Performance Comparison of Wireless Communication Protocols for Smart Agriculture IoT Systems

Abstract

Smart Agriculture leverages Internet of Things (IoT) technologies to monitor environmental parameters such as soil moisture, temperature, Humidity, Nutrient levels and crop growth, while also enabling real-time detection of animal or human intrusions that may damage crops. The efficiency of these systems is strongly influenced by the choice of wireless communication protocol, which must support long-range connectivity, low power consumption, reliable data transmission and scalability across large farmland areas. This paper presents a detailed performance comparison of widely used wireless communication protocols for smart agriculture IoT systems, including ZigBee, BLE, Wi-Fi, LoRaWAN, Sigfox, NB-IoT and LTE-M. The evaluation focuses on critical factors relevant to agricultural monitoring, such as communication range for remote fields, energy efficiency of battery-powered sensor nodes, latency for time-sensitive events like animal movement, data rate requirements for environment sensing and cost implication for large-scale deployments. Experimental analysis and simulation-based comparisons reveal that LoRaWAN and NB-IoT offer superior long-range performance and energy efficiency for applications such as soil moisture monitoring, climate sensing and farm intruder detection, while shortrange protocols like ZigBee and Wi-Fi are more suitable for controlled environments like greenhouses. The outcomes of this study provide practical insights for selecting optimal communication protocols tailored to diverse agricultural conditions, contributing to the development of robust, sustainable and scalable IoT solutions for precision farming.

Introduction

The text discusses the need for modernizing agriculture due to challenges like population growth, climate change, and resource scarcity, which make traditional farming methods inefficient. It introduces Smart Agriculture, which uses IoT, wireless sensor networks, and data-driven automation to monitor environmental and crop conditions such as soil moisture, temperature, humidity, and intrusion detection in real time. This helps improve productivity while reducing water, fertilizer use, and crop damage.

A key focus is the importance of choosing suitable wireless communication protocols for agricultural IoT systems. Since farms vary in size and conditions, communication technologies must balance long range, low power consumption, reliability, latency, and cost. The text reviews multiple protocols including Zigbee, BLE, Wi-Fi, LoRaWAN, Sigfox, NB-IoT, and LTE-M, highlighting that no single technology fits all agricultural needs.

The literature review shows that LPWAN technologies (especially LoRaWAN) are widely used for long-range, low-power sensing, while NB-IoT and LTE-M are useful where cellular coverage exists. Short-range technologies like Zigbee, BLE, and Wi-Fi are better suited for controlled environments like greenhouses. Many studies also emphasize hybrid systems, combining different protocols depending on application needs such as irrigation, climate monitoring, and animal intrusion detection.

The study identifies a research gap: there is no comprehensive, agriculture-specific comparison of communication protocols based on real performance metrics.

To address this, the research proposes a system architecture and methodology that includes real-world field testing and simulations. A smart agriculture system is built using sensors for soil, climate, and intrusion detection, connected to multiple communication modules. Data is transmitted to gateways and cloud platforms for analysis.

Performance is evaluated using metrics such as communication range, power consumption, latency, packet delivery ratio, cost, and overall suitability. Simulations are also conducted for different farm sizes and node densities to test scalability.

Conclusion

This study systematically evaluated multiple wireless communication protocols—LoRaWAN, NB-IoT, Zigbee, BLE, Wi-Fi, and Sigfox—based on their range, power consumption, latency, packet delivery ratio, cost, and overall suitability for smart agriculture applications. The experimental results clearly indicate that LoRaWAN offers the best overall performance for large-scale agricultural deployments due to its long communication range, low power profile, and stable PDR under varying weather conditions. NB-IoT demonstrates strong reliability and low latency, making it suitable for mission-critical scenarios but at the cost of higher power consumption and recurring network charges. Short-range protocols such as Zigbee and BLE show promise for greenhouse monitoring and localized node clusters but are unsuitable for large fields due to limited coverage. Wi-Fi, while offering high throughput, presents substantial energy demands and reduced outdoor reliability. The suitability index confirms that no single protocol excels in all metrics, and optimal selection depends heavily on field size, energy availability, sensor density, and desired data upload frequency. Overall, the results highlight that hybrid multi-protocol architectures may provide the most efficient solution for next-generation smart agriculture IoT systems.

