Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop

El uso intensivo de nuevas tecnologías que provocan más interacciones entre los sistemas y las actividades diarias de los usuarios humanos está cambiando el enfoque sobre cómo deben administrarse los recursos de la red. Sin embargo, estos cambios pueden crear desafíos relacionados con el nivel de in...

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Autores:: Alzate Mejía, Néstor
Santos Boada, Germán
Almeida Amazonas, José Roberto de

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2022

Institución:: Universidad Cooperativa de Colombia

Repositorio:: Repositorio UCC

Idioma:

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repository_id_str
dc.title.spa.fl_str_mv	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
title	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
spellingShingle	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop Rendimiento de la red Incertidumbre Detección de multitudes 5G Manejo de recursos Comportamiento humano Network performance Uncertainty Crowdsensing 5G Resource management Human behavior
title_short	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
title_full	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
title_fullStr	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
title_full_unstemmed	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
title_sort	Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop
dc.creator.fl_str_mv	Alzate Mejía, Néstor Santos Boada, Germán Almeida Amazonas, José Roberto de
dc.contributor.author.none.fl_str_mv	Alzate Mejía, Néstor Santos Boada, Germán Almeida Amazonas, José Roberto de
dc.subject.spa.fl_str_mv	Rendimiento de la red Incertidumbre Detección de multitudes 5G Manejo de recursos Comportamiento humano
topic	Rendimiento de la red Incertidumbre Detección de multitudes 5G Manejo de recursos Comportamiento humano Network performance Uncertainty Crowdsensing 5G Resource management Human behavior
dc.subject.other.spa.fl_str_mv	Network performance Uncertainty Crowdsensing 5G Resource management Human behavior
description	El uso intensivo de nuevas tecnologías que provocan más interacciones entre los sistemas y las actividades diarias de los usuarios humanos está cambiando el enfoque sobre cómo deben administrarse los recursos de la red. Sin embargo, estos cambios pueden crear desafíos relacionados con el nivel de incertidumbre que las personas introducen en el sistema. En este contexto, esta investigación busca determinar si la incertidumbre de las personas influye en el desempeño de la red y qué tan significativo es su impacto. Para estos fines, un estudio de caso simulado de una aplicación Vehicle for Hire diseñada para ejecutarse en una red que se divide en una red de quinta generación (5G). Las simulaciones compararon las tasas de caída de llamadas en varios entornos configurados para representar diferentes niveles de incertidumbre, introduciendo alteraciones aleatorias en la planificación de canales libres reservados para el proceso de traspaso. Los resultados de la simulación revelan que la incertidumbre introducida específicamente por las personas ejerce un alto impacto negativo en el rendimiento de la red, lo que evidencia la necesidad de desarrollar un algoritmo que considere esta incertidumbre al administrar los recursos dentro del núcleo de la red 5G.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-09-20T22:56:37Z
dc.date.available.none.fl_str_mv	2022-09-20T22:56:37Z
dc.date.issued.none.fl_str_mv	2022-03
dc.type.none.fl_str_mv	Artículos Científicos
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dc.identifier.issn.spa.fl_str_mv	2331-1916
dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12494/46456
dc.identifier.bibliographicCitation.spa.fl_str_mv	Néstor Alzate-Mejía, Germán Santos-Boada & José Roberto de Almeida Amazonas (2022) Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop, Cogent Engineering, 9:1, DOI: 10.1080/23311916.2022.2062878
identifier_str_mv	2331-1916 Néstor Alzate-Mejía, Germán Santos-Boada & José Roberto de Almeida Amazonas (2022) Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop, Cogent Engineering, 9:1, DOI: 10.1080/23311916.2022.2062878
