A Middleware for Location-Based Virtual Sensors in the Context of Smart Cities

• Alice Menezes UFAM
• Carlos Figueiredo UEA

DOI: https://doi.org/10.5753/sbcup.2019.6597

Abstract

Urban areas are growing over the years and this, together with the modernization of cities, allows us to have access to heterogeneous data that can be used to create new services and use existing resources and infrastructures efficiently. With this, several platform architectures for Smart Cities were proposed with the purpose of managing a large volume of data and providing solutions for urban issues. In this way, this work proposes a middleware to facilitate the knowledge extraction from the data generated in the cities, monitoring abstract information generated from low level data. As proof of concept, we made a case study with a large volume of open data from an urban area.

Keywords: Smart Cities, Location-based, IoT

References

Berntzen, L., Johannesen, M. R., Böhm, S., Weber, C., e Morales, R. (2018). Citizens as sensors: human sensors as a smart city data source. In Proceedings of the 7th Interna- tional Conference on Smart Cities, Systems, Devices and Technologies Citizens, pages 11–18. IARIA.

Coetzee, L. e Eksteen, J. (2011). The internet of things - promise for the future? an introduction. In IST-Africa Conference Proceedings, pages 1–9. IEEE.

Costa, C. e Santos, M. Y. (2016). Basis: A big data architecture for smart cities. In 2016 SAI Computing Conference (SAI), pages 1247–1256. IEEE.

Gubbi, J., Buyya, R., Marusic, S., e Palaniswami, M. (2013). Internet of things (iot): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7):1645–1660.

Kabadayi, S., Pridgen, A., e Julien, C. (2006). Virtual sensors: Abstracting data from physical sensors. In Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, pages 587–592. IEEE Computer Society.

Madria, S., Kumar, V., e Dalvi, R. (2014). Sensor cloud: A cloud of virtual sensors. IEEE software, 31(2):70–77.

Menezes, A. A. F., dos Santos, J. W. V., Souza, B. Á., Almeida, T. G., Nakamura, F. G., Nakamura, E. F., e Figueiredo, C. M. S. (2017). Um método de partição de regiões funcionais utilizando dados de redes sociais. In XXXV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuı́dos, pages 860–873. SBC.

Menezes, A. A. F. e Figueiredo, C. M. S. (2017). Um método de agrupamento incremen- tal para a detecção de eventos em redes sociais. In XXXVII Congresso da Sociedade Brasileira de Computação, pages 1556–1565. SBC.

Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., e Milojicic, D. S. (2017). Soft- ware platforms for smart cities: Concepts, requirements, challenges, and a unified re- ference architecture. ACM Computing Surveys (CSUR), 50(6):78.

Souza, A., Pereira, J., Batista, T., Cavalcante, E., Cacho, N., Lopes, F., e Almeida, A. (2018). A geographic-layered data middleware for smart cities. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, WebMedia ’18, pages 411–414, New York, NY, USA. ACM.

Wu, C., Birch, D., Silva, D., Lee, C.-H., Tsinalis, O., e Guo, Y. (2014). Concinnity: A generic platform for big sensor data applications. IEEE Cloud Computing, 1(2):42–50.

Xavier, A. S., Menezes, A. A. F., Nakamura, F. G., e Figueiredo, C. M. S. (2018). Analysis of urban regions popularity using foursquare. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, WebMedia ’18, pages 379–386, New York, NY, USA. ACM.

Zheng, Y., Capra, L., Wolfson, O., e Yang, H. (2014). Urban computing: concepts, metho- dologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3):38.