Towards an IoT-Based Architecture for Monitoring and Automated Decision-Making in an Aviary Environment
ResumoInternet of Things (IoT) is a prominent technology in which everyday objects can be equipped with identifying, sensing, networking, and processing capabilities that allow them to communicate with each another and with other devices and services over the Internet to achieve some goal. In agribusiness, the use of technologies, such as IoT, is called precision livestock farming, which includes the use of different technologies in production and care of livestock animals. Brazil is one of the largest poultry producers in the world, being the first in exportation. Despite that, poultry production faces difficulties due to sensitivity that birds have to numerous environmental factors in aviaries, such as lightning, sounds, harmful gases, air humidity, food quality and clean water. When these variables are not well controlled, problems in meat quality and poultry production are likely to occur. As a result, farmers may face severe financial losses. Thus, providing a healthy environment is essential, and to achieve this, accurate monitoring and fast decision making are required in order to solve the problem as soon as possible. In this paper a detailed process of requirements elicitation and architectural design for IoT-Based aviary monitoring systems is proposed along with an informal literature review from the area. As preliminary results, we delivered a requirements document with functional and nonfunctional requirements closer to the real needs of farmers and an architectural proposal that can be used as a reference for further studies.
Bass, L., Clements, P., and Kazman, R. (2003). Software Architecture in Practice.
Batista, P. E. P., Bulcão Neto, R. d. F., Paes, C. E. d. B., Lima, E. A. d., Rohling, A. J.,and Graciano Neto, V. V. (2021). Reference architecture for systems of systems: a systematic mapping. iSys - Brazilian Journal of Information Systems, 14(1):119–153. DOI: https://doi.org/10.5753/isys.2021.988
Berckmans, D. (2006). Automatic on-line monitoring of animals by precision livestock farming. Livestock production and society, 287:27–30.
Bourque, P. and Fairley, R. E., editors (2014). SWEBOK: Guide to the Software Engineering Body of Knowledge. IEEE Computer Society, Los Alamitos, CA, version 3.0 edition.
Camargo, T. F., Silva, R. L., Higa, M., Coutinho, M. R., de Oliveira, J. C., and Conceicão, W. A. d. S. (2019). Monitoramento do conforto térmico em aviários mediante sistemas de aquisição de dados em tempo real. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(9):694–701. DOI: https://doi.org/10.1590/1807-1929/agriambi.v23n9p694-701
Choukidar, G. A. and Dawande, N. (2017). Smart poultry farm automation and monitoring system. In 2017 ICCUBEA, pages 1–5. IEEE. DOI: https://doi.org/10.1109/ICCUBEA.2017.8463953
da Silva Oliveira, G., dos Santos, V. M., Rodrigues, J. C., and Nascimento, S. T. (2019). Protótipo para o estudo do comportamento e da zona de conforto térmico de frangos de corte. REVISTA EIXO, 8(1).
Debauche, O., Mahmoudi, S., Mahmoudi, S. A., Manneback, P., Bindelle, J., and Lebeau, F. (2020). Edge computing and artificial intelligence for real-time poultry monitoring. Procedia Computer Science, 175:534–541. DOI: https://doi.org/10.1016/j.procs.2020.07.076
Dias-Neto, A. C., Spinola, R., and Travassos, G. H. (2010). Developing software technologies through experimentation: experiences from the battlefield. In XIII CIbSE.
Feijó, T., David, J. M., Braga, R., Otenio, M. H., Paula, V. R., Santos, G. M., Campos, F., and Stroele, V. (2021). @grogestambiental: A web-based decision support system for agribusiness. WebMedia ’21, page 1–8, New York, NY, USA. Association for Computing Machinery.
Hofmeister, C., Kruchten, P., Nord, R. L., Obbink, H., Ran, A., and America, P. (2007). A general model of software architecture design derived from five industrial approaches. Journal of Systems and Software, 80(1):106–126. DOI: http://doi.org/10.1016/j.jss.2006.05.024
Horowitz, E., Sahni, S., and Rajasekaran, S. (2007). Computer Algorithms. Silicon Press, USA, 2nd edition. DOI: http://doi.org/10.1186/1472-6947-12-59
Kruchten, P.B.(1995). The4+1viewmodelofarchitecture. IEEEsoftware, 12(6):42–50. DOI: https://doi.org/10.1109/52.469759
Lashari, M. H., Memon, A. A., Shah, S. A. A., Nenwani, K., and Shafqat, F. (2018). Iot based poultry environment monitoring system. In 2018 IEEE IOTAIS, pages 1–5. DOI: https://doi.org/10.1109/IOTAIS.2018.8600837
Lopes, V. C., Norberto, M., R. S., D. W., Kassab, M., da Silva Soares, A., Oliveira, R., and Neto, V. V. G. (2020). A systematic mapping study on software testing for systems-of-systems. SAST 20, page 88–97, New York, NY, USA. Association for Computing Machinery. DOI: https://doi.org/10.1145/3425174.3425216
Manshor, N., Rahiman, A. R. A., and Yazed, M. K. (2019). Iot based poultry house monitoring. In 2019 2nd ICCET, pages 72–75. IEEE. DOI: http://doi.org/10.1109/ICCET.2019.8726880
Mumbelli, A., Brito, R. C., Pegorini, V., and Priester, L. F. (2020). Low cost iot-based system for monitoring and remote controlling aviaries. In 2020 3rd ICICT, pages 531–535. IEEE. DOI: http://doi.org/10.1109/ICICT50521.2020.00090
Pessoa, G. T., de Sousa, G. V., Ferraz, M. S., Feitosa, M. L. T., and de Miranda Sampaio, A. (2013). Estratégias inovadoras no manejo de frangos de corte em avicultura industrial: fases pré-inicial, inicial, engorda e final. Pubvet, 7:1002–1136. DOI: http://doi.org/10.22256/pubvet.v7n12.1553
Raj, A. A. G. and Jayanthi, J. G. (2018). Iot-based real-time poultry monitoring and health status identification. In 2018 11th ISMA, pages 1–7. DOI: https://doi.org/10.1007/978-981-13-5758-9_3
Santos, 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. DOI: https://doi.org/10.5753/mssis.2021.17256