Desafios de Pesquisa em Arquiteturas para IoT social
Resumo
As aplicações em IoT Social, com suas diferentes naturezas, apresentam vários desafios na concepção, definição da arquitetura, implementação e gerenciamento das relações. Este artigo realiza uma Revisão Sistemática de Literatura - RSL, onde seleciona as pesquisas recentes nesta área, com intuito de investigar as características arquiteturais e as lacunas de pesquisa ainda existentes. Como principal contribuição deste estudo, destacaríamos os aspectos arquiteturais mais importantes para uma solução Social IoT, bem como os estudos que dão ênfase a determinada temática estão agrupados, isso pode guiar futuras pesquisas na área e favorecer o desenvolvimento de novas soluções. Por fim, uma síntese das contribuições dos trabalhos selecionados está descrita.
Palavras-chave:
Funcionalidades chave, Ambiente Inteligente, Gestão de Relacionamentos, Internet das Coisas Social
Referências
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Al-Turjman, F. (2019). 5g-enabled devices and smart-spaces in social-iot: an overview. Future Generation Computer Systems, 92:732–744.
Anjomshoa, F., Aloqaily, M., Kantarci, B., Erol-Kantarci, M., and Schuckers, S. (2017). Social behaviometrics for personalized devices in the internet of things era. IEEE Access, 5:12199–12213.
Anke, J. (2019). Design-integrated financial assessment of smart services. Electronic Markets, 29(1):19–35.
Armando, N., Rodrigues, A., Pereira, V., Sá Silva, J., and Boavida, F. (2018). An outlookon physical and virtual sensors for a socially interactive internet. Sensors, 18(8):2578.
Bai, L., Yang, D., Wang, X., Tong, L., Zhu, X., Bai, C., and Powell, C. A. (2020). Chinese experts’ consensus on the internet of things-aided diagnosis and treatment of corona-virus disease 2019. Clinical eHealth.
Cicirelli, F., Guerrieri, A., Mercuri, A., Spezzano, G., and Vinci, A. (2019). Itema: Amethodological approach for cognitive edge computing iot ecosystems. Future Generation Computer Systems, 92:189–197.
Cirani, S., Davoli, L., Ferrari, G., Léone, R., Medagliani, P., Picone, M., and Veltri,L. (2014). A scalable and self-configuring architecture for service discovery in theinternet of things. IEEE internet of things journal, 1(5):508–521.
Cumpston, M., Li, T., Page, M. J., Chandler, J., Welch, V. A., Higgins, J. P., and Thomas,J. (2019). Updated guidance for trusted systematic reviews: a new edition of the coch-rane handbook for systematic reviews of interventions. Cochrane Database Syst Rev,10:ED000142.
Dhelim, S., Ning, H., Farha, F., Chen, L., Atzori, L., and Daneshmand, M. (2021). Iot-enabled social relationships meet artificial social intelligence.arXiv preprint ar-Xiv:2103.01776.
Emmanouilidis, C., Bertoncelj, L., Bevilacqua, M., Tedeschi, S., and Ruiz-Carcel, C.(2018). Internet of things-enabled visual analytics for linked maintenance and productlifecycle management. IFAC-PapersOnLine, 51(11):435–440.
Gupta, D., Rodrigues, J. J., Sundaram, S., Khanna, A., Korotaev, V., and de Albuquerque,V. H. C. (2018). Usability feature extraction using modified crow search algorithm: anovel approach. Neural Computing and Applications, pages 1–11.
Gusenbauer, M. (2019). Google scholar to overshadow them all? comparing the sizes of12 academic search engines and bibliographic databases. Scientometrics, 118(1):177–214.
Hamrioui, S., Hamrioui, C. A. M., Lioret, J., and Lorenz, P. (2018). Smart and self-organised routing algorithm for efficient iot communications in smart cities. IET Wi-reless Sensor Systems, 8(6):305–312.
Iqbal, R., Butt, T. A., Shafique, M. O., Talib, M. W. A., and Umer, T. (2018). Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles. IEEE Access, 6:58182–58194.
Khamparia, A., Singh, A., Anand, D., Gupta, D., Khanna, A., Kumar, N. A., and Tan, J.(2018). A novel deep learning-based multi-model ensemble method for the predictionof neuromuscular disorders. Neural computing and applications, pages 1–13.
Kolozali, S ̧ ., Bermudez-Edo, M., FarajiDavar, N., Barnaghi, P., Gao, F., Ali, M. I., Mileo,A., Fischer, M., Iggena, T., Kuemper, D., et al. (2018). Observing the pulse of a city: A smart city framework for real-time discovery, federation, and aggregation of datastreams. IEEE Internet of Things Journal, 6(2):2651–2668.
Lippi, M., Mamei, M., Mariani, S., and Zambonelli, F. (2018). An argumentation-basedperspective over the social iot. IEEE Internet of Things Journal, 5(4):2537–2547.
