Model-Driven Mobile CrowdSensing for Smart Cities
Resumo
Making cities smarter can help improve city services, optimize resource and infrastructure utilization and increase quality of life. Smart Cities connect citizens in novel ways by leveraging the latest advances in information and communication technologies (ICT). The integration of rich sensing capabilities in today’s mobile devices allows their users to actively participate in sensing the environment. In Mobile CrowdSensing (MCS) citizens of a Smart City collect, share and jointly use services based on sensed data. The main challenges for smart cities regarding MCS is the heterogeneity of devices and the dynamism of the environment. To overcome these challenges, this paper presents an architecture based on models at runtime (M@rt) to support dynamic MCS queries in Smart Cities. The architecture is proposed as an extension of the InterSCity platform, leveraging on its existing services and on its capability to integrate city infrastructure resources.
Referências
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Aubry, E., Silverston, T., Lahmadi, A., & Festor, O. (2014, March). “CrowdOut: a mobile crowdsourcing service for road safety in digital cities.” In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on (pp. 86-91). IEEE.
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Celino, I., Kotoulas, S. “Smart Cities [Guest editors' introduction].” IEEE Internet Computing, v. 17, n. 6, p. 8-11, 2013.
Del Esposte, A. D. M., Kon, F., Costa, F. M., & Lago, N. “InterSCity: A Scalable Microservice-based Open Source Platform for Smart Cities”, 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Porto, Portugal, 2017
Diniz, H. B., Silva, E. C. G. F., and Gama, K. "Uma Arquitetura de Referência para Plataforma de Crowdsensing em Smart Cities." XI Brazilian Symposium on Information System. 2015.
Filipponi, L., Vitaletti, A., Landi, G., Memeo, V., Laura, G., & Pucci, P. (2010, July). “Smart city: An event driven architecture for monitoring public spaces with heterogeneous sensors.” In Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on (pp. 281-286). IEEE.
Ganti, R. K.;i YE, F.;i LEI, H. “Mobile crowdsensing: Current state and future challenges.” Communications Magazine, IEEE, 49(11):32–39, 2011
Melo, P. C. F. "CSVM: Uma plataforma para crowdsensing móvel dirigida por modelos em tempo de execução." (2014).
OMG, Q. V. T. “Meta object facility (mof) 2.0 query/view/transformation specifcation.” Final Adopted Specifcation (November 2005), 2008.
Petkovics, A., et al. "Crowdsensing solutions in smart cities: Introducing a horizontal architecture." Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia. ACM, 2015.
Piro, G., et al. "Information centric services in smart cities." Journal of Systems and Software 88 (2014): 169-188.
Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2017). Software platforms for smart cities: Concepts, requirements, challenges, and a unifed reference architecture. ACM Computing Surveys (CSUR), 50(6), 78.
Soldatos, J., Kefalakis, N., Hauswirth, M., Serrano, M., Calbimonte, J. P., Riahi, M. & Skorin-Kapov, L. (2015). Openiot: Open source internet-of-things in the cloud. In Interoperability and open-source solutions for the internet of things (pp. 13-25). Springer, Cham.
Stojanovic, D., Predic, B. and Stojanovic, N. "Mobile crowd sensing for smart urban mobility." European Handbook of Crowdsourced Geographic Information (2016): 371.
Zappatore, M., Longo, A., & Bochicchio, M. A. Using mobile crowd sensing for noise monitoring in smart cities. In Computer and Energy Science (SpliTech), International Multidisciplinary Conference on (pp. 1-6). IEEE, 2016.
Anastasi, Giuseppe, et al. "Urban and social sensing for sustainable mobility in smart cities." Sustainable Internet and ICT for Sustainability (SustainIT), 2013. IEEE, 2013.
Aubry, E., Silverston, T., Lahmadi, A., & Festor, O. (2014, March). “CrowdOut: a mobile crowdsourcing service for road safety in digital cities.” In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on (pp. 86-91). IEEE.
Batista, D. M., Goldman, A., Hirata, R., Kon, F., Costa, F. M., & Endler, M. "Interscity: Addressing future internet research challenges for smart cities." Network of the Future (NOF), 2016 7th International Conference on the. IEEE, 2016.
Blair, G.;i Bencomo, N.;i France, R. B. “Models@run.time.” Computer, v. 42, n. 10, 2009.
Borgia, E. “The Internet of Things vision: Key features, applications and open issues.” Computer Communications, v. 54, p. 1-31, 2014.
Borja, R., & Gama, K. (2014). “Middleware para cidades inteligentes baseado em um barramento de serviços. ” X Simpósio Brasileiro de Sistemas de Informação (SBSI), 1, 584-590.
Celino, I., Kotoulas, S. “Smart Cities [Guest editors' introduction].” IEEE Internet Computing, v. 17, n. 6, p. 8-11, 2013.
Del Esposte, A. D. M., Kon, F., Costa, F. M., & Lago, N. “InterSCity: A Scalable Microservice-based Open Source Platform for Smart Cities”, 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Porto, Portugal, 2017
Diniz, H. B., Silva, E. C. G. F., and Gama, K. "Uma Arquitetura de Referência para Plataforma de Crowdsensing em Smart Cities." XI Brazilian Symposium on Information System. 2015.
Filipponi, L., Vitaletti, A., Landi, G., Memeo, V., Laura, G., & Pucci, P. (2010, July). “Smart city: An event driven architecture for monitoring public spaces with heterogeneous sensors.” In Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on (pp. 281-286). IEEE.
Ganti, R. K.;i YE, F.;i LEI, H. “Mobile crowdsensing: Current state and future challenges.” Communications Magazine, IEEE, 49(11):32–39, 2011
Melo, P. C. F. "CSVM: Uma plataforma para crowdsensing móvel dirigida por modelos em tempo de execução." (2014).
OMG, Q. V. T. “Meta object facility (mof) 2.0 query/view/transformation specifcation.” Final Adopted Specifcation (November 2005), 2008.
Petkovics, A., et al. "Crowdsensing solutions in smart cities: Introducing a horizontal architecture." Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia. ACM, 2015.
Piro, G., et al. "Information centric services in smart cities." Journal of Systems and Software 88 (2014): 169-188.
Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2017). Software platforms for smart cities: Concepts, requirements, challenges, and a unifed reference architecture. ACM Computing Surveys (CSUR), 50(6), 78.
Soldatos, J., Kefalakis, N., Hauswirth, M., Serrano, M., Calbimonte, J. P., Riahi, M. & Skorin-Kapov, L. (2015). Openiot: Open source internet-of-things in the cloud. In Interoperability and open-source solutions for the internet of things (pp. 13-25). Springer, Cham.
Stojanovic, D., Predic, B. and Stojanovic, N. "Mobile crowd sensing for smart urban mobility." European Handbook of Crowdsourced Geographic Information (2016): 371.
Zappatore, M., Longo, A., & Bochicchio, M. A. Using mobile crowd sensing for noise monitoring in smart cities. In Computer and Energy Science (SpliTech), International Multidisciplinary Conference on (pp. 1-6). IEEE, 2016.
Publicado
25/07/2018
Como Citar
MELO, Paulo César F.; COSTA, Fábio M..
Model-Driven Mobile CrowdSensing for Smart Cities. In: WORKSHOP BRASILEIRO DE CIDADES INTELIGENTES (WBCI), 1. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 41-50.
DOI: https://doi.org/10.5753/wbci.2018.3226.
