Construyendo una ciudad inteligente para la gestión de crisis en ciudades de rápido crecimiento y no planeadas: escenario en Colombia
Main Article Content
Keywords
Gestión de crisis, administración de los datos, ciudades no planeadas, ciudades inteligentes
Resumen
Los desastres naturales o provocados por el hombre podrían causar grandes daños en las áreas urbanas y eventualmente podrían cobrar vidas. Es fundamental conocer las características del evento para disponer de rápida respuesta para ayudar a las personas afectadas o para evitar que todos los ciudadanos salgan de la zona de peligro, y luego se tendrá tiempo de responder a la crisis. El internet de las cosas (IoT) tiene un gran impacto en este tipo de situaciones porque con una gran cantidad de datos a través de diferentes dispositivos podrían brindar información suficiente sobre la situación y sobre las personas involucradas en medio de la crisis. En un desastre, uno de los grandes problemas es la desinformación, por eso es
necesario tener datos confiables en caso de desastre, y disponer de una infraestructura capáz de responder en un estado de emergencia. Para informar a las personas afectadas sobre el evento de crisis, existen algunos trabajos previos que han utilizado datos de sensores, texto de redes sociales o imágenes, para finalmente ser procesados [1],[2],[3],[4],[5],[6],[7],[8]. Este artículo tiene como objetivo revisar los casos de estudio donde las ciudades implementan plataformas de gestión de crisis, y se centra en el entorno de IoT donde las aplicaciones utilizan datos híbridos para ser procesados y ayudar a los ciudadanos en una situación de crisis.
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Referencias
[1] R. Q. Wang, H. Mao, Y. Wang, C. Rae, and W. Shaw, “ Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data,” Computers and Geosciences, vol. 111, pp. 139–147, 2 2018. https://doi.org/10.1016/j.cageo.2017.11.008
[2] S. Sudrich, J. Borges, and M. Beigl, “ Graph-based Anomaly Detection for Smart Cities: A Survey,” in IEEE International Conference on Smart City Innovations (IEEE SCI 2017). Karlsruhe, German: IEEE, 8 2017, pp. 1–7. https://ieeexplore.ieee.org/document/8397570/
[3] Z. Xu, Y. Liu, J. Xuan, H. Chen, and L. Mei, “ Crowdsourcing based social media data analysis of urban emergency events,” Multimedia Tools and Applications, vol. 76, no. 9, pp. 11 567–11 584, 5 2017. https://doi.org/10.1007/s11042-015-2731-1
[4] B. Schwarz, G. Pestre, B. Tellman, J. Sullivan, C. Kuhn, R. Mahtta, B. Pandey, and L. Hammett, “ Mapping Floods and Assessing Flood Vulnerability for Disaster Decision-Making: A Case Study Remote Sensing Application in Senegal,” in Earth Observation Open Science and Innovation. Cham: Springer International Publishing, 2018, pp. 293–300. https://doi.org/10.1007/978-3-319-65633-5_16
[5] J. R. Ragini, P. M. Anand, and V. Bhaskar, “ Big data analytics for disaster response and recovery through sentiment analysis,” International Journal of Information Management, vol. 42, pp. 13–24, 10 2018. https://linkinghub.elsevier.com/retrieve/pii/S0268401217307843
[6] Y. Zheng, T. Liu, Y. Wang, Y. Zhu, Y. Liu, and E. Chang, “ Diagnosing New York city’s noises with ubiquitous data,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’14 Adjunct. New York, New York, USA: ACM Press, 2014, pp. 715–725. https://doi.org/10.1145/2632048.2632102
[7] B. Kantarci and H. T. Mouftah, “ Trustworthy sensing for public safety in cloud-centric Internet of things,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 360–368, 8 2014. http://ieeexplore.ieee.org/document/6851843/
[8] J. Kim and M. Hastak, “ Social network analysis: Characteristics of online social networks after a disaster,” International Journal of Information Management, vol. 38, no. 1, pp. 86–96, 2 2018. https://doi.org/10.1016/j.ijinfomgt.2017.08.003
[9] A. P. Castellani, N. Bui, P. Casari, M. Rossi, Z. Shelby, and M. Zorzi, “ Architecture and protocols for the internet of things: A case study,” in 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2010. IEEE, 3 2010, pp. 678–683. http://ieeexplore.ieee.org/document/5470520/
[10] A. Cenedese, A. Zanella, L. Vangelista, and M. Zorzi, “ Padova smart City: An urban Internet of Things experimentation,” in Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, WoWMoM 2014. IEEE, 6 2014, pp. 1–6. http://ieeexplore.ieee.org/document/6918931/
[11] C. Adjih, E. Baccelli, E. Fleury, G. Harter, N. Mitton, T. Noel, R. Pissard-Gibollet, F. Saint-Marcel, G. Schreiner, J. Vandaele, and T. Watteyne, “ FIT IoT-LAB: A Large Scale Open Experimental IoT Testbed,” in IEEE World Forum on Internet of Things (IEEE WF-IoT), Milan, Italy, Dec. 2015. https://hal.inria.fr/hal-01213938
[12] Y. Huang, W. Lin, and H. Zheng, “ A Decision Support System Based on GIS for Flood Prevention of Quanzhou City,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 8 2013, pp. 50–53.
