Revisão Sistemática das Aplicações Imersivas com base nas Tecnologias Habilitadoras B5G/6G, MEC e IA
Resumo
Esta revisão sistemática tem como objetivo principal identificar estudos que abordem questões relacionadas às aplicações imersivas com base nas tecnologias habilitadoras B5G/6G, MEC e IA. A motivação é compreender os avanços realizados nesse campo e identificar soluções emergentes e lacunas de conhecimento. Os resultados obtidos indicam a predominância do uso da técnica de Aprendizado por Reforço Profundo para abordar soluções de rede baseadas na computação de borda de múltiplo acesso, a fim de viabilizar as aplicações imersivas. Espera-se que esta revisão sistemática contribua para uma melhor compreensão do estado atual das aplicações imersivas e para pesquisas futuras relacionadas ao tema.
Referências
Ahmad, M., Jafri, S. U., Ikram, A., Qasmi, W. N. A., Nawazish, M. A., Uzmi, Z. A., and Qazi, Z. A. (2020). A Low Latency and Consistent Cellular Control Plane. In Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication, pages 648–661, Virtual Event USA. ACM
Ateya, A. A., Muthanna, A., Koucheryavy, A., Maleh, Y., and El-Latif, A. A. A. (2023). Energy efficient offloading scheme for MEC-based augmented reality system. Cluster Comput, 26(1):789–806.
Binucci, F., Banelli, P., Di Lorenzo, P., and Barbarossa, S. (2022). Adaptive resource optimization for edge inference with goal-oriented communications. EURASIP J. Adv. Signal Process., 2022(1):123.
Bojović, B., Lagén, S., Koutlia, K., Zhang, X., Wang, P., and Yu, L. (2023). Enhancing 5G QoS Management for XR Traffic Through XR Loopback Mechanism. IEEE J. Select. Areas Commun., 41(6):1772–1786.
Cardoso, L. F. d. S., Kimura, B. Y. L., and Zorzal, E. R. (2023). Towards augmented and mixed reality on future mobile networks. Multimedia Tools and Applications, pages 1–36.
Chen, M., Liu, W., Wang, T., Liu, A., and Zeng, Z. (2021). Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach. Computer Networks, 195:108186.
Chu, N. H., Hoang, D. T., Nguyen, D. N., Phan, K. T., Dutkiewicz, E., Niyato, D., and Shu, T. (2023). MetaSlicing: A Novel Resource Allocation Framework for Metaverse. IEEE Trans. on Mobile Comput., pages 1–18.
Chukhno, O., Galinina, O., Andreev, S., Molinaro, A., and Iera, A. (2023). User and Content Dynamics of Edge-Aided Immersive Reality Services. IEEE Netw. Lett., pages 1–1.
Du, H., Wang, J., Niyato, D., Kang, J., Xiong, Z., Shen, X. S., and Kim, D. I. (2022). Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services. IEEE Network, pages 1–8.
Dutta, S., Roy, D., and Das, G. (2023). Modified Split-Rendering Architecture to enable AI-assisted Application-aware MAC for XR Slice. IEEE Netw. Lett., pages 1–1.
Hsu, C.-F., Hung, T.-H., and Hsu, C.-H. (2022). Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV. ACM Trans. Multimedia Comput. Commun. Appl., 18(1):1–25.
Kitchenham, B., Charters, S., et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Koutlia, K., Bojovic, B., Lagén, S., Zhang, X., Wang, P., and Liu, J. (2023). System analysis of QoS schedulers for XR traffic in 5G NR. Simulation Modelling Practice and Theory, 125:102745.
Lagen, S., Bojovic, B., Koutlia, K., Zhang, X., Wang, P., and Qu, Q. (2023). QoS Management for XR Traffic in 5G NR: A Multi-Layer System View & End-to-End Evaluation. IEEE Commun. Mag., pages 1–7.
Li, J., Shang, Y., Qin, M., Yang, Q., Cheng, N., Gao, W., and Kwak, K. S. (2022a). Multiobjective Oriented Task Scheduling in Heterogeneous Mobile Edge Computing Networks. IEEE Trans. Veh. Technol., 71(8):8955–8966.
