Proceedings of International Conference on Applied Innovation in IT  ·  2025/07/26  ·  Vol. 13  ·  Issue 3  ·  pp. 19–26
A Robust Wireless Sensor Network for Real-Time Soil Moisture Analysis in Precision Farming
Wayan Puguh, Haidir Amin and Arkan Adnan Imran
A wireless sensor network (WSN) is designed to monitor soil moisture in real-time in precision farming, as demonstrated in this paper. This system enables farmers to make informed decisions regarding irrigation, water usage, and crop yield through the integration of advanced sensors and communication protocols. WSN architecture, design, and functionality, including node deployment, data transmission protocols, and sensor integration, are examined. Based on the results, the proposed system offers an ideal solution for large-scale agricultural monitoring, as it provides real-time soil moisture data with minimal power consumption. This system significantly improves data accuracy, scalability, and network stability, as demonstrated through extensive simulations and practical experiments conducted under various conditions. Our proposed system effectively addresses several critical challenges prevalent in agricultural Wireless Sensor Networks (WSNs), including enhanced energy efficiency, robust and reliable data transmission, and adaptive sampling techniques specifically designed for dynamic and unpredictable field environments. The network incorporates advanced, low-power capacitive soil moisture sensors integrated seamlessly with a novel hybrid routing protocol. This innovative protocol strategically combines both cluster-based routing and direct node-to-sink transmission approaches, thereby optimizing communication pathways, reducing overall power consumption, and maintaining stable network operation under fluctuating agricultural conditions. This integrated solution ensures accurate real-time monitoring, enabling improved decision-making and resource allocation in precision agriculture applications.
Wireless Sensor Networks (WSNs) Soil Moisture Monitoring Precision Farming Energy-Efficient Design Agricultural Monitoring.
References
  1. N. Kumar, P. Rani, V. Kumar, S. V. Athawale, and D. Koundal, “THWSN: Enhanced energy-efficient clustering approach for three-tier heterogeneous wireless sensor networks,” IEEE Sens. J., vol. 22, no. 20, pp. 20053–20062, 2022.
  2. N. Kumar, P. Rani, V. Kumar, P. K. Verma, and D. Koundal, “Teeech: Three-tier extended energy efficient clustering hierarchy protocol for heterogeneous wireless sensor network,” Expert Syst. Appl., vol. 216, p. 119448, 2023.
  3. A. Baggio, “Wireless sensor networks in precision agriculture,” in ACM workshop on real-world wireless sensor networks (REALWSN 2005), Stockholm, Sweden, Citeseer, 2005, pp. 1567–1576. Accessed: Apr. 02, 2025. [Online]. Available: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=cdcb4cf0271a1049c8240ea70fd8579bdcdf0c97.
  4. P. Rani and M. H. Falaah, “Real-Time Congestion Control and Load Optimization in Cloud-MANETs Using Predictive Algorithms,” NJF Intell. Eng. J., vol. 1, no. 1, pp. 66–76, 2024.
  5. T. Grift, “The first word: the farm of the future,” Resour. Mag., vol. 18, no. 1, p. 1, 2011.
  6. A. Kamilaris, F. Gao, F. X. Prenafeta-Boldu, and M. I. Ali, “Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications,” in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA: IEEE, Dec. 2016, pp. 442–447. doi: 10.1109/WF-IoT.2016.7845467.
  7. R. Adams, B. Hurd, S. Lenhart, and N. Leary, “Effects of global climate change on world agriculture: an interpretive review,” Clim. Res., vol. 11, pp. 19–30, 1998, doi: 10.3354/cr011019.
  8. O. Westermann, W. Förch, P. Thornton, J. Körner, L. Cramer, and B. Campbell, “Scaling up agricultural interventions: Case studies of climate-smart agriculture,” Agric. Syst., vol. 165, pp. 283–293, Sep. 2018, doi: 10.1016/j.agsy.2018.07.007.
  9. H. Channe, S. Kothari, and D. Kadam, “Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis,” Int J Comput. Technol. Appl., vol. 6, no. 3, pp. 374–382, 2015.
  10. A. Singh et al., “Smart Traffic Monitoring Through Real-Time Moving Vehicle Detection Using Deep Learning via Aerial Images for Consumer Application,” IEEE Trans. Consum. Electron., vol. 70, no. 4, pp. 7302–7309, Nov. 2024, doi: 10.1109/TCE.2024.3445728.
  11. M. Bacco et al., “Smart farming: Opportunities, challenges and technology enablers,” in 2018 IoT Vertical and Topical Summit on Agriculture - Tuscany (IOT Tuscany), Tuscany: IEEE, May 2018, pp. 1–6. doi: 10.1109/IOT-TUSCANY.2018.8373043.
