Analyzing the Carro Pipa Operation with Geointelligence Techniques

Aloísio Vieira Lira Neto

^1. Federal Institute of Ceará, Fortaleza/CE, Brazil

Email: [email protected]; [email protected]

^2. Department of Teleinformatics Engineering (DETI), Federal University of Ceará, Fortaleza/CE, Brazil

Email: [email protected]; [email protected]

^3. State University of Ceará, Fortaleza/CE, Brazil

Email: [email protected]

^4. Mepco Schlenk Engineering College, Sivakasi, India

Email: [email protected]

*Corresponding Author: Aloísio Vieira Lira Neto, Email: [email protected]

Operation Carro Pipa (OCP) is a federal government action in Brazil with the objective of distributing drinking water to regions severely affected by long periods of drought and low rainfall using trucks. The region served has continental dimensions, covering an area of 688,064 km² and supplying 1703 cities and approximately 5.2 million people. Because it is a large-scale action that involves a lot of public resources, it is essential that all OCP activities are recorded in a safe, complete, and standardized way. This information can be used for potential benefits, audits, and analysis to propose improvements and ensure the provision of this essential service to society. To achieve this, this work analyzes data recorded from the OCP service offered in a Brazilian state and presents computational solutions that can improve the monitoring, registration, storage, and processing of the data generated by this action.

Operation Carro Pipa, Internet of Things, Data analysis, Northeast of Brazil, Internet of Things

Aloísio Vieira Lira Neto, Elias Paulino Medeiros, Filipe Maciel de Moura, Jose Wally Mendonça Menezes, Senthil Kumar Jagatheesaperumal, Victor Hugo C. de Albuquerque (2023). Analyzing the Carro Pipa Operation with Geointelligence Techniques. Journal of Artificial Intelligence and Systems, 5, 1–22. https://doi.org/10.33969/AIS.2023050101.

[1] Ab'Sáber, A. N. (1999). Nordeste sertanejo: a região semi-árida mais povoada do mundo. Estudos avançados, 13(36), 60-61.

[2] Brasil. Ministério da Integração Nacional. Relatório final do grupo de trabalho interministerial para a redelimitação do Semiárido nordestino e do polígono das secas. Brasília, DF, 2005a. 1 CD-ROM.

[3] Duarte, R. (2002). O estado da arte das tecnologias para a convivência com as secas no Nordeste (Vol. 6). Banco do Nordeste.

[4] Silva, A. M. da; Brito, L. P. de; Trandade, U. B.; Silva, V. C. da (2022). OPERAÇÃO CARRO PIPA: Uma abordagem da atuação do Exército Brasileiro como mão amiga e integração nacional. Mauá, SP.

[5] Rose, K., Eldridge, S., & Chapin, L. (2015). The internet of things: An overview. The internet society (ISOC), 80, 1-50.

[6] Li, S., Xu, L. D., & Zhao, S. (2015). The internet of things: a survey. Information systems frontiers, 17, 243-259.

[7] Hussain, T., Muhammad, K., Ullah, A., Del Ser, J., Gandomi, A. H., Sajjad, M., ... & de Albuquerque, V. H. C. (2020). Multiview summarization and activity recognition meet edge computing in IoT environments. IEEE Internet of Things Journal, 8(12), 9634-9644.

[8] Muhammad, K., Hussain, T., Rodrigues, J. J., Bellavista, P., de Macêdo, A. R. L., & de Albuquerque, V. H. C. (2020). Efficient and privacy preserving video transmission in 5G-enabled IoT surveillance networks: Current challenges and future directions. IEEE Network, 35(2), 26-33.

[9] Hussain, T., Muhammad, K., Ding, W., Lloret, J., Baik, S. W., & de Albuquerque, V. H. C. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition, 109, 107567.

[10] Hussain, T., Ullah, F. U. M., Khan, S. U., Ullah, A., Haroon, U., Muhammad, K., ... & de Albuquerque, V. H. C. (2022). Deep Learning Assists Surveillance Experts: Toward Video Data Prioritization. IEEE Transactions on Industrial Informatics.