[13] A. C. Weaver, J. P. Boyle, and L. I. Besaleva, “ Applications and trust issues when crowdsourcing a crisis,” in 2012 21st International Conference on Computer Communications and Networks, ICCCN 2012 - Proceedings.
IEEE, 7 2012, pp. 1–5. https://doi.org/10.1109/ICCCN.2012.6289256
[14] S. Koontanakulvong and P. Santitamnanon, “ Lessons learned and information technology roles in Thailand floods 2011,” in 2013 IEEE Region 10 Humanitarian Technology Conference. IEEE, 8 2013, pp. 298–302. http://ieeexplore.ieee.org/document/6669059/
[15] Y. S. Yilmaz, M. F. Bulut, C. G. Akcora, M. A. Bayir, and M. Demirbas, “ Trend sensing via Twitter,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 14, no. 1, p. 16, 2013. https://doi.org/10.1504/IJAHUC.2013.056271
[16] J. Clement, “ Social network users in leading markets 2023 | Statista,” 2019. https://www.statista.com/statistics/278341/ number-of-social-network-users-in-selected-countries/
[17] L. Marek, M. Campbell, and L. Bui, “ Shaking for innovation: The (re)building of a (smart) city in a post disaster environment,” Cities, vol. 63, pp. 41–50, 3 2017. https://doi.org/10.1016/j.cities.2016.12.013
[18] Y. Ding, D. Wang, X. Xin, G. Li, D. Sun, X. Zeng, and R. Ranjan, “ SCFM: Social and crowdsourcing factorization machines for recommendation,” Applied Soft Computing Journal, vol. 66, pp. 548–556, 5 2018. https:
//doi.org/10.1016/j.asoc.2017.08.028
[19] R. Gaire, C. Sriharsha, D. Puthal, H. Wijaya, J. Kim, P. Keshari, R. Ranjan, R. Buyya, R. K. Ghosh, R. K. Shyamasundar, and S. Nepal, “ Internet of Things (IoT) and Cloud Computing Enabled Disaster Management,” 6 2018. http://arxiv.org/abs/1806.07530
[20] L. F. F. de Assis, F. E. Horita, E. P. de Freitas, J. Ueyama, and J. P. de Albuquerque, “ A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management,” Sensors (Switzerland), vol. 18, no. 6, p. 1689, 5 2018. https://doi.org/10.3390/s18061689
[21] COLPRENSA, “ Emergencias por lluvias en San Gil dejan un niño muerto y 20 heridos,” 2016.
[22] M. V. Llorente, J. C. Garzón, and B. Ramirez, “ Así se concentra el homicidio en las ciudades,” p. 2016, 2016.
[23] M. Puentes, D. Novoa, J. M. Delgado Nivia, C. J. Barrios Hernandez, O. Carrillo, and F. Le Mouël, “Pedestrian Behaviour Modeling and Simulation from Real Time Data Information,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915702
[24] J. Mejía, “ Con 900 cámaras de seguridad analizarán comportamiento de santandereanos - Blu Radio,” 2019.
[25] A. E. K. Vanguardia liberal, “ Malos olores enrarecen el aire que se respira en Bucaramanga,” 2018.
[26] M.-A. Lèbre, F. Le Mouël, and E. Ménard, “ Efficient Vehicular Crowdsourcing Models in VANET for Disaster Management,” in VTC Spring 2020 - IEEE 91st Vehicular Technology Conference. Antwerp, Belgium: IEEE, May 2020. https://hal.inria.fr/hal-02917145
[27] S. Tschiatschek, A. Singla, M. G. Rodriguez, A. Merchant, and A. Krause, “ Fake News Detection in Social Networks via Crowd Signals,” 11 2017. http://arxiv.org/abs/1711.09025
[28] C. C. Byers and P. Wetterwald, “ Fog Computing Distributing Data and Intelligence for Resiliency and Scale Necessary for IoT,” Ubiquity, vol. 2015, no. November, pp. 1–12, 2015.