url	https://hdl.handle.net/20.500.12494/46456
dc.relation.isversionof.spa.fl_str_mv	https://doi.org/10.1080/23311916.2022.2062878
dc.relation.ispartofjournal.spa.fl_str_mv	Cogent Engineering
dc.relation.references.spa.fl_str_mv	Abualsaud, Khalid, Elfouly, Tarek, Khattab, Tamer, Yaacoub, Elias, Ismail, Loay Sabry, Ahmed, Mohamed Hossan, Guizani, Mohsen et al. (2019). A survey on mobile crowd-sensing and its applications in the IoT era. IEEE Access, 7, 3855–18. Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655. Agiwal, M., Saxena, N., & Roy, A. (2019). Towards connected living: 5G enabled internet of things (IoT). IETE Technical Review, 36(2), 190–202. Alzate-Mejia, N., Santos-Boada, G., & de Almeida-amazonas, J. R. (2021). Decision-making under uncertainty for the deployment of future hyperconnected networks: A survey. Sensors, 21(11), 3791. Arooj, Ansif, Farooq, Muhammad Shoaib, Umer, Tariq, Rasool, Ghulam, & Wang, Bo. (2020). Cyber physical and social networks in IoV (CPSN-IoV): A multimodal architecture in edge-based networks for optimal route selection using 5G technologies. IEEE Access, 8, 33609–33630. Arshad, Rabe, Elsawy, Hesham, Sorour, Sameh, Al-Naffouri, Tareq, & Alouini, Mohamed-Slim. (2016). Handover management in 5G and Beyond: A topology aware skipping approach. IEEE Access, 4, 9073–9081. Arshad, R., ElSawy, H., Sorour, S., Al-Naffouri, T. Y., & Alouini, M. (2017). Velocity-aware handover management in two-tier cellular networks. IEEE Transactions on Wireless Communications, 16(3), 1851–1867. Ghosh, Amitabha, Maeder, Andreas, Baker, Matthew, & Chandramouli, Devaki. (2019). 5G evolution: A view on 5G cellular technology beyond 3GPP release 15. IEEE Access, 7, 127639–127651. Gupta, Akhil, & Jha, Rakesh Kumar. (2015). A survey of 5G network: Architecture and emerging technologies. IEEE Access, 3, 1206–1232. Ismail, S., Shah, K., Reza, H., Marsh, R., & Grant, E. (2021). Toward management of uncertainty in self-adaptive software systems: IoT case study. Computers, 10(3), 27. Kong, X., Liu, X., Jedari, B., Li, M., Wan, L., & Xia, F. (2019). Mobile crowdsourcing in smart cities: Technologies, applications, and future challenges. IEEE Internet of Things Journal, 6(5), 8095–8113. Luu, Q.-T., Kerboeuf, S., & Kieffer, M. (2021). Uncertainty-aware resource provisioning for network slicing. IEEE Transactions on Network and Service Management, 18(1), 79–93. Mazied, EmadelDin, ElNainay, Mustafa, Abdel-Rahman, Mohammad, Midkiff, Scott, Rizk, Mohamed, Rakha, Hesham, & MacKenzie, Allen. (2019). The wireless control plane: An overview and directions for future research. Journal of Network and Computer Applications, 126, 104–122. Merwaday, A., & Güvenç, İ. (2016). Handover count based velocity estimation and mobility state detection in dense hetNets. IEEE Trans. Wirel. Commun, 15(7), 4673–4688. Moltafet, Mohammad, Parsaeefard, Saeedeh, Javan, Mohammad Reza, & Mokari, Nader. (2019). Robust radio resource allocation in MISO-SCMA assisted C-RAN in 5G networks. IEEE Transactions on Vehicular Technology, 68(6), 5758–5768 . Phuttharak, Jurairat, & Loke, Seng. (2019). A review of mobile crowdsourcing architectures and challenges: Toward crowd-empowered internet-of-things. IEEE Access, 7, 304–324 Polese, M., Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2017). Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 2069–2084. Rueda, Diego F., Calle, Eusebi, & Marzo,Jose Luis. (2017). Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements. Journal of Network and Systems Management, 25(2), 269–289 . Silva, Thiago H., Viana, Aline Carneiro, Benevenuto, Fabrício, Villas, Leandro, Salles, Juliana, Loureiro, Antonio, Quercia, Daniele et al. (2019). Urban computing leveraging location-based social network data: A survey. ACM Comput. Surv, 52(1 1–39). . Vidhya, R., Karthik, Pillapalem, & Jamadagni, Satish, “Anticipatory QoE mechanisms for 5G data analytics,” in 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020 January 7-11 (IEEE) Bengaluru, India, January. 2020, pp. 523–526, . Wan, Xiangpeng, Ghazzai, Hakim, & Massoud, Yehia. (2019). Mobile crowdsourcing for intelligent transportation systems: Real-time navigation in urban areas. IEEE Access, 7, 136995–137009. Zhang, Hongtao, Huang, Wanqing, & Liu, Yi. (2019). Handover probability analysis of anchor-based multi-connectivity in 5G user-centric network. IEEE Wireless Communications Letters, 8(2), 396–399. Zhao, T., Yang, Y., Wang, E., Mumtaz, S., & Cheng, X. (2021). Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems, 36(9), 4936–4961. Zheng, Z., Mumtaz, S., Khosravi, M. R., & Menon, V. G. (2021). Linked data processing for human-in-the-loop in cyber–physical systems. IEEE Transactions on Computational Social Systems, 8(5), 1238–1248.