Li, Y., Wang, T., Wu, Y., and Jia, W. (2022b). Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing. Digital Communications and Networks, 8(3):247–256.
Liu, X., Deng, Y., Han, C., and Renzo, M. D. (2022). Learning-Based Prediction, Rendering and Transmission for Interactive Virtual Reality in RIS-Assisted Terahertz Networks. IEEE J. Select. Areas Commun., 40(2):710–724.
Liubogoshchev, M., Ragimova, K., Lyakhov, A., Tang, S., and Khorov, E. (2021). Adaptive Cloud-Based Extended Reality: Modeling and Optimization. IEEE Access, 9:35287–35299.
Maier, M., Ebrahimzadeh, A., Rostami, S., and Beniiche, A. (2020). The Internet of No Things: Making the Internet Disappear and ”See the Invisible”. IEEE Commun. Mag., 58(11):76–82.
Nguyen, T.-V., Dao, N.-N., Dat Tuong, V., Noh, W., and Cho, S. (2022). User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks. IEEE Internet Things J., 9(5):3251–3269.
Passas, V., Makris, N., Wang, Y., Apostolaras, A., Mpatziakas, A., Drosou, A., Korakis, T., and Tzovaras, D. (2022). Artificial Intelligence for network function autoscaling in a cloud-native 5G network. Computers and Electrical Engineering, 103:108327.
Perez, P., Corregidor, D., Garrido, E., Benito, I., Gonzalez-Sosa, E., Cabrera, J., Berjon, D., Diaz, C., Moran, F., Garcia, N., Igual, J., and Ruiz, J. (2022). Live Free-Viewpoint Video in Immersive Media Production Over 5G Networks. IEEE Trans. on Broadcast., 68(2):439–450.
Picano, B. and Fantacci, R. (2023). A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems. ACM Trans. Internet Things, 4(2):1–20.
Qian, P., Huynh, V. S. H., Wang, N., Anmulwar, S., Mi, D., and Tafazolli, R. R. (2022). Remote Production for Live Holographic Teleportation Applications in 5G Networks. IEEE Trans. on Broadcast., 68(2):451–463.
Sadiki, A., Bentahar, J., Dssouli, R., En-Nouaary, A., and Otrok, H. (2023). Deep reinforcement learning for the computation offloading in MIMO-based edge computing. 141:103080.
Sehad, N., Cherif, B., Khadraoui, I., Hamidouche, W., Bader, F., Jantti, R., and Debbah, M. (2023). Locomotion-Based UAV Control Toward the Internet of Senses. IEEE Trans. Circuits Syst. II, 70(5):1804–1808.
Siriwardhana, Y., Porambage, P., Liyanage, M., and Ylianttila, M. (2021). A survey on mobile augmented reality with 5g mobile edge computing: Architectures, applications, and technical aspects. IEEE Communications Surveys Tutorials, 23(2):1160–1192.
Song, L., Hu, X., Zhang, G., Spachos, P., Plataniotis, K. N., and Wu, H. (2022). Networking Systems of AI: On the Convergence of Computing and Communications. IEEE Internet Things J., 9(20):20352–20381.
Trinh, B. and Muntean, G.-M. (2023). A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems. IEEE Trans. Veh. Technol., 72(1):1254–1264.
Wang, C., Yu, X., Xu, L., Wang, Z., and Wang, W. (2023). Multimodal semantic communication accelerated bidirectional caching for 6G MEC. Future Generation Computer Systems, 140:225–237.
Wang, Y., Chen, X., Chen, Y., and Du, S. (2021). Resource allocation algorithm for MEC based on Deep Reinforcement Learning. In 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), pages 1–6, Austin, TX, USA. IEEE.
Wazlawick, R. S. (2009). Metodologia de pesquisa para ciência da computação, volume 2. Elsevier.
Yazici, I., Shayea, I., and Din, J. (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, 44:101455.
Yu, W., Chua, T. J., and Zhao, J. (2023). Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse Over Wireless Communications. IEEE J. Select. Areas Commun., 41(7):2138–2157.