Loscri, V., Ruggeri, G., Vegni, A. M., and Cricelli, I. (2018). Social structure analysis ininternet of vehicles. In2018 IEEE International Conference on Sensing, Communica-tion and Networking (SECON Workshops), pages 1–4. IEEE.
Maghawry, N. E. and Ghoniemy, S. (2019). A proposed internet of everything frameworkfor disease prediction. International Journal of Online and Biomedical Engineering(iJOE), 15(04):20–27.
Ning, H., Liu, H., Ma, J., Yang, L. T., and Huang, R. (2016). Cybermatics: Cyber–physical–social–thinking hyperspace based science and technology. Future generation computer systems, 56:504–522.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studiesin software engineering. InEase, volume 8, pages 68–77.
Rho, S. and Chen, Y. (2019). Social internet of things: Applications, architectures andprotocols.
Roopa, M., Pattar, S., Buyya, R., Venugopal, K. R., Iyengar, S., and Patnaik, L. (2019).Social internet of things (siot): Foundations, thrust areas, systematic review and future directions. Computer Communications, 139:32–57.
Saleem, Y., Crespi, N., Rehmani, M. H., Copeland, R., Hussein, D., and Bertin, E. (2016).Exploitation of social iot for recommendation services. In2016 IEEE 3rd World Forumon Internet of Things (WF-IoT), pages 359–364. IEEE.
Seal, A. and Mukherjee, A. (2018). On the emerging coexistence of edge, fog and cloudcomputing paradigms in real-time internets-of-everythings which operate in the big-squared data space. InSoutheastCon 2018, pages 1–9. IEEE.
Singh, S. P., Nayyar, A., Kumar, R., and Sharma, A. (2019). Fog computing: from archi-tecture to edge computing and big data processing. The Journal of Supercomputing,75(4):2070–2105.
Song, Z., Sun, Y., Yan, H., Wu, D., Niu, P., and Wu, X. (2017). Robustness of smartmanufacturing information systems under conditions of resource failure: A complexnetwork perspective.IEEE Access, 6:3731–3738.
Tahsien, S. M., Karimipour, H., and Spachos, P. (2020). Machine learning based solutionsfor security of internet of things (iot): A survey. Journal of Network and ComputerApplications, page 102630.
Wang, K., Du, M., Maharjan, S., and Sun, Y. (2017). Strategic honeypot game modelfor distributed denial of service attacks in the smart grid. IEEE Transactions on SmartGrid, 8(5):2474–2482.
Wu, J., Su, Z., Wang, S., and Li, J. (2017). Crowd sensing-enabling security service recommendation for social fog computing systems. Sensors, 17(8):1744.
Al-Turjman, F. (2019). 5g-enabled devices and smart-spaces in social-iot: an overview. Future Generation Computer Systems, 92:732–744.
Anjomshoa, F., Aloqaily, M., Kantarci, B., Erol-Kantarci, M., and Schuckers, S. (2017). Social behaviometrics for personalized devices in the internet of things era. IEEE Access, 5:12199–12213.
Anke, J. (2019). Design-integrated financial assessment of smart services. Electronic Markets, 29(1):19–35.
Armando, N., Rodrigues, A., Pereira, V., Sá Silva, J., and Boavida, F. (2018). An outlookon physical and virtual sensors for a socially interactive internet. Sensors, 18(8):2578.
Bai, L., Yang, D., Wang, X., Tong, L., Zhu, X., Bai, C., and Powell, C. A. (2020). Chinese experts’ consensus on the internet of things-aided diagnosis and treatment of corona-virus disease 2019. Clinical eHealth.
Cicirelli, F., Guerrieri, A., Mercuri, A., Spezzano, G., and Vinci, A. (2019). Itema: Amethodological approach for cognitive edge computing iot ecosystems. Future Generation Computer Systems, 92:189–197.
Cirani, S., Davoli, L., Ferrari, G., Léone, R., Medagliani, P., Picone, M., and Veltri,L. (2014). A scalable and self-configuring architecture for service discovery in theinternet of things. IEEE internet of things journal, 1(5):508–521.
Cumpston, M., Li, T., Page, M. J., Chandler, J., Welch, V. A., Higgins, J. P., and Thomas,J. (2019). Updated guidance for trusted systematic reviews: a new edition of the coch-rane handbook for systematic reviews of interventions. Cochrane Database Syst Rev,10:ED000142.
Dhelim, S., Ning, H., Farha, F., Chen, L., Atzori, L., and Daneshmand, M. (2021). Iot-enabled social relationships meet artificial social intelligence.arXiv preprint ar-Xiv:2103.01776.
