Simulator of Artificial Intelligence of Things applications for real-time monitoring
Abstract
The advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies has opened up new possibilities for applications in several areas, including real-time monitoring. This work presents the development of an Artificial Intelligence of Things (AIoT) applications simulator to monitor rural areas using Unmanned Aerial Vehicles (UAVs). The proposal integrates an edge/fog/cloud architecture, where UAVs equipped with cameras and AI algorithms perform real-time animal detection. The system distributes the processing load between the edge devices and the fog server, optimizing the efficiency and accuracy of detections. The developed graphical interface allows visualization and management of simulations, facilitating analysis and decision-making. The results demonstrate the viability and effectiveness of the system for monitoring difficult-to-access environments, contributing to efficient resource management and rapid response to application events.
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Barriga, J. A., Clemente, P. J., Pérez-Toledano, M. A., Jurado-Málaga, E., and Hernández, J. (2023). Design, code generation and simulation of iot environments with mobility devices by using model-driven development: Simulateiot-mobile. Pervasive and Mobile Computing, 89:101751.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. CoRR, abs/2005.12872.
Del-Pozo-Puñal, E., García-Carballeira, F., and Camarmas-Alonso, D. (2023). A scalable simulator for cloud, fog and edge computing platforms with mobility support. Future Generation Computer Systems, 144:117–130.
Eclipse, F. (2024). Mosquitto Documentation. Accessed: 2024-06-24.
Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., and Berners-Lee, T. (1999). Hypertext transfer protocol – http/1.1. Technical report, IETF. Accessed: 2024-07-10.
Geraci, G., López-Pérez, D., Benzaghta, M., and Chatzinotas, S. (2022). Integrating terrestrial and non-terrestrial networks: 3d opportunities and challenges. ArXiv, abs/2207.10385.
Google (2024). Angular Documentation. Accessed: 2024-07-10.
Group, P. G. D. (2024). PostgreSQL Documentation. Accessed: 2024-07-10.
Kertész, A., Pflanzner, T., and Gyimóthy, T. (2019). A mobile iot device simulator for iot-fog-cloud systems. Journal of Grid Computing, 17:529–551.
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollár, P. (2015). Microsoft coco: Common objects in context.
Minoli, D., Sohraby, K., and Occhiogrosso, B. (2017). Iot considerations, requirements, and architectures for smart buildings—energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1):269–283.
Myśliwiec, K. (2024). NestJS Documentation. Accessed: 2024-07-10.
Network, M. D. (2024). Web Documentation. Accessed: 2024-07-10.
OASIS (2024). MQTT Protocol. Accessed: 2024-07-10.
Obaid, A., Shawkat, A. S., and Abdulhussein, N. S. (2023). Exploring the potential of a-resnet in person-independent face recognition and classification. International Journal of Advanced Network, Monitoring and Controls, 8:12–19.
Pereira, R. S., Prazeres, C. V. S., Barbosa, M. T. M., Barros, E. B. C., and Peixoto, M. L. M. (2021). Iotfogsim: Um simulador orientado a eventos para avaliaçao de aplicaçoes baseadas em iot-fog-cloud. In Anais Estendidos do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 25–32. SBC.
Prisma, I. (2024). Prisma Documentation. Accessed: 2024-07-10.
SmartBear (2024). Swagger Documentation. Accessed: 2024-07-01.
Stewart, J. C., Davis, G. A., and Igoche, D. A. (2020). Ai, iot, and aiot: Definitions and impacts on the artificial intelligence curriculum. Issues in Information Systems, 21(4).
Wan, C., Pang, Y., and Lan, S. (2022). Overview of yolo object detection algorithm. International Journal of Computing and Information Technology.
Wang, C.-Y. and Liao, H.-Y. M. (2024). YOLOv9: Learning what you want to learn using programmable gradient information.
Wang, H., Li, K., and Xu, C. (2022). A new generation of resnet model based on artificial intelligence and few data driven and its construction in image recognition model. Computational Intelligence and Neuroscience, 2022.
Zhu, S., Ota, K., and Dong, M. (2022). Green AI for IIoT: Energy efficient intelligent edge computing for industrial internet of things. IEEE Transactions on Green Communications and Networking, 6:79–88.
Published
2024-09-11
How to Cite
GONÇALVES, Allan Jheyson R.; CASTRO, Ana Beatriz N. de; EVILLY, Brenda; LEÃO, Erico Meneses; TORRES NETO, Jose R.; SILVA, Romuere R. V.; C. FILHO, Antonio Oseas; RABELO, Ricardo A. L..
Simulator of Artificial Intelligence of Things applications for real-time monitoring. In: REGIONAL SCHOOL ON COMPUTING OF CEARÁ, MARANHÃO, AND PIAUÍ (ERCEMAPI), 12. , 2024, Parnaíba/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 159-168.
DOI: https://doi.org/10.5753/ercemapi.2024.243722.
