol has been compared with the S-ALOHA protocol. The simulation results show that the proposed hybrid protocol outperforms all ALOHA protocols. On average, the proposed protocol outperforms the S-ALOHA protocol by 60% in terms of normalized throughput, by 15% in terms of access delay, and by 23% in terms of total energy consumed during the transmission process.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 3","pages":"383-409"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10255395/10255400.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67890242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.13052/jicts2245-800X.1043
Gagan Kumar;Vinay Chopra
Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity's ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data's efficacy, as a result, the test data's adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829–2008 test documentation in entire testing process.
{"title":"Hybrid Approach for Automated Test Data Generation","authors":"Gagan Kumar;Vinay Chopra","doi":"10.13052/jicts2245-800X.1043","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1043","url":null,"abstract":"Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity's ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data's efficacy, as a result, the test data's adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829–2008 test documentation in entire testing process.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 4","pages":"531-561"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10254731/10255394.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68009002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.13052/jicts2245-800X.1042
H. L. Gururaj;H. Lakshmi;B. C. Soundarya;Francesco Flammini;V. Janhavi
In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset.
{"title":"Machine Learning-Based Approach for Fake News Detection","authors":"H. L. Gururaj;H. Lakshmi;B. C. Soundarya;Francesco Flammini;V. Janhavi","doi":"10.13052/jicts2245-800X.1042","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1042","url":null,"abstract":"In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 4","pages":"509-530"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10254731/10255417.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68009003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.13052/jicts2245-800X.10212
Hakim El Massari;Zineb Sabouri;Sajida Mhammedi;Noreddine Gherabi
Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.
{"title":"Diabetes Prediction Using Machine Learning Algorithms and Ontology","authors":"Hakim El Massari;Zineb Sabouri;Sajida Mhammedi;Noreddine Gherabi","doi":"10.13052/jicts2245-800X.10212","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.10212","url":null,"abstract":"Diabetes is one of the chronic diseases, which is increasing from year to year. The problems begin when diabetes is not detected at an early phase and diagnosed properly at the appropriate time. Different machine learning techniques, as well as ontology-based ML techniques, have recently played an important role in medical science by developing an automated system that can detect diabetes patients. This paper provides a comparative study and review of the most popular machine learning techniques and ontology-based Machine Learning classification. Various types of classification algorithms were considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and Decision Tree. The results are evaluated based on performance metrics like Recall, Accuracy, Precision, and F-Measure that are derived from the confusion matrix. The experimental results showed that the best accuracy goes for ontology classifiers and SVM.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 2","pages":"319-337"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10254727/10255389.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68110508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on 10 selected papers of the workshop “6G Knowledge Lab Opening and 36th GISFI Workshop” held on 21–22 December 2020, organized jointly by the CTIF Global Capsule (CGC) and the Global ICT Standardisation Forum for India (GISFI), the Special Issue has been divided in 2 parts, consisting of 5 papers each.
根据2020年12月21日至22日由CTIF Global Capsule（CGC）和印度全球ICT标准化论坛（GISFI）联合组织的研讨会“6G知识实验室开放和第36届GISFI研讨会”的10篇精选论文，特刊分为2部分，各5篇。
{"title":"Editorial Foreword","authors":"Ramjee Prasad;Anand R. Prasad","doi":"","DOIUrl":"https://doi.org/","url":null,"abstract":"Based on 10 selected papers of the workshop “6G Knowledge Lab Opening and 36th GISFI Workshop” held on 21–22 December 2020, organized jointly by the CTIF Global Capsule (CGC) and the Global ICT Standardisation Forum for India (GISFI), the Special Issue has been divided in 2 parts, consisting of 5 papers each.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10255387/10255426.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68134609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.13052/jicts2245-800X.10210
Khalid Qbouche;Khadija Rhoulami
Due to the rapid urbanization of the world, the issue of daily movement has become an important topic. It examines the daily movements of people and analyzes the behavior of individuals. This system is closely related to the urban area, especially traffic. This work will provide a mixed model of daily mobility and a person's shifting condition. Bottom-up techniques, such as Markov Chain and Multi-agent Systems, allow the creation of individual or group displacements. Bayesian Belief Network combined with Markov Chain allow for designing and managing individual behavior displacements.
{"title":"Simulation Daily Mobility in Rabat Region Using Multi-Agent Systems Models","authors":"Khalid Qbouche;Khadija Rhoulami","doi":"10.13052/jicts2245-800X.10210","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.10210","url":null,"abstract":"Due to the rapid urbanization of the world, the issue of daily movement has become an important topic. It examines the daily movements of people and analyzes the behavior of individuals. This system is closely related to the urban area, especially traffic. This work will provide a mixed model of daily mobility and a person's shifting condition. Bottom-up techniques, such as Markov Chain and Multi-agent Systems, allow the creation of individual or group displacements. Bayesian Belief Network combined with Markov Chain allow for designing and managing individual behavior displacements.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 2","pages":"293-303"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10254727/10255415.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68110511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor's readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.
