IoT-Based Monitoring System for Smart Agriculture to Enhance Crop Yield Efficiency
DOI:
Abstract
This study investigates an IoT architecture for smart agriculture that combines event-triggered sensing with edge-level multi-sensor fusion to reduce energy consumption across distributed sensor networks. While prior research has largely focused on optimizing individual node efficiency, our findings reveal that the primary source of energy savings arises from systemic behavioral changes within the network’s communication ecology. Real-world experiments on a multi-node deployment show that edge fusion reduces redundant transmissions, stabilizes medium-access contention, and significantly extends sleep intervals. Collectively, these effects produce an average 30% reduction in wake-up frequency, even in relay nodes that do not perform fusion. The results indicate that the underlying mechanism is ecological rather than local: by lowering network-wide communication turbulence, the system achieves a more stable, low-activity state, allowing overlapping dormancy clusters to form naturally. This challenges the long-standing assumption that energy efficiency must be pursued primarily at the node level. Limitations include the controlled experimental environment, moderate network scale, and potential latency risks in time-critical scenarios. The study’s theoretical contribution lies in reframing energy-efficient IoT design as a complex adaptive systems problem, where efficiency emerges from interactions across the network rather than isolated node behavior. This ecosystem-centric perspective opens new directions for sustainable IoT architectures. Future research should focus on designing protocols, topology strategies, and fusion mechanisms that deliberately shape systemic behavior in IoT networks, aiming to achieve greater efficiency, resilience, and longevity than node-centric approaches alone.
Downloads
Views : 32
Downloads : 23
References
S. Vijayakumar, V. Murugaiyan, S. Ilakkiya et al., “Opportunities, challenges, and interventions for agriculture 4.0 adoption,” Discov. Food, vol. 5, p. 265, 2025.
M. M. H. Sajib and A. S. M. Sayem, “Innovations in sensor-based systems and sustainable energy solutions for smart agriculture: A review,” Encyclopedia, vol. 5, no. 2, 2025.
D. Muhammed, E. Ahvar, S. Ahvar, M. Trocan, M.-J. Montpetit, and R. Ehsani, “Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions,” J. Netw. Comput. Appl., vol. 228, Aug. 2024.
D. D. Olatinwo, H. C. Myburgh, A. De Freitas, and A. M. Abu-Mahfouz, “A review of smart crop technologies for resource constrained environments: Leveraging multimodal data fusion, edge-to-cloud computing, and IoT virtualization,” J. Sens. Actuator Netw., vol. 14, no. 5, 2025.
R. Priyadarshi, “Efficient node deployment for enhancing coverage and connectivity in Wireless Sensor Networks,” Sci. Rep., vol. 15, p. 29052, 2025.
C. Shivakeshi and B. Sreepathi, “Software defined network based enhanced energy-aware load balancing routing protocol,” Electric Power Components Syst., pp. 1–17, 2024.
A. S. Rathor, S. Choudhury, A. Sharma, P. Nautiyal, and G. Shah, “Empowering vertical farming through IoT and AI-driven technologies: A comprehensive review,” Heliyon, vol. 10, no. 15, Aug. 15, 2024.
S. Balamurali, M. Kathirvelu, S. K. Palanisamy, and I. H. Jaghdam, “Redefining IoT networks for improving energy and memory efficiency through compressive sensing paradigm,” Sci. Rep., vol. 15, Art. no. 27180, 2025.
S. Hudda and K. Haribabu, “A review on WSN based resource constrained smart IoT systems,” Discov. Internet Things, vol. 5, p. 56, 2025.
A. M. S. Saleh, B. M. Ali, M. F. A. Rasid, and A. Ismail, “A self-optimizing scheme for energy balanced routing in wireless sensor networks using SensorAnt,” Sensors, vol. 12, no. 8, pp. 11307–11333, 2012.
A. Choudhary, “Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions,” Discov. Internet Things, vol. 4, p. 31, 2024.
M. Hajder, J. Kolbusz, and M. Liput, “An AI-based integrated multi-sensor system with edge computing for the adaptive management of human–wildlife conflict,” Sensors, vol. 25, no. 20, 2025.
I. Ullah, D. Adhikari, X. Su, F. Palmieri, C. Wu, and C. Choi, “Integration of data science with the intelligent IoT (IIoT): Current challenges and future perspectives,” Digit. Commun. Netw., vol. 11, no. 2, pp. 280–298, Apr. 2025.
M. Poyyamozhi, B. Murugesan, N. Rajamanickam, M. Shorfuzzaman, and Y. Aboelmagd, “IoT—A promising solution to energy management in smart buildings: A systematic review, applications, barriers, and future scope,” Buildings, vol. 14, no. 11, Art. no. 3446, 2024.
S. Javaid, H. Fahim, S. Zeadally, and B. He, “From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs,” Digit. Commun. Netw., vol. 11, no. 4, pp. 937–960, Aug. 2025.
G. Routis and I. Roussaki, “Low power IoT electronics in precision irrigation,” Smart Agric. Technol., vol. 5, Oct. 2023.
Y. Zhang, G. Wang, L. Li, and M. Huang, “A monitoring method for agricultural soil moisture using wireless sensors and the Biswas model,” Agriculture, vol. 15, no. 3, Art. no. 344, 2025.
V. Masalskyi, D. Čičiurėnas, A. Dzedzickis, U. Prentice, G. Braziulis, and V. Bučinskas, “Synchronization of separate sensors’ data transferred through a local Wi-Fi network: A use case of human-gait monitoring,” Future Internet, vol. 16, no. 2, Art. no. 36, 2024.
F. Barchi, E. Parisi, L. Zanatta et al., “Energy efficient and low-latency spiking neural networks on embedded microcontrollers through spiking activity tuning,” Neural Comput. Appl., vol. 36, pp. 18897–18917, 2024.
A. S. Rathor, S. Choudhury, A. Sharma, P. Nautiyal, and G. Shah, “Empowering vertical farming through IoT and AI-driven technologies: A comprehensive review,” Heliyon, vol. 10, no. 15, Aug. 15, 2024.
L. Liu, W. Cheng, and H.-W. Kuo, “A narrative review on smart sensors and IoT solutions for sustainable agriculture and aquaculture practices,” Sustainability, vol. 17, no. 12, 2025.
M. Boumaiz, M. El Ghazi, A. Bouayad, Y. Balboul, and M. El Bekkali, “Energy-efficient strategies in wireless body area networks: A comprehensive survey,” IoT, vol. 6, no. 3, Art. no. 49, 