▷ Wireless Sensor Network (WSN)

Wireless Sensor Network (WSN), based on Internet of Things (IoT) applications, has transformed the way farmers manage their daily operations and significantly enhanced strategic productivity in the agricultural sector. This groundbreaking technology enables wireless connectivity among sensors scattered in the field, creating an intelligent network that provides real-time data for various key applications.

One of the standout applications is the smart irrigation system, where sensors collect data on soil moisture and weather conditions. This data is used to optimize irrigation patterns, ensuring efficient water use and maximizing crop growth. Furthermore, WSN facilitates monitoring parameters such as soil moisture, temperature, evapotranspiration (ET) in the agricultural environment, allowing farmers to take swift action upon detecting contaminants or unfavorable conditions for production.

Another important application is indoor positioning, aiding farmers in tracking and managing assets within warehouses and agricultural structures more efficiently. Moreover, WSN is utilized to measure energy consumption, enabling farmers to identify consumption patterns and adopt more sustainable practices.

By implementing these WSN-based applications, farmers can make informed and timely decisions, optimizing their cultivation processes, reducing costs, and increasing production. This not only leads to a significant improvement in operational efficiency but also contributes to the conservation of natural resources by reducing water and energy wastage. Ultimately, WSN in IoT applications not only enhances farmers' strategic productivity but also promotes more sustainable and environmentally friendly agricultural practices.

Publications:

• José M. Pereira Pontón, Verónica Ojeda, Víctor Asanza, Leandro L. Lorente-Leyva and Diego H. Peluffo-Ordóñez, “Design and Implementation of an IoT Control and Monitoring System for the Optimization of Shrimp Pools using LoRa Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140829
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• Avilés, J. C., Ojeda, V., & Asanza, V. (2023). Device Free Indoor Localization in the 28 GHz band based on machine learning. Procedia Computer Science, 220, 55-63, doi: https://doi.org/10.1016/j.procs.2023.03.010
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• Rebeca Estrada, Xavier Aizaga, Nelson Vera, and Victor Asanza. 2023. Evaluation of ML-based Positioning Systems with RSSI Measured on the User's Device or APs. In Proceedings of the Int'l ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks (PE-WASUN '23). Association for Computing Machinery, New York, NY, USA, 77–82. https://doi.org/10.1145/3616394.3618261
• GitHub

• Estrada, R., Asanza, V., Torres, D., Bazurto, A., & Valeriano, I. (2022). Learning-based Energy Consumption Prediction. Procedia Computer Science, 203, 272-279, doi: https://doi.org/10.1016/j.procs.2022.07.035.
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• GoogleDrive: SSE

• J. Landívar, C. Ormaza, V. Asanza, V. Ojeda, J. C. Avilés and D. H. Peluffo-Ordóñez, "Trilateration-based Indoor Location using Supervised Learning Algorithms," 2022 International Conference on Applied Electronics (AE), 2022, pp. 1-6, doi: 10.1109/AE54730.2022.9920073.
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• A. Maisincho-Jivaja et al., "Monitoring a turkey hatchery based on a cyber-physical system," 2021 International Conference on Applied Electronics (AE), 2021, pp. 1-6, doi: 10.23919/AE51540.2021.9542899.
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• J. Capelo et al., "Raspberry Pi-based IoT for shrimp farms Real-time remote monitoring with automated system," 2021 International Conference on Applied Electronics (AE), 2021, pp. 1-4, doi: 10.23919/AE51540.2021.9542907.
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• V. Asanza, R. E. Pico, D. Torres, S. Santillan and J. Cadena, "FPGA Based Meteorological Monitoring Station," 2021 IEEE Sensors Applications Symposium (SAS), 2021, pp. 1-6, doi: 10.1109/SAS51076.2021.9530151.
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• Cordova, R., Garcia, I. M., Munoz-Arcentales, A., Asanza, V., & Vargas, W. A. V. (2018). Modelo de red de comunicación que soporta un sistema de detección basado en criterios de robustez. IEEE Latin America Transactions, 16(10), 2600-2608.

Figure Reference: Authentication model in wireless sensor network

• Yu, S.; Park, Y. SLUA-WSN: Secure and Lightweight Three-Factor-Based User Authentication Protocol for Wireless Sensor Networks. Sensors 2020, 20, 4143. https://doi.org/10.3390/s20154143

Read related topics

• ✅2023 Journal: Design and Implementation of an IoT Control and Monitoring System for the Optimization of Shrimp Pools using LoRa Technology
• ✅2023 Journal: Device Free Indoor Localization in the 28 GHz band based on machine learning
• ✅2023 Paper: Evaluation of ML-based Positioning Systems with RSSI Measured on the User's Device or APs
• ✅2023 Paper: Comparison of Traditional ML Algorithms for Energy Consumption Prediction Models
• ✅ 2022 Paper: Learning-based Energy Consumption Prediction
• ✅2022 Paper: #Trilateration-based Indoor Location using Supervised Learning Algorithms
• ✅ 2021 Paper: #RaspberryPi-based #IoT for #shrimp farms Real-time remote monitoring with automated system
• ✅ 2021 Paper: Monitoring a turkey hatchery based on a #cyber_physical_system
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