References

[1] Ahmed, M., & Khan, S. (2023). Optimizing wireless sensor networks for livestock intrusion detection in agriculture fields. Information Processing in Agriculture, 10(3), 456–468. [2] Ahmed, M., Khan, S., & Raza, U. (2023). Optimizing wireless sensor networks for livestock intrusion detection in open fields. Information Processing in Agriculture, 10(3), 456–468. [3] Ali, M. U., Zia, T., & Iqbal, M. (2023). IoT-based precision agriculture using multi-protocol sensor networks. Journal of Network and Computer Applications, 222, 103621. [4] Awan, K. A., Ashraf, N., Imran, A., & Abbas, N. (2022). Performance evaluation of LPWAN technologies for smart agriculture applications. IEEE Access, 10, 45782–45795. [5] Badreddine Miles, E.-B. B. (2020). A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Computer Communications, 148–157. [6] Bicamumakuba, E., & López-Ortiz, F. (2025). IoT-enabled LoRaWAN gateway for monitoring and predicting microclimate in protected agriculture. Precision Agriculture (Early access). [7] Brinkhoff, J. &. (2017). Characterization of WiFi signal range for agricultural WSNs. 23rd Asia-Pacific Conference on Communications, IEEE. [8] Choudhary, V., et al. (2021). Multi-protocol IoT framework for soil, humidity, and temperature sensing in agriculture. Computer Networks, 193, 108081. [9] C.N. Verdouw, J. W. (2016). Virtualization of food supply chains with the Internet of Things. Journal of Food Engineering, 128–136. [10] Correia, F. P. (2023). LoRaWAN Gateway Placement in Smart Agriculture: An Analysis of Clustering Algorithms and Performance Metrics. Energies, 16(5), 2356. [11] Correia, F. P., Silva, J. M., & Santos, L. (2023). LoRaWAN gateway placement in smart agriculture: Coverage modelling and deployment heuristics. Energies, 16(5), 2356. [12] Da Costa, C. A., Pasluosta, C. F., & Farella, E. (2020). NB-IoT for remote environmental monitoring: Performance analysis. Sensors, 20(21), 6126. [13] Dabove, P., & Di Pietra, V. (2021). Sigfox technology for remote monitoring applications: Experimental evaluation. Sensors, 21(9), 3055. [14] Delwar, T., S.-w.-Y. (2025). Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection. Future Internet, 17(2), 70. [15] Diane, A. D. (2025). A systematic and comprehensive review on low power wide area network. Discover Internet of Things. [16] Ferreira, R., & Silva, P. (2025). Farm-Flow dataset: Intrusion detection and anomaly benchmarking for agricultural IoT. Computers & Security (in press). [17] Gatkal, N. R., N. S. (2024). Review of IoT and electronics enabled smart agriculture. International Journal of Agriculture and Biological Engineering, 17(5), 44–57. [18] Goyal, N. (2024). Animal Intrusion Detection and Alert System for Crop Protection. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 145–151. [19] Gule, P., & Raba, A. (2022). LoRa-based smart agriculture monitoring system: Design, deployment, and evaluation. Sensors, 22(18), 6754. [20] Jawad, H. M. (2017). Energy-efficient wireless sensor networks for precision agriculture: A review. Sensors, 17(8). [21] K. V. d. Oliveira, H. M. (2017). Wireless Sensor Network for Smart Agriculture using ZigBee Protocol. IEEE S3C, 61–66. [22] Klaina, H., & Zio, E. (2022). Analysis of LPWAN technologies for large-scale farm monitoring. Computer Networks, 201, 108656. [23] Klaina, H., Rault, T., & Kouiched, A. (2022). LPWANs for near-ground sensor networks in agriculture. Ad Hoc Networks, 127, 