[29] A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong, and J. P. Jue, “ All one needs to know about fog computing and related edge computing paradigms: A complete survey,” pp. 289–330, 2019. https://doi.org/10.1016/j.sysarc.2019.02.009
[30] M. Parra, E. Guillen, F. Le Mouël, and O. Carrillo, “ Sistema colaborativo de medición de parámetros ambientales basado en IoT,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915701
[31] R. Roman, J. Lopez, and M. Mambo, “ Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges,” Future Generation Computer Systems, vol. 78, pp. 680–698, 1 2018. https://doi.org/10.1016/j.future.2016.11.009
[32] P. Y. Zhang, M. C. Zhou, and G. Fortino, “ Security and trust issues in Fog computing: A survey,” Future Generation Computer Systems, vol. 88, pp. 16–27, 11 2018. https://doi.org/10.1016/j.future.2018.05.008
[33] J. He, J. Wei, K. Chen, Z. Tang, Y. Zhou, and Y. Zhang, “ Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 677–686, 2018. https://doi.org/10.1109/JIOT.2017.2724845
[34] S. Nadal, O. Romero, A. Abelló, P. Vassiliadis, and S. Vansummeren, “ An integration-oriented ontology to govern evolution in Big Data ecosystems,” 2 2018. https://doi.org/10.1016/j.is.2018.01.006
[35] M. Avvenuti, S. Cresci, F. Del Vigna, T. Fagni, and M. Tesconi, “ CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing,” pp. 1–19, 3 2018. https://doi.org/10.1007/s10796-018-9833-z
[36] N. Silva, E. R. B. Marques, and L. M. B. Lopes, “ Flux: a platform for dynamically reconfigurable mobile crowd-sensing,” ACM Trans. Sensor Netw. Article, vol. 1, no. 1, 2018. https://doi.org/10.1145/3200202
[37] N. Shoval, Y. Schvimer, and M. Tamir, “ Tracking technologies and urban analysis: Adding the emotional dimension,” Cities, vol. 72, pp. 34–42, 2 2018. https://doi.org/10.1016/j.cities.2017.08.005
[38] A. Y. Zhang, M. Lu, D. Kong, and J. Yang, “Bayesian time series forecasting with change point and anomaly detection,” 2018. https://openreview.net/forum?id=rJLTTe-0W
[39] I. A. Delgado, O. Carrillo, and F. Le Mouël, “ Modleamiento y predicción de lluvias usando Edge Computing para el entorno colombiano,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915700
[2] S. Sudrich, J. Borges, and M. Beigl, “ Graph-based Anomaly Detection for Smart Cities: A Survey,” in IEEE International Conference on Smart City Innovations (IEEE SCI 2017). Karlsruhe, German: IEEE, 8 2017, pp. 1–7. https://ieeexplore.ieee.org/document/8397570/
[3] Z. Xu, Y. Liu, J. Xuan, H. Chen, and L. Mei, “ Crowdsourcing based social media data analysis of urban emergency events,” Multimedia Tools and Applications, vol. 76, no. 9, pp. 11 567–11 584, 5 2017. https://doi.org/10.1007/s11042-015-2731-1
[4] B. Schwarz, G. Pestre, B. Tellman, J. Sullivan, C. Kuhn, R. Mahtta, B. Pandey, and L. Hammett, “ Mapping Floods and Assessing Flood Vulnerability for Disaster Decision-Making: A Case Study Remote Sensing Application in Senegal,” in Earth Observation Open Science and Innovation. Cham: Springer International Publishing, 2018, pp. 293–300. https://doi.org/10.1007/978-3-319-65633-5_16
[5] J. R. Ragini, P. M. Anand, and V. Bhaskar, “ Big data analytics for disaster response and recovery through sentiment analysis,” International Journal of Information Management, vol. 42, pp. 13–24, 10 2018. https://linkinghub.elsevier.com/retrieve/pii/S0268401217307843
[6] Y. Zheng, T. Liu, Y. Wang, Y. Zhu, Y. Liu, and E. Chang, “ Diagnosing New York city’s noises with ubiquitous data,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’14 Adjunct. New York, New York, USA: ACM Press, 2014, pp. 715–725. https://doi.org/10.1145/2632048.2632102