  12. L. Lamport and C. aTime, “the Ordering of Events in a Distributed System, o Comm.” ACM, 1978.
  13. F. Sivrikaya and B. Yener, “Time synchronization in sensor networks: a survey,” IEEE Netw., vol. 18, no. 4, pp. 45–50, Jul. 2004, doi: 10.1109/MNET.2004.1316761.
  14. L. Tavares Bruscato, T. Heimfarth, and E. Pignaton De Freitas, “Enhancing Time Synchronization Support in Wireless Sensor Networks,” Sensors, vol. 17, no. 12, p. 2956, Dec. 2017, doi: 10.3390/s17122956.
  15. P. Rani and R. Sharma, “An experimental study of IEEE 802.11 n devices for vehicular networks with various propagation loss models,” in International Conference on Signal Processing and Integrated Networks, Springer, 2022, pp. 125–135.
  16. N. Kumar Agrawal et al., “TFL-IHOA: Three-Layer Federated Learning-Based Intelligent Hybrid Optimization Algorithm for Internet of Vehicle,” IEEE Trans. Consum. Electron., vol. 70, no. 3, pp. 5818–5828, Aug. 2024, doi: 10.1109/TCE.2023.3344129.
  17. D. K. Shinghal and N. Srivastava, “Wireless sensor networks in agriculture: for potato farming,” Available SSRN 3041375, 2017, Accessed: Apr. 02, 2025. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3041375.
  18. P. Rani and R. Sharma, “Intelligent Transportation System Performance Analysis of Indoor and Outdoor Internet of Vehicle (IoV) Applications Towards 5G,” Tsinghua Sci. Technol., vol. 29, no. 6, pp. 1785–1795, Dec. 2024, doi: 10.26599/TST.2023.9010119.
  19. M. R. Mohd Kassim, I. Mat, and A. N. Harun, “Wireless Sensor Network in precision agriculture application,” in 2014 International Conference on Computer, Information and Telecommunication Systems (CITS), Jeju, South Korea: IEEE, Jul. 2014, pp. 1–5. doi: 10.1109/CITS.2014.6878963.
  20. S. Sulaiman, A. Manut, and A. R. N. Firdaus, “Design, Fabrication and Testing of Fringing Electric Field Soil Moisture Sensor for Wireless Precision Agriculture Applications,” in 2009 International Conference on Information and Multimedia Technology, Jeju Island, Korea (South): IEEE, 2009, pp. 513–516. doi: 10.1109/ICIMT.2009.54.
  21. P. Rani, U. C. Garjola, and H. Abbas, “A Predictive IoT and Cloud Framework for Smart Healthcare Monitoring Using Integrated Deep Learning Model,” NJF Intell. Eng. J., vol. 1, no. 1, pp. 53–65, 2024.
  22. R. Liao, S. Zhang, X. Zhang, M. Wang, H. Wu, and L. Zhangzhong, “Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept,” Agric. Water Manag., vol. 245, p. 106632, Feb. 2021, doi: 10.1016/j.agwat.2020.106632.
  23. H. Liu, Y. Yang, X. Wan, J. Cui, F. Zhang, and T. Cai, “Prediction of soil moisture and temperature based on deep learning,” in 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China: IEEE, Jun. 2021, pp. 46–51. doi: 10.1109/ICAICA52286.2021.9498190.
  24. C. Saad Hajjar, C. Hajjar, M. Esta, and Y. Ghorra Chamoun, “Machine learning methods for soil moisture prediction in vineyards using digital images,” E3S Web Conf., vol. 167, p. 02004, 2020, doi: 10.1051/e3sconf/202016702004.
  25. M. ElSaadani, E. Habib, A. M. Abdelhameed, and M. Bayoumi, “Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations,” Front. Artif. Intell., vol. 4, p. 636234, Mar. 2021, doi: 10.3389/frai.2021.636234.
  26. S. Prakash, A. Sharma, and S. S. Sahu, “Soil Moisture Prediction Using Machine Learning,” in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore: IEEE, Apr. 2018, pp. 1–6. doi: 10.1109/ICICCT.2018.8473260.
  27. P. Rani and R. Sharma, “Intelligent transportation system for internet of vehicles based vehicular networks for smart cities,” Comput. Electr. Eng., vol. 105, p. 108543, 2023.
  28. K. Sarode and P. Chaudhari, “Zigbee based agricultural monitoring and controlling system,” Int J Eng Sci, vol. 8, pp. 15907–15910, 2018.
  29. S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Internet of Things (IoT) communication protocols,” in 2017 8th International conference on information technology (ICIT), IEEE, 2017, pp. 685–690. Accessed: Apr. 04, 2025. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8079928/.

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