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spelling	Alzate Mejía, NéstorSantos Boada, GermánAlmeida Amazonas, José Roberto de9/12022-09-20T22:56:37Z2022-09-20T22:56:37Z2022-032331-1916https://hdl.handle.net/20.500.12494/46456Néstor Alzate-Mejía, Germán Santos-Boada & José Roberto de Almeida Amazonas (2022) Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop, Cogent Engineering, 9:1, DOI: 10.1080/23311916.2022.2062878El uso intensivo de nuevas tecnologías que provocan más interacciones entre los sistemas y las actividades diarias de los usuarios humanos está cambiando el enfoque sobre cómo deben administrarse los recursos de la red. Sin embargo, estos cambios pueden crear desafíos relacionados con el nivel de incertidumbre que las personas introducen en el sistema. En este contexto, esta investigación busca determinar si la incertidumbre de las personas influye en el desempeño de la red y qué tan significativo es su impacto. Para estos fines, un estudio de caso simulado de una aplicación Vehicle for Hire diseñada para ejecutarse en una red que se divide en una red de quinta generación (5G). Las simulaciones compararon las tasas de caída de llamadas en varios entornos configurados para representar diferentes niveles de incertidumbre, introduciendo alteraciones aleatorias en la planificación de canales libres reservados para el proceso de traspaso. Los resultados de la simulación revelan que la incertidumbre introducida específicamente por las personas ejerce un alto impacto negativo en el rendimiento de la red, lo que evidencia la necesidad de desarrollar un algoritmo que considere esta incertidumbre al administrar los recursos dentro del núcleo de la red 5G.The intensive use of new technologies that cause more interactions between systems and the daily activities of human users is changing the focus on how network re- sources should be managed. However, these changes can create challenges related to the level of uncertainty that people introduce to the system. In this context, this research study seeks to determine whether people’s uncertainty influences network performance and how significant its impact is. For these purposes, a simulated case study of a Vehicle for Hire application designed to run over a network slicing of a fifth-generation (5G) network. The simulations compared call drop rates in several settings configured to represent different levels of uncertainty, introducing random alterations to free channel planning reserved for the handover process. The simulation results reveal that the uncertainty specifically introduced by people exerts a high negative impact on network performance, evidencing the need to develop an algorithm that considers this uncertainty when managing resources within the 5G network core.https://orcid.org/0000-0003-3287-643Xhttps://orcid.org/0000-0002-9528-2287https://orcid.org/0000-0001-9589-0373nestor.alzatem@campusucc.edu.co1 - 18 p.Universidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, CaliIngeniería de SistemasCalihttps://doi.org/10.1080/23311916.2022.2062878Cogent EngineeringAbualsaud, Khalid, Elfouly, Tarek, Khattab, Tamer, Yaacoub, Elias, Ismail, Loay Sabry, Ahmed, Mohamed Hossan, Guizani, Mohsen et al. (2019). A survey on mobile crowd-sensing and its applications in the IoT era. IEEE Access, 7, 3855–18.Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655.Agiwal, M., Saxena, N., & Roy, A. (2019). Towards connected living: 5G enabled internet of things (IoT). IETE Technical Review, 36(2), 190–202.Alzate-Mejia, N., Santos-Boada, G., & de Almeida-amazonas, J. R. (2021). Decision-making under uncertainty for the deployment of future hyperconnected networks: A survey. Sensors, 21(11), 3791.Arooj, Ansif, Farooq, Muhammad Shoaib, Umer, Tariq, Rasool, Ghulam, & Wang, Bo. (2020). Cyber physical and social networks in IoV (CPSN-IoV): A multimodal architecture in edge-based networks for optimal route selection using 5G technologies. IEEE Access, 8, 33609–33630.Arshad, Rabe, Elsawy, Hesham, Sorour, Sameh, Al-Naffouri, Tareq, & Alouini, Mohamed-Slim. (2016). Handover management in 5G and Beyond: A topology aware skipping approach. IEEE Access, 4, 9073–9081.Arshad, R., ElSawy, H., Sorour, S., Al-Naffouri, T. Y., & Alouini, M. (2017). Velocity-aware handover management in two-tier cellular networks. IEEE Transactions on Wireless Communications, 16(3), 1851–1867.Ghosh, Amitabha, Maeder, Andreas, Baker, Matthew, & Chandramouli, Devaki. (2019). 5G evolution: A view on 5G cellular technology beyond 3GPP release 15. IEEE Access, 7, 127639–127651.Gupta, Akhil, & Jha, Rakesh Kumar. (2015). A survey of 5G network: Architecture and emerging technologies. IEEE Access, 3, 1206–1232.Ismail, S., Shah, K., Reza, H., Marsh, R., & Grant, E. (2021). Toward management of uncertainty in self-adaptive software systems: IoT case study. Computers, 10(3), 27.Kong, X., Liu, X., Jedari, B., Li, M., Wan, L., & Xia, F. (2019). Mobile crowdsourcing in smart cities: Technologies, applications, and future challenges. IEEE Internet of Things Journal, 6(5), 8095–8113.Luu, Q.-T., Kerboeuf, S., & Kieffer, M. (2021). Uncertainty-aware resource provisioning for network slicing. IEEE Transactions on Network and Service Management, 18(1), 79–93.Mazied, EmadelDin, ElNainay, Mustafa, Abdel-Rahman, Mohammad, Midkiff, Scott, Rizk, Mohamed, Rakha, Hesham, & MacKenzie, Allen. (2019). The wireless control plane: An overview and directions for future research. Journal of Network and Computer Applications, 126, 104–122.Merwaday, A., & Güvenç, İ. (2016). Handover count based velocity estimation and mobility state detection in dense hetNets. IEEE Trans. Wirel. Commun, 15(7), 4673–4688.Moltafet, Mohammad, Parsaeefard, Saeedeh, Javan, Mohammad Reza, & Mokari, Nader. (2019). Robust radio resource allocation in MISO-SCMA assisted C-RAN in 5G networks. IEEE Transactions on Vehicular Technology, 68(6), 5758–5768 .Phuttharak, Jurairat, & Loke, Seng. (2019). A review of mobile crowdsourcing architectures and challenges: Toward crowd-empowered internet-of-things. IEEE Access, 7, 304–324Polese, M., Giordani, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2017). Improved handover through dual connectivity in 5G mmWave mobile networks. IEEE Journal on Selected Areas in Communications, 35(9), 2069–2084.Rueda, Diego F., Calle, Eusebi, & Marzo,Jose Luis. (2017). Robustness comparison of 15 real telecommunication networks: Structural and centrality measurements. Journal of Network and Systems Management, 25(2), 269–289 .Silva, Thiago H., Viana, Aline Carneiro, Benevenuto, Fabrício, Villas, Leandro, Salles, Juliana, Loureiro, Antonio, Quercia, Daniele et al. (2019). Urban computing leveraging location-based social network data: A survey. ACM Comput. Surv, 52(1 1–39). .Vidhya, R., Karthik, Pillapalem, & Jamadagni, Satish, “Anticipatory QoE mechanisms for 5G data analytics,” in 2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020 January 7-11 (IEEE) Bengaluru, India, January. 2020, pp. 523–526, .Wan, Xiangpeng, Ghazzai, Hakim, & Massoud, Yehia. (2019). Mobile crowdsourcing for intelligent transportation systems: Real-time navigation in urban areas. IEEE Access, 7, 136995–137009.Zhang, Hongtao, Huang, Wanqing, & Liu, Yi. (2019). Handover probability analysis of anchor-based multi-connectivity in 5G user-centric network. IEEE Wireless Communications Letters, 8(2), 396–399.Zhao, T., Yang, Y., Wang, E., Mumtaz, S., & Cheng, X. (2021). Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems, 36(9), 4936–4961.Zheng, Z., Mumtaz, S., Khosravi, M. R., & Menon, V. G. (2021). Linked data processing for human-in-the-loop in cyber–physical systems. 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Verification of performance degradation in a telecommunications system due to the uncertainty of human users in the loop

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