[7] B. Kantarci and H. T. Mouftah, “ Trustworthy sensing for public safety in cloud-centric Internet of things,” IEEE Internet of Things Journal, vol. 1, no. 4, pp. 360–368, 8 2014. http://ieeexplore.ieee.org/document/6851843/
[8] J. Kim and M. Hastak, “ Social network analysis: Characteristics of online social networks after a disaster,” International Journal of Information Management, vol. 38, no. 1, pp. 86–96, 2 2018. https://doi.org/10.1016/j.ijinfomgt.2017.08.003
[9] A. P. Castellani, N. Bui, P. Casari, M. Rossi, Z. Shelby, and M. Zorzi, “ Architecture and protocols for the internet of things: A case study,” in 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2010. IEEE, 3 2010, pp. 678–683. http://ieeexplore.ieee.org/document/5470520/
[10] A. Cenedese, A. Zanella, L. Vangelista, and M. Zorzi, “ Padova smart City: An urban Internet of Things experimentation,” in Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014, WoWMoM 2014. IEEE, 6 2014, pp. 1–6. http://ieeexplore.ieee.org/document/6918931/
[11] C. Adjih, E. Baccelli, E. Fleury, G. Harter, N. Mitton, T. Noel, R. Pissard-Gibollet, F. Saint-Marcel, G. Schreiner, J. Vandaele, and T. Watteyne, “ FIT IoT-LAB: A Large Scale Open Experimental IoT Testbed,” in IEEE World Forum on Internet of Things (IEEE WF-IoT), Milan, Italy, Dec. 2015. https://hal.inria.fr/hal-01213938
[12] Y. Huang, W. Lin, and H. Zheng, “ A Decision Support System Based on GIS for Flood Prevention of Quanzhou City,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 8 2013, pp. 50–53.
[13] A. C. Weaver, J. P. Boyle, and L. I. Besaleva, “ Applications and trust issues when crowdsourcing a crisis,” in 2012 21st International Conference on Computer Communications and Networks, ICCCN 2012 - Proceedings.
IEEE, 7 2012, pp. 1–5. https://doi.org/10.1109/ICCCN.2012.6289256
[14] S. Koontanakulvong and P. Santitamnanon, “ Lessons learned and information technology roles in Thailand floods 2011,” in 2013 IEEE Region 10 Humanitarian Technology Conference. IEEE, 8 2013, pp. 298–302. http://ieeexplore.ieee.org/document/6669059/
[15] Y. S. Yilmaz, M. F. Bulut, C. G. Akcora, M. A. Bayir, and M. Demirbas, “ Trend sensing via Twitter,” International Journal of Ad Hoc and Ubiquitous Computing, vol. 14, no. 1, p. 16, 2013. https://doi.org/10.1504/IJAHUC.2013.056271
[16] J. Clement, “ Social network users in leading markets 2023 | Statista,” 2019. https://www.statista.com/statistics/278341/ number-of-social-network-users-in-selected-countries/
[17] L. Marek, M. Campbell, and L. Bui, “ Shaking for innovation: The (re)building of a (smart) city in a post disaster environment,” Cities, vol. 63, pp. 41–50, 3 2017. https://doi.org/10.1016/j.cities.2016.12.013
[18] Y. Ding, D. Wang, X. Xin, G. Li, D. Sun, X. Zeng, and R. Ranjan, “ SCFM: Social and crowdsourcing factorization machines for recommendation,” Applied Soft Computing Journal, vol. 66, pp. 548–556, 5 2018. https:
//doi.org/10.1016/j.asoc.2017.08.028
[19] R. Gaire, C. Sriharsha, D. Puthal, H. Wijaya, J. Kim, P. Keshari, R. Ranjan, R. Buyya, R. K. Ghosh, R. K. Shyamasundar, and S. Nepal, “ Internet of Things (IoT) and Cloud Computing Enabled Disaster Management,” 6 2018. http://arxiv.org/abs/1806.07530
[20] L. F. F. de Assis, F. E. Horita, E. P. de Freitas, J. Ueyama, and J. P. de Albuquerque, “ A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management,” Sensors (Switzerland), vol. 18, no. 6, p. 1689, 5 2018. https://doi.org/10.3390/s18061689
[21] COLPRENSA, “ Emergencias por lluvias en San Gil dejan un niño muerto y 20 heridos,” 2016.
[22] M. V. Llorente, J. C. Garzón, and B. Ramirez, “ Así se concentra el homicidio en las ciudades,” p. 2016, 2016.