102794. [24] Kumar, R., Singh, J., & Sharma, A. (2023). A comparative study of wireless communication protocols for precision farming. Computers and Electronics in Agriculture, 205, 107634. [25] Li, H., & Zhou, K. (2023). Cost-performance analysis of LPWAN protocols for agricultural monitoring. Sensors, 23(4), 2156. [26] López-Ortiz, J., et al. (2020). Smart agriculture based on LoRaWAN: Protocol evaluation and deployment considerations. AIP Conference Proceedings, 2499, 090001. [27] Mahapatra, B., & Yun, S. (2024). Comparative analysis of BLE, Zigbee, and Wi-Fi for IoT farm systems. Ad Hoc Networks, 152, 103213. [28] Mamidi, K. K., Reddy, S., & Kumar, A. (2024). IoT-based animal detection using edge analytics. E3S Web of Conferences, 400, 01041. [29] Masud, M., et al. (2021). Energy-efficient data transmission for IoT-enabled agriculture systems. Future Generation Computer Systems, 125, 827–839. [30] Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2020). A comparative study of LPWAN technologies for IoT deployment. ICT Express, 6(2), 220–229. [31] Miles, B., Bourennane, E. B., & Chikhi, S. (2020). LoRaWAN protocol performance for IoT in agriculture. Computer Communications, 164, 148–157. [32] Quy, V. K. (2022). IoT-enabled smart agriculture: Architecture, applications and challenges. Applied Sciences, 12(7), 3396. [33] Rajak, P., Das, S., & Paul, S. (2023). IoT and smart sensors in agriculture: Research directions. Computers & Electronics in Agriculture, 206, 107697. [34] Rafi, M. S. M., et al. (2025). Reliable and cost-efficient IoT connectivity for smart agriculture. arXiv preprint. [35] Raza, S. W. (2017). A survey of LPWAN technologies for IoT. Journal of Network and Computer Applications, 78, 1–11. [36] Rehman, A., et al. (2022). Hybrid IoT architecture using LoRa and NB-IoT for smart farming. IEEE Sensors Journal, 22(18), 17821–17830. [37] Sharma, P., & Gupta, M. (2022). Review of wireless communication protocols for sustainable farming. Sustainable Computing, 35, 100794. [38] Singh, P., & Verma, R. (2024). Latency and PDR characterization of LPWAN protocols. IEEE Access, 12, 50332–50345. [39] Soussi, A., El-Khatib, K., & Hamouda, M. (2024). Smart sensors and data fusion in precision irrigation. Sensors, 24(3), 1458. [40] Sodhro, A. H., Zahid, N., Pirbhulal, S., & Wang, L. (2021). Energy-efficient LPWANs for IoT agriculture. IEEE Internet of Things Journal, 8(5), 3645–3654. [41] Su, T., Lin, Y., & Chen, H. (2024). LoRaWAN performance under extreme weather. IEEE Internet of Things Journal, 11(2), 1456–1467. [42] Sun Yilin, W. W. (2024). NB-IoT Technology for Agriculture. American Journal of Engineering Research, 13(8), 72–87. [43] Ting, Y.-T., & Nguyen, H. (2024). Optimising LoRa-based IoT solutions for farms. Computers & Electronics in Agriculture, 220, 107866. [44] Torres, J., & Molina, N. (2022). WSN reliability in outdoor agriculture. Journal of Communications and Networks, 24(6), 645–655. [45] Valecce, G. &. (2020). NB-IoT for Smart Agriculture: Experiments from the Field. CoDIT 2020, 71–75. [46] Wang, R., et al. (2021). Energy and latency trade-offs in IoT for precision farming. IEEE Transactions on Green Communications and Networking, 5(4), 2033–2045. [47] Yoon, D., & Park, J. (2025). NB-IoT vs 5G mMTC in rural farming. ICT Express, 11(1), 65–72.

Copyright

Copyright © 2026 Ms. Gayathri B, Mr. Harish M, Ms. Shaline S, Dr. Mary Tency E. L., Ms. Monicasri L. S., Ms. Rithika S.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.