Emmanouilidis, C., Bertoncelj, L., Bevilacqua, M., Tedeschi, S., and Ruiz-Carcel, C.(2018). Internet of things-enabled visual analytics for linked maintenance and productlifecycle management. IFAC-PapersOnLine, 51(11):435–440.
Gupta, D., Rodrigues, J. J., Sundaram, S., Khanna, A., Korotaev, V., and de Albuquerque,V. H. C. (2018). Usability feature extraction using modified crow search algorithm: anovel approach. Neural Computing and Applications, pages 1–11.
Gusenbauer, M. (2019). Google scholar to overshadow them all? comparing the sizes of12 academic search engines and bibliographic databases. Scientometrics, 118(1):177–214.
Hamrioui, S., Hamrioui, C. A. M., Lioret, J., and Lorenz, P. (2018). Smart and self-organised routing algorithm for efficient iot communications in smart cities. IET Wi-reless Sensor Systems, 8(6):305–312.
Iqbal, R., Butt, T. A., Shafique, M. O., Talib, M. W. A., and Umer, T. (2018). Context-aware data-driven intelligent framework for fog infrastructures in internet of vehicles. IEEE Access, 6:58182–58194.
Khamparia, A., Singh, A., Anand, D., Gupta, D., Khanna, A., Kumar, N. A., and Tan, J.(2018). A novel deep learning-based multi-model ensemble method for the predictionof neuromuscular disorders. Neural computing and applications, pages 1–13.
Kolozali, S ̧ ., Bermudez-Edo, M., FarajiDavar, N., Barnaghi, P., Gao, F., Ali, M. I., Mileo,A., Fischer, M., Iggena, T., Kuemper, D., et al. (2018). Observing the pulse of a city: A smart city framework for real-time discovery, federation, and aggregation of datastreams. IEEE Internet of Things Journal, 6(2):2651–2668.
Lippi, M., Mamei, M., Mariani, S., and Zambonelli, F. (2018). An argumentation-basedperspective over the social iot. IEEE Internet of Things Journal, 5(4):2537–2547.
Loscri, V., Ruggeri, G., Vegni, A. M., and Cricelli, I. (2018). Social structure analysis ininternet of vehicles. In2018 IEEE International Conference on Sensing, Communica-tion and Networking (SECON Workshops), pages 1–4. IEEE.
Maghawry, N. E. and Ghoniemy, S. (2019). A proposed internet of everything frameworkfor disease prediction. International Journal of Online and Biomedical Engineering(iJOE), 15(04):20–27.
Ning, H., Liu, H., Ma, J., Yang, L. T., and Huang, R. (2016). Cybermatics: Cyber–physical–social–thinking hyperspace based science and technology. Future generation computer systems, 56:504–522.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studiesin software engineering. InEase, volume 8, pages 68–77.
Rho, S. and Chen, Y. (2019). Social internet of things: Applications, architectures andprotocols.
Roopa, M., Pattar, S., Buyya, R., Venugopal, K. R., Iyengar, S., and Patnaik, L. (2019).Social internet of things (siot): Foundations, thrust areas, systematic review and future directions. Computer Communications, 139:32–57.
Saleem, Y., Crespi, N., Rehmani, M. H., Copeland, R., Hussein, D., and Bertin, E. (2016).Exploitation of social iot for recommendation services. In2016 IEEE 3rd World Forumon Internet of Things (WF-IoT), pages 359–364. IEEE.
Seal, A. and Mukherjee, A. (2018). On the emerging coexistence of edge, fog and cloudcomputing paradigms in real-time internets-of-everythings which operate in the big-squared data space. InSoutheastCon 2018, pages 1–9. IEEE.
Singh, S. P., Nayyar, A., Kumar, R., and Sharma, A. (2019). Fog computing: from archi-tecture to edge computing and big data processing. The Journal of Supercomputing,75(4):2070–2105.
Song, Z., Sun, Y., Yan, H., Wu, D., Niu, P., and Wu, X. (2017). Robustness of smartmanufacturing information systems under conditions of resource failure: A complexnetwork perspective.IEEE Access, 6:3731–3738.
Tahsien, S. M., Karimipour, H., and Spachos, P. (2020). Machine learning based solutionsfor security of internet of things (iot): A survey. Journal of Network and ComputerApplications, page 102630.
Wang, K., Du, M., Maharjan, S., and Sun, Y. (2017). Strategic honeypot game modelfor distributed denial of service attacks in the smart grid. IEEE Transactions on SmartGrid, 8(5):2474–2482.
Wu, J., Su, Z., Wang, S., and Li, J. (2017). Crowd sensing-enabling security service recommendation for social fog computing systems. Sensors, 17(8):1744.
Publicado
18/07/2021
Como Citar
CAMARGO, Leandro; PERNAS, Ana Marilza; YAMIN, Adenauer; HAERTEL, Felipe.
Desafios de Pesquisa em Arquiteturas para IoT social. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 1-10.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2021.15998.