[11] Hussain, T., Muhammad, K., Del Ser, J., Baik, S. W., & de Albuquerque, V. H. C. (2019). Intelligent embedded vision for summarization of multiview videos in IIoT. IEEE Transactions on Industrial Informatics, 16(4), 2592-2602.

[12] Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, 1-12.

[13] Mehmood, I., Ullah, A., Muhammad, K., Deng, D. J., Meng, W., Al-Turjman, F., ... & de Albuquerque, V. H. C. (2019). Efficient image recognition and retrieval on IoT-assisted energy-constrained platforms from big data repositories. IEEE Internet of Things Journal, 6(6), 9246-9255.

[14] Zhang, J., Xu, C., Gao, Z., Rodrigues, J. J., & de Albuquerque, V. H. C. (2020). Industrial pervasive edge computing-based intelligence IoT for surveillance saliency detection. IEEE Transactions on Industrial Informatics, 17(7), 5012-5020.

[15] Magaia, N., Fonseca, R., Muhammad, K., Segundo, A. H. F. N., Neto, A. V. L., & de Albuquerque, V. H. C. (2020). Industrial internet-of-things security enhanced with deep learning approaches for smart cities. IEEE Internet of Things Journal, 8(8), 6393-6405.

[16] Muhammad, K., Del Ser, J., Magaia, N., Fonseca, R., Hussain, T., Gandomi, A. H., ... & de Albuquerque, V. H. C. (2022). Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives. IEEE Network.

[17] Silva, L., Magaia, N., Sousa, B., Kobusińska, A., Casimiro, A., Mavromoustakis, C. X., ... & De Albuquerque, V. H. C. (2021). Computing paradigms in emerging vehicular environments: A review. IEEE/CAA Journal of Automatica Sinica, 8(3), 491-511.

[18] Sodhro, A. H., Rodrigues, J. J., Pirbhulal, S., Zahid, N., de Macedo, A. R. L., & de Albuquerque, V. H. C. (2020). Link optimization in software defined IoV driven autonomous transportation system. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3511-3520.

[19] Sodhro, A. H., Luo, Z., Sodhro, G. H., Muzamal, M., Rodrigues, J. J., & De Albuquerque, V. H. C. (2019). Artificial Intelligence based QoS optimization for multimedia communication in IoV systems. Future Generation Computer Systems, 95, 667-680.

[20] Allam, Z., & Dhunny, ZA (2019). On big data, artificial intelligence and smart cities. Cities , 89 , 80-91.

[21] Wang, S., Liu, X., Liu, S., Muhammad, K., Heidari, A. A., Del Ser, J., & de Albuquerque, V. H. C. (2021). Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities. IEEE Internet of Things Journal, 9(10), 7128-7139.

[22] Liu, S., Wang, S., Liu, X., Gandomi, A. H., Daneshmand, M., Muhammad, K., & De Albuquerque, V. H. C. (2021). Human memory update strategy: a multi-layer template update mechanism for remote visual monitoring. IEEE Transactions on Multimedia, 23, 2188-2198.

[23] Moalla, I., Nabli, A., Bouzguenda, L., & Hammami, M. (2017). Data warehouse design approaches from social media: review and comparison. Social Network Analysis and Mining, 7(1), 5.

[24] Li, X., Liu, H., Wang, W., Zheng, Y., Lv, H., & Lv, Z. (2022). Big data analysis of the internet of things in the digital twins of smart city based on deep learning. Future Generation Computer Systems, 128, 167-177.

[25] Zygiaris, S. (2018). Database management systems: A business-oriented approach using ORACLE, MySQL and MS access. Emerald Group Publishing.

[26] Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37-37. 

[27] Muhammad, K., Hussain, T., Del Ser, J., Palade, V., & De Albuquerque, V. H. C. (2019). DeepReS: A deep learning-based video summarization strategy for resource-constrained industrial surveillance scenarios. IEEE Transactions on Industrial Informatics, 16(9), 5938-5947.