Pub Date : 2023-01-01DOI: 10.1504/ijwmc.2023.10058729
Abhijit Paul, Priyadarshi Guha, Sukanya Kool
{"title":"An automatic accident detection system for trains using onboard monitoring system","authors":"Abhijit Paul, Priyadarshi Guha, Sukanya Kool","doi":"10.1504/ijwmc.2023.10058729","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.10058729","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66705820","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.10060169
Jawahar Thakur, Deepak Kumar
{"title":"Performance analysis of various shortest-path routing algorithms using RYU controller in SDN","authors":"Jawahar Thakur, Deepak Kumar","doi":"10.1504/ijwmc.2023.10060169","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.10060169","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135262990","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.134659
B. Venkata Krishnaveni, Katam Suresh Reddy, P. Ramana Reddy
{"title":"Performance comparison of TOA-based indoor positioning algorithms using ultra-wideband technology in 3D","authors":"B. Venkata Krishnaveni, Katam Suresh Reddy, P. Ramana Reddy","doi":"10.1504/ijwmc.2023.134659","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.134659","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135448041","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.134674
Hui Hao, Yan Li, Fengrong Zhang, Zhiyi Lv
{"title":"Bifurcation analysis of a predator-prey model with volume-filling mechanism","authors":"Hui Hao, Yan Li, Fengrong Zhang, Zhiyi Lv","doi":"10.1504/ijwmc.2023.134674","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.134674","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135448283","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.133066
V. Anupama, M. Sudheep Elayidom
In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learning-based approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics.
{"title":"Analytical review and study on various course recommendation systems","authors":"V. Anupama, M. Sudheep Elayidom","doi":"10.1504/ijwmc.2023.133066","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.133066","url":null,"abstract":"In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learning-based approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics.","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"846 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135058185","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 : 2023-01-01DOI: 10.1504/IJWMC.2023.10054160
Aounallah Naceur
{"title":"Robust min-norm algorithms for coherent sources DOA estimation based on Toeplitz matrix reconstruction methods","authors":"Aounallah Naceur","doi":"10.1504/IJWMC.2023.10054160","DOIUrl":"https://doi.org/10.1504/IJWMC.2023.10054160","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"24 1","pages":"9-16"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66705513","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.10060172
Deepika Kukreja, Deepak Kumar Sharma
{"title":"ACCO: adaptive congestion control protocol for opportunistic networks","authors":"Deepika Kukreja, Deepak Kumar Sharma","doi":"10.1504/ijwmc.2023.10060172","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.10060172","url":null,"abstract":"","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263004","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.129085
S. Senthil Kumaran, S.P. Balakannan
A cloud is a new paradigm for IoT-based WSN that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD), a novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. IoT, cloud computing and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platforms and is capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real-data set from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.
{"title":"Sensor cloud virtualisation systems for improving performance of IoT-based WSN","authors":"S. Senthil Kumaran, S.P. Balakannan","doi":"10.1504/ijwmc.2023.129085","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.129085","url":null,"abstract":"A cloud is a new paradigm for IoT-based WSN that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD), a novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. IoT, cloud computing and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platforms and is capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real-data set from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135534440","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 : 2023-01-01DOI: 10.1504/ijwmc.2023.131326
Kai Zhang, Yongyan Xu
The promotion of wind power generation can effectively optimise China's energy structure and promote the sustainable development of economy and society. However, the application and promotion of wind power will bring a new problem, that is, it increases the complexity of power system structure and poses a certain hidden danger to the security of power grid. Therefore, Kuramoto model is used to analyse the transient stability of power system, and a comprehensive evaluation system of power system stability is constructed from the two directions of static security and dynamic security. Finally, based on grey correlation analysis, a comprehensive evaluation model (GRA) of power system stability is constructed. The results show that the accuracy of the evaluation model reaches 97.5%. Therefore, the evaluation model can evaluate the stability of power system efficiently and accurately, and avoid large-scale blackouts caused by power system collapse.
{"title":"Research on power system stability evaluation based on grey correlation support","authors":"Kai Zhang, Yongyan Xu","doi":"10.1504/ijwmc.2023.131326","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.131326","url":null,"abstract":"The promotion of wind power generation can effectively optimise China's energy structure and promote the sustainable development of economy and society. However, the application and promotion of wind power will bring a new problem, that is, it increases the complexity of power system structure and poses a certain hidden danger to the security of power grid. Therefore, Kuramoto model is used to analyse the transient stability of power system, and a comprehensive evaluation system of power system stability is constructed from the two directions of static security and dynamic security. Finally, based on grey correlation analysis, a comprehensive evaluation model (GRA) of power system stability is constructed. The results show that the accuracy of the evaluation model reaches 97.5%. Therefore, the evaluation model can evaluate the stability of power system efficiently and accurately, and avoid large-scale blackouts caused by power system collapse.","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135685650","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}
The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm.
{"title":"An enhanced genetic algorithm for computation task offloading in MEC scenario","authors":"Jiacheng Zhao, Wenzao Li, Hantao Liu, Peizhen Yu, Hanyun Li, Zhan Wen","doi":"10.1504/ijwmc.2023.133059","DOIUrl":"https://doi.org/10.1504/ijwmc.2023.133059","url":null,"abstract":"The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm.","PeriodicalId":53709,"journal":{"name":"International Journal of Wireless and Mobile Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135058188","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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