Zhang, L., Zhuge, S., Wang, Y., Xu, H., and Sun, E. (2023). Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach. J Ambient Intell Human Comput, 14(6):6603–6621.
Ateya, A. A., Muthanna, A., Koucheryavy, A., Maleh, Y., and El-Latif, A. A. A. (2023). Energy efficient offloading scheme for MEC-based augmented reality system. Cluster Comput, 26(1):789–806.
Binucci, F., Banelli, P., Di Lorenzo, P., and Barbarossa, S. (2022). Adaptive resource optimization for edge inference with goal-oriented communications. EURASIP J. Adv. Signal Process., 2022(1):123.
Bojović, B., Lagén, S., Koutlia, K., Zhang, X., Wang, P., and Yu, L. (2023). Enhancing 5G QoS Management for XR Traffic Through XR Loopback Mechanism. IEEE J. Select. Areas Commun., 41(6):1772–1786.
Cardoso, L. F. d. S., Kimura, B. Y. L., and Zorzal, E. R. (2023). Towards augmented and mixed reality on future mobile networks. Multimedia Tools and Applications, pages 1–36.
Chen, M., Liu, W., Wang, T., Liu, A., and Zeng, Z. (2021). Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach. Computer Networks, 195:108186.
Chu, N. H., Hoang, D. T., Nguyen, D. N., Phan, K. T., Dutkiewicz, E., Niyato, D., and Shu, T. (2023). MetaSlicing: A Novel Resource Allocation Framework for Metaverse. IEEE Trans. on Mobile Comput., pages 1–18.
Chukhno, O., Galinina, O., Andreev, S., Molinaro, A., and Iera, A. (2023). User and Content Dynamics of Edge-Aided Immersive Reality Services. IEEE Netw. Lett., pages 1–1.
Du, H., Wang, J., Niyato, D., Kang, J., Xiong, Z., Shen, X. S., and Kim, D. I. (2022). Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services. IEEE Network, pages 1–8.
Dutta, S., Roy, D., and Das, G. (2023). Modified Split-Rendering Architecture to enable AI-assisted Application-aware MAC for XR Slice. IEEE Netw. Lett., pages 1–1.
Hsu, C.-F., Hung, T.-H., and Hsu, C.-H. (2022). Optimizing Immersive Video Coding Configurations Using Deep Learning: A Case Study on TMIV. ACM Trans. Multimedia Comput. Commun. Appl., 18(1):1–25.
Kitchenham, B., Charters, S., et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Koutlia, K., Bojovic, B., Lagén, S., Zhang, X., Wang, P., and Liu, J. (2023). System analysis of QoS schedulers for XR traffic in 5G NR. Simulation Modelling Practice and Theory, 125:102745.
Lagen, S., Bojovic, B., Koutlia, K., Zhang, X., Wang, P., and Qu, Q. (2023). QoS Management for XR Traffic in 5G NR: A Multi-Layer System View & End-to-End Evaluation. IEEE Commun. Mag., pages 1–7.
Li, J., Shang, Y., Qin, M., Yang, Q., Cheng, N., Gao, W., and Kwak, K. S. (2022a). Multiobjective Oriented Task Scheduling in Heterogeneous Mobile Edge Computing Networks. IEEE Trans. Veh. Technol., 71(8):8955–8966.
Li, Y., Wang, T., Wu, Y., and Jia, W. (2022b). Optimal dynamic spectrum allocation-assisted latency minimization for multiuser mobile edge computing. Digital Communications and Networks, 8(3):247–256.
Liu, X., Deng, Y., Han, C., and Renzo, M. D. (2022). Learning-Based Prediction, Rendering and Transmission for Interactive Virtual Reality in RIS-Assisted Terahertz Networks. IEEE J. Select. Areas Commun., 40(2):710–724.
Liubogoshchev, M., Ragimova, K., Lyakhov, A., Tang, S., and Khorov, E. (2021). Adaptive Cloud-Based Extended Reality: Modeling and Optimization. IEEE Access, 9:35287–35299.