[23] M. Puentes, D. Novoa, J. M. Delgado Nivia, C. J. Barrios Hernandez, O. Carrillo, and F. Le Mouël, “Pedestrian Behaviour Modeling and Simulation from Real Time Data Information,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915702
[24] J. Mejía, “ Con 900 cámaras de seguridad analizarán comportamiento de santandereanos - Blu Radio,” 2019.
[25] A. E. K. Vanguardia liberal, “ Malos olores enrarecen el aire que se respira en Bucaramanga,” 2018.
[26] M.-A. Lèbre, F. Le Mouël, and E. Ménard, “ Efficient Vehicular Crowdsourcing Models in VANET for Disaster Management,” in VTC Spring 2020 - IEEE 91st Vehicular Technology Conference. Antwerp, Belgium: IEEE, May 2020. https://hal.inria.fr/hal-02917145
[27] S. Tschiatschek, A. Singla, M. G. Rodriguez, A. Merchant, and A. Krause, “ Fake News Detection in Social Networks via Crowd Signals,” 11 2017. http://arxiv.org/abs/1711.09025
[28] C. C. Byers and P. Wetterwald, “ Fog Computing Distributing Data and Intelligence for Resiliency and Scale Necessary for IoT,” Ubiquity, vol. 2015, no. November, pp. 1–12, 2015.
[29] A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong, and J. P. Jue, “ All one needs to know about fog computing and related edge computing paradigms: A complete survey,” pp. 289–330, 2019. https://doi.org/10.1016/j.sysarc.2019.02.009
[30] M. Parra, E. Guillen, F. Le Mouël, and O. Carrillo, “ Sistema colaborativo de medición de parámetros ambientales basado en IoT,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915701
[31] R. Roman, J. Lopez, and M. Mambo, “ Mobile edge computing, Fog et al.: A survey and analysis of security threats and challenges,” Future Generation Computer Systems, vol. 78, pp. 680–698, 1 2018. https://doi.org/10.1016/j.future.2016.11.009
[32] P. Y. Zhang, M. C. Zhou, and G. Fortino, “ Security and trust issues in Fog computing: A survey,” Future Generation Computer Systems, vol. 88, pp. 16–27, 11 2018. https://doi.org/10.1016/j.future.2018.05.008
[33] J. He, J. Wei, K. Chen, Z. Tang, Y. Zhou, and Y. Zhang, “ Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 677–686, 2018. https://doi.org/10.1109/JIOT.2017.2724845
[34] S. Nadal, O. Romero, A. Abelló, P. Vassiliadis, and S. Vansummeren, “ An integration-oriented ontology to govern evolution in Big Data ecosystems,” 2 2018. https://doi.org/10.1016/j.is.2018.01.006
[35] M. Avvenuti, S. Cresci, F. Del Vigna, T. Fagni, and M. Tesconi, “ CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing,” pp. 1–19, 3 2018. https://doi.org/10.1007/s10796-018-9833-z
[36] N. Silva, E. R. B. Marques, and L. M. B. Lopes, “ Flux: a platform for dynamically reconfigurable mobile crowd-sensing,” ACM Trans. Sensor Netw. Article, vol. 1, no. 1, 2018. https://doi.org/10.1145/3200202
[37] N. Shoval, Y. Schvimer, and M. Tamir, “ Tracking technologies and urban analysis: Adding the emotional dimension,” Cities, vol. 72, pp. 34–42, 2 2018. https://doi.org/10.1016/j.cities.2017.08.005
[38] A. Y. Zhang, M. Lu, D. Kong, and J. Yang, “Bayesian time series forecasting with change point and anomaly detection,” 2018. https://openreview.net/forum?id=rJLTTe-0W
[39] I. A. Delgado, O. Carrillo, and F. Le Mouël, “ Modleamiento y predicción de lluvias usando Edge Computing para el entorno colombiano,” in 2nd Workshop CATAÏ - SmartData for Citizen Wellness, Bogotá, Colombia, Oct. 2019. https://hal.inria.fr/hal-02915700
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nov 11, 2020
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Puentes, M., Arroyo Delgado, I., Carrillo, O., Barrios H, C. J., & Le Mouel, F. (2020). Construyendo una ciudad inteligente para la gestión de crisis en ciudades de rápido crecimiento y no planeadas: escenario en Colombia. Ingeniería Y Ciencia, 16(32), 151–169. https://doi.org/10.17230/ingciencia.16.32.7
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