{"title":"Basic Activity Recognition from Wearable Sensors Using a Lightweight Deep Neural Network","authors":"Zakaria Benhaili;Youness Abouqora;Youssef Balouki;Lahcen Moumoun","doi":"10.13052/jicts2245-800X.1028","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1028","url":null,"abstract":"The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor's readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"10 2","pages":"241-260"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10254727/10255406.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68111657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the first 5G networks are being deployed across the world, new services enabled by the superior performance of 5G in terms of throughput, latency and reliability are emerging. Connected and Automated Mobility (CAM) services are perhaps among the most demanding applications that 5G networks will have to support and their deployment, performance and potential for improvement has been well investigated over the past few years. However, CAM operation in multi-operator environments and the inevitable inter-PLMN handover caused by the inherent mobility of CAM services have not been studied in length. Moreover, the multiple domains, multi-vendor components and inherent high mobility of the cross-border vehicular environment, introduce multiple challenges in terms of network management and dynamic slicing, making Zero-touch network and Service Management (ZSM) solutions an attractive alternative for these environments. The work presented in this study attempts to analyse the requirements for cross-border CAM operation for the five main CAM use cases selected by 3GPP, based on input from key European stakeholders (Network Operators, vendors, Automotive Manufacturers etc.). A detailed analysis and categorization into four categories of the main challenges for cross-border CAM service provisioning is performed, namely Telecommunication, Application, Security/Privacy and Regulatory issues, while potential solutions based on existing and upcoming technological enablers are discussed for each of them. The role of standardization and relevant regulatory and administrative bodies is analysed, leading to insights regarding the most promising future research directions in the field of cross-border CAM services.
{"title":"Inter-PLMN Mobility Management Challenges for Supporting Cross-Border Connected and Automated Mobility (CAM) Over 5G Networks","authors":"Konstantinos Trichias;Panagiotis Demestichas;Nikolaos Mitrou","doi":"10.13052/jicts2245-800X.924","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.924","url":null,"abstract":"As the first 5G networks are being deployed across the world, new services enabled by the superior performance of 5G in terms of throughput, latency and reliability are emerging. Connected and Automated Mobility (CAM) services are perhaps among the most demanding applications that 5G networks will have to support and their deployment, performance and potential for improvement has been well investigated over the past few years. However, CAM operation in multi-operator environments and the inevitable inter-PLMN handover caused by the inherent mobility of CAM services have not been studied in length. Moreover, the multiple domains, multi-vendor components and inherent high mobility of the cross-border vehicular environment, introduce multiple challenges in terms of network management and dynamic slicing, making Zero-touch network and Service Management (ZSM) solutions an attractive alternative for these environments. The work presented in this study attempts to analyse the requirements for cross-border CAM operation for the five main CAM use cases selected by 3GPP, based on input from key European stakeholders (Network Operators, vendors, Automotive Manufacturers etc.). A detailed analysis and categorization into four categories of the main challenges for cross-border CAM service provisioning is performed, namely Telecommunication, Application, Security/Privacy and Regulatory issues, while potential solutions based on existing and upcoming technological enablers are discussed for each of them. The role of standardization and relevant regulatory and administrative bodies is analysed, leading to insights regarding the most promising future research directions in the field of cross-border CAM services.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"9 2","pages":"113-146"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10255460/10255486.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68121010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.13052/jicts2245-800X.932
Nidhi;Bahram Khan;Albena Mihovska;Ramjee Prasad;Fernando J. Velez
Mobile networks have always been an indispensable part of a fully connected digital society. The industry and academia have joined hands to develop technologies for the anticipated future wireless communication. The predicted Key Performance Indicators (KPIs) and use cases for the 6G networks have raised the bar high. 6G networks are developing to provide the required infrastructure for many new devices and services. The 6G networks are conceptualized to partially inherit 5G technologies and standards but they will open the ground for innovations. This study provides the vision and requirements for beyond 5G (B5G) networks and emphasizes our vision on the required standards to reach a fully functional and interoperable 6G era in general. We highlight various KPIs and enabling technologies for the B5G networks. In addition, standardization activities and initiatives concerning challenges in the use of spectrum are discussed in detail.
{"title":"Trends in Standardization Towards 6G","authors":"Nidhi;Bahram Khan;Albena Mihovska;Ramjee Prasad;Fernando J. Velez","doi":"10.13052/jicts2245-800X.932","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.932","url":null,"abstract":"Mobile networks have always been an indispensable part of a fully connected digital society. The industry and academia have joined hands to develop technologies for the anticipated future wireless communication. The predicted Key Performance Indicators (KPIs) and use cases for the 6G networks have raised the bar high. 6G networks are developing to provide the required infrastructure for many new devices and services. The 6G networks are conceptualized to partially inherit 5G technologies and standards but they will open the ground for innovations. This study provides the vision and requirements for beyond 5G (B5G) networks and emphasizes our vision on the required standards to reach a fully functional and interoperable 6G era in general. We highlight various KPIs and enabling technologies for the B5G networks. In addition, standardization activities and initiatives concerning challenges in the use of spectrum are discussed in detail.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"9 3","pages":"327-348"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10255453/10266733.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68097851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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