Maier, M., Ebrahimzadeh, A., Rostami, S., and Beniiche, A. (2020). The Internet of No Things: Making the Internet Disappear and ”See the Invisible”. IEEE Commun. Mag., 58(11):76–82.
Nguyen, T.-V., Dao, N.-N., Dat Tuong, V., Noh, W., and Cho, S. (2022). User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks. IEEE Internet Things J., 9(5):3251–3269.
Passas, V., Makris, N., Wang, Y., Apostolaras, A., Mpatziakas, A., Drosou, A., Korakis, T., and Tzovaras, D. (2022). Artificial Intelligence for network function autoscaling in a cloud-native 5G network. Computers and Electrical Engineering, 103:108327.
Perez, P., Corregidor, D., Garrido, E., Benito, I., Gonzalez-Sosa, E., Cabrera, J., Berjon, D., Diaz, C., Moran, F., Garcia, N., Igual, J., and Ruiz, J. (2022). Live Free-Viewpoint Video in Immersive Media Production Over 5G Networks. IEEE Trans. on Broadcast., 68(2):439–450.
Picano, B. and Fantacci, R. (2023). A Channel-aware FL Approach for Virtual Machine Placement in 6G Edge Intelligent Ecosystems. ACM Trans. Internet Things, 4(2):1–20.
Qian, P., Huynh, V. S. H., Wang, N., Anmulwar, S., Mi, D., and Tafazolli, R. R. (2022). Remote Production for Live Holographic Teleportation Applications in 5G Networks. IEEE Trans. on Broadcast., 68(2):451–463.
Sadiki, A., Bentahar, J., Dssouli, R., En-Nouaary, A., and Otrok, H. (2023). Deep reinforcement learning for the computation offloading in MIMO-based edge computing. 141:103080.
Sehad, N., Cherif, B., Khadraoui, I., Hamidouche, W., Bader, F., Jantti, R., and Debbah, M. (2023). Locomotion-Based UAV Control Toward the Internet of Senses. IEEE Trans. Circuits Syst. II, 70(5):1804–1808.
Siriwardhana, Y., Porambage, P., Liyanage, M., and Ylianttila, M. (2021). A survey on mobile augmented reality with 5g mobile edge computing: Architectures, applications, and technical aspects. IEEE Communications Surveys Tutorials, 23(2):1160–1192.
Song, L., Hu, X., Zhang, G., Spachos, P., Plataniotis, K. N., and Wu, H. (2022). Networking Systems of AI: On the Convergence of Computing and Communications. IEEE Internet Things J., 9(20):20352–20381.
Trinh, B. and Muntean, G.-M. (2023). A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems. IEEE Trans. Veh. Technol., 72(1):1254–1264.
Wang, C., Yu, X., Xu, L., Wang, Z., and Wang, W. (2023). Multimodal semantic communication accelerated bidirectional caching for 6G MEC. Future Generation Computer Systems, 140:225–237.
Wang, Y., Chen, X., Chen, Y., and Du, S. (2021). Resource allocation algorithm for MEC based on Deep Reinforcement Learning. In 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), pages 1–6, Austin, TX, USA. IEEE.
Wazlawick, R. S. (2009). Metodologia de pesquisa para ciência da computação, volume 2. Elsevier.
Yazici, I., Shayea, I., and Din, J. (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, 44:101455.
Yu, W., Chua, T. J., and Zhao, J. (2023). Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse Over Wireless Communications. IEEE J. Select. Areas Commun., 41(7):2138–2157.
Zhang, L., Zhuge, S., Wang, Y., Xu, H., and Sun, E. (2023). Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach. J Ambient Intell Human Comput, 14(6):6603–6621.
Publicado
07/12/2023
Como Citar
GONÇALVES, André Luiz de J.; OLIVEIRA-JR, Antonio; FREITAS, Leandro A..
Revisão Sistemática das Aplicações Imersivas com base nas Tecnologias Habilitadoras B5G/6G, MEC e IA. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 11. , 2023, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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DOI: https://doi.org/10.5753/erigo.2023.237392.
