Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks

Authors

  • Pushpavalli M
  • Jothi B
  • Buvaneswari B
  • Srinitya G
  • Prabu S Professor, Department of ECE, Mahendra Institute of Technology(Autonomous), Namakkal.

DOI:

https://doi.org/10.22399/ijcesen.480

Keywords:

Energy-efficient IoT, Precision agriculture, Location-aware framework

Abstract

Precision agriculture has emerged as a promising approach to enhance crop yield, reduce environmental impact, and optimize resource utilization through advanced sensing and automation technologies. This paper proposes an energy-efficient and location-aware framework for Internet of Things (IoT) and Wireless Sensor Networks (WSN)-based precision agriculture systems. The framework leverages low-power wireless communication protocols, adaptive sensor scheduling, and location-based clustering algorithms to minimize energy consumption and prolong the network lifetime. Key features include real-time monitoring of soil moisture, temperature, humidity, and crop health through geographically distributed sensors, with automated decision-making for irrigation, fertilization, and pest control. The proposed framework also integrates machine learning models for predictive analysis and anomaly detection, enabling early identification of potential issues that could adversely affect crop productivity. Simulation results demonstrate a significant reduction in energy consumption and communication overhead, while maintaining high accuracy in environmental parameter monitoring and resource allocation. This framework offers a scalable and robust solution for implementing sustainable precision agriculture practices, particularly in remote and resource-constrained areas

References

Zhang, J., Li, X., & Wang, L. (2020). Precision agriculture technologies for improving crop yield and reducing environmental impact. Agricultural Systems, 178, 102763.

Li, X., Zhang, C., & Wang, Y. (2019). IoT-based precision agriculture: An overview and future perspectives. Computers and Electronics in Agriculture, 168, 105992.

Shah, D., Singh, M., & Sahu, A. (2021). Real-time monitoring and automation in precision agriculture using IoT. Journal of Sensor and Actuator Networks, 10(1), 10.

Nguyen, T., Singh, A., & Yadav, P. (2020). A review of IoT-based applications in precision agriculture. Journal of Agricultural Science and Technology, 22(2), 1-15.

Singh, A., Yadav, S., & Singh, M. (2018). Low-power IoT and WSN-based systems for agricultural applications. International Journal of Precision Agriculture, 3(4), 245-256.

Shah, D., Gupta, R., & Yadav, A. (2020). Advanced sensor technologies for precision agriculture: A review. Sensors, 20(14), 3926.

Kumar, P., Patel, D., & Sharma, V. (2019). Smart farming with IoT and WSN: Challenges and solutions. Journal of Agricultural and Environmental Information, 4(3), 131-145.

Li, Z., Zhang, B., & Huang, L. (2020). Application of variable rate technology in precision agriculture: A review. Biosystems Engineering, 196, 55-67.

Zhou, J., Wu, Q., & Liu, M. (2021). Energy-efficient IoT frameworks for smart agriculture. IEEE Internet of Things Journal, 8(12), 9835-9847.

Zhang, C., Sun, Y., & Liu, H. (2020). Energy-efficient communication protocols for IoT and WSN-based precision agriculture. IEEE Transactions on Green Communications and Networking, 4(4), 1193-1205.

Wu, Y., Liu, J., & Zhang, T. (2021). Low-power adaptive sensor networks for smart agriculture. IEEE Access, 9; 98761-98770.

Li, X., Zhang, Y., & Wang, R. (2018). Adaptive clustering algorithms for energy-efficient WSNs in agriculture. IEEE Access, 6, 46548-46559.

Sharma, D., Patel, A., & Shah, M. (2019). Overcoming communication challenges in IoT-based precision agriculture. Journal of Agricultural Research, 5(1), 112-125.

Li, Y., Zhou, X., & Wang, P. (2019). Multi-hop communication in WSNs for agricultural applications. Journal of Sensor Technology, 8(3), 222-235.

Wang, T., Yang, H., & Zhang, L. (2020). Location-aware clustering for IoT-based precision agriculture. IEEE Transactions on Network and Service Management, 17(3), 1495-1507.

Zhou, P., Liu, Y., & Wang, Q. (2019). Multi-hop clustering algorithms for WSNs in precision agriculture. IEEE Access, 7, 76488-76499.

Singh, M., Sharma, A., & Patel, D. (2019). Energy-efficient location-aware IoT frameworks for smart farming. Sensors, 19(17), 3798.

Zhao, J., Zhang, Y., & Wang, H. (2021). Cost-effective IoT solutions for small-scale precision agriculture. Journal of Agricultural Engineering Research, 14(1), 92-101.

Kumar, A., Singh, R., & Sharma, V. (2021). Low-cost WSN solutions for precision agriculture in developing regions. International Journal of Agricultural Technology, 17(3), 1081-1095.

Li, Y., Zhou, X., & Zhao, T. (2018). Scalable and low-cost WSN frameworks for smart agriculture. IEEE Access, 6, 30904-30912.

Wang, H., Yang, F., & Zhang, C. (2020). Multifunctional sensor networks for IoT-based precision agriculture. Journal of Sensor and Actuator Networks, 9(2), 26.

Shah, M., Zhang, Y., & Liu, L. (2020). Hybrid communication protocols for energy-efficient WSNs in agriculture. IEEE Sensors Journal, 20(23), 14078

Maheshwari, R.U., Kumarganesh, S., K V M, S. et al. (2024). Advanced Plasmonic Resonance-enhanced Biosensor for Comprehensive Real-time Detection and Analysis of Deepfake Content. Plasmonics. https://doi.org/10.1007/s11468-024-02407-0

Maheshwari, R. U., Paulchamy, B., Arun, M., Selvaraj, V., & Saranya, N. N. (2024). Deepfake Detection using Integrate-backward-integrate Logic Optimization Algorithm with CNN. International Journal of Electrical and Electronics Research, 12(2), 696-710.

Maheshwari, R. U., & Paulchamy, B. (2024). Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training. Automatika, 65(4), 1517-1532. https://doi.org/10.1080/00051144.2024.2400640

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Published

2024-10-07

How to Cite

M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.480

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Section

Research Article