Um Estudo Exploratório do Cooja para Simulação de Cenário em Internet das Coisas
Resumo
Disciplinas ministradas com aprendizagem baseada em problemas (ABP) possuem simulações como um recurso valioso para exercitar as habilidades de resolução de problemas dos alunos. O uso de simulações computacionais têm o potencial de explorar uma variedade de problemas relacionados à Internet das Coisas sem os custos e riscos presentes nas implementações físicas. Este trabalho apresenta alguns exemplos de problemas no domínio de agricultura e uma simulação utilizando o Cooja Simulator que podem ser usados como ponto de partida para o seu uso com a metodologia ABP.
Palavras-chave:
Engenharia de Software, Redes de Sensores Sem Fio, Simulação, Aprendizagem Baseada em Problemas
Referências
Abuarqoub, A., Hammoudeh, M., Alfayez, F., and Aldabbas, O. (2016). A Survey on Wireless Sensor Networks Simulation Tools and Testbeds, pages 283–302
Barrett, T. and Moore, S. (2010). New approaches to problem-based learning: Revitalising your practice in higher education. Routledge.
de Souza e Luis Dourado, S. C. (2015). Aprendizagem baseada em problemas (abp): Um método de aprendizagem inovador para o ensino educativo. HOLOS, 5(0):182–200.
Fialho, D., Basso, F., Luizelli, M., and Cabrera, S. (2021). Emprego de simulações computacionais em problemas envolvendo agricultura: Um estudo de mapeamento sistemático. In Anais do III Workshop em Modelagem e Simulação de Sistemas Intensivos em Software, pages 20–29, Porto Alegre, RS, Brasil. SBC.
Jiang, X., Yi, W., Chen, Y., and He, H. (2018). Energy Efficient Smart Irrigation System Based on 6LoWPAN: 4th International Conference, ICCCS 2018, Haikou, China, June 8-10, 2018, Revised Selected Papers, Part V, pages 308–319.
Khan, F. and Kumar, D. (2019). Ambient crop field monitoring for improving context based agricultural by mobile sink in wsn. Journal of Ambient Intelligence and Humanized Computing.
Khan, S., Pathan, A.-S. K., and Alrajeh, N. A. (2016). Wireless Sensor Networks: Current Status and Future Trends. CRC Press, Inc., Boca Raton, FL, USA, 1st edition.
Khelifi, F. (2020). Monitoring System Based in Wireless Sensor Network for Precision Agriculture, pages 461–472. Springer International Publishing, Cham.
Linsner, S., Varma, R., and Reuter, C. (2019). Vulnerability assessment in the smart farming infrastructure through cyberattacks. In Meyer-Aurich, A., Gandorfer, M., Barta, N., Gronauer, A., Kantelhardt, J., and Floto, H., editors, 39. GIL-Jahrestagung, Digitalisierung für landwirtschaftliche Betriebe in kleinstrukturierten Regionen - ein Widerspruch in sich?, pages 119–124, Bonn. Gesellschaft für Informatik e.V.
Mahalakshmi, J., Kuppusamy, K., Kaleeswari, C., and Maheswari, P. (2020). Iot sensor-based smart agricultural system. In Subramanian, B., Chen, S.-S., and Reddy, K. R., editors, Emerging Technologies for Agriculture and Environment, pages 39–52, Singapore. Springer Singapore.
Nandhini, A., Rajendran, H., Sankararajan, R., and Indumathi, K. (2017). Web enabled plant disease detection system for agricultural applications using wmsn. Wireless Personal Communications.
ONU, N. U. (2021). Un calls for urgent action to feed the world’s growing population healthily, equitably and sustainably.
Shipu, X., Yunsheng, W., Yong, L., Weixiong, R., Mingzhou, M., Jingyin, Z., and Chenxi, Z. (2018). Research on wsn routing algorithm for vegetable greenhouse. pages 37–42.
Thakur, D., Kumar, Y., Kumar, A., and Singh, P. K. (2019). Applicability of wireless sensor networks in precision agriculture: A review. Wireless Personal Communications, 107(1):471–512.
Wang, K. I.-K., Wu, S., Ivoghlian, A., Salcic, Z., Austin, A., and Zhou, X. (2019). Lws: A lorawan wireless underground sensor network simulator for agriculture applications. In 2019 IEEE (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 475–482.
Wu, S., Austin, A. C., Ivoghlian, A., Bisht, A., Kevin, I., and Wang, K. (2020). Long range wide area network for agricultural wireless underground sensor networks. Journal of Ambient Intelligence and Humanized Computing, pages 1–17.
Yanes, A. R., Martinez, P., and Ahmad, R. (2020). Towards automated aquaponics: A review on monitoring, iot, and smart systems. Journal of Cleaner Production, 263:121571.
Zhang, M., Xiong, S., and Wang, L. (2019). Sensor-cloud based precision sprinkler irrigation management system.
Barrett, T. and Moore, S. (2010). New approaches to problem-based learning: Revitalising your practice in higher education. Routledge.
de Souza e Luis Dourado, S. C. (2015). Aprendizagem baseada em problemas (abp): Um método de aprendizagem inovador para o ensino educativo. HOLOS, 5(0):182–200.
Fialho, D., Basso, F., Luizelli, M., and Cabrera, S. (2021). Emprego de simulações computacionais em problemas envolvendo agricultura: Um estudo de mapeamento sistemático. In Anais do III Workshop em Modelagem e Simulação de Sistemas Intensivos em Software, pages 20–29, Porto Alegre, RS, Brasil. SBC.
Jiang, X., Yi, W., Chen, Y., and He, H. (2018). Energy Efficient Smart Irrigation System Based on 6LoWPAN: 4th International Conference, ICCCS 2018, Haikou, China, June 8-10, 2018, Revised Selected Papers, Part V, pages 308–319.
Khan, F. and Kumar, D. (2019). Ambient crop field monitoring for improving context based agricultural by mobile sink in wsn. Journal of Ambient Intelligence and Humanized Computing.
Khan, S., Pathan, A.-S. K., and Alrajeh, N. A. (2016). Wireless Sensor Networks: Current Status and Future Trends. CRC Press, Inc., Boca Raton, FL, USA, 1st edition.
Khelifi, F. (2020). Monitoring System Based in Wireless Sensor Network for Precision Agriculture, pages 461–472. Springer International Publishing, Cham.
Linsner, S., Varma, R., and Reuter, C. (2019). Vulnerability assessment in the smart farming infrastructure through cyberattacks. In Meyer-Aurich, A., Gandorfer, M., Barta, N., Gronauer, A., Kantelhardt, J., and Floto, H., editors, 39. GIL-Jahrestagung, Digitalisierung für landwirtschaftliche Betriebe in kleinstrukturierten Regionen - ein Widerspruch in sich?, pages 119–124, Bonn. Gesellschaft für Informatik e.V.
Mahalakshmi, J., Kuppusamy, K., Kaleeswari, C., and Maheswari, P. (2020). Iot sensor-based smart agricultural system. In Subramanian, B., Chen, S.-S., and Reddy, K. R., editors, Emerging Technologies for Agriculture and Environment, pages 39–52, Singapore. Springer Singapore.
Nandhini, A., Rajendran, H., Sankararajan, R., and Indumathi, K. (2017). Web enabled plant disease detection system for agricultural applications using wmsn. Wireless Personal Communications.
ONU, N. U. (2021). Un calls for urgent action to feed the world’s growing population healthily, equitably and sustainably.
Shipu, X., Yunsheng, W., Yong, L., Weixiong, R., Mingzhou, M., Jingyin, Z., and Chenxi, Z. (2018). Research on wsn routing algorithm for vegetable greenhouse. pages 37–42.
Thakur, D., Kumar, Y., Kumar, A., and Singh, P. K. (2019). Applicability of wireless sensor networks in precision agriculture: A review. Wireless Personal Communications, 107(1):471–512.
Wang, K. I.-K., Wu, S., Ivoghlian, A., Salcic, Z., Austin, A., and Zhou, X. (2019). Lws: A lorawan wireless underground sensor network simulator for agriculture applications. In 2019 IEEE (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pages 475–482.
Wu, S., Austin, A. C., Ivoghlian, A., Bisht, A., Kevin, I., and Wang, K. (2020). Long range wide area network for agricultural wireless underground sensor networks. Journal of Ambient Intelligence and Humanized Computing, pages 1–17.
Yanes, A. R., Martinez, P., and Ahmad, R. (2020). Towards automated aquaponics: A review on monitoring, iot, and smart systems. Journal of Cleaner Production, 263:121571.
Zhang, M., Xiong, S., and Wang, L. (2019). Sensor-cloud based precision sprinkler irrigation management system.
Publicado
01/12/2021
Como Citar
SANTOS, Dienefer Fialho dos; BASSO, Fábio Paulo; LUIZELLI, Marcelo Caggiani; CABRERA, Saimon Martins.
Um Estudo Exploratório do Cooja para Simulação de Cenário em Internet das Coisas. In: ESCOLA REGIONAL DE ENGENHARIA DE SOFTWARE (ERES), 5. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 31-39.
DOI: https://doi.org/10.5753/eres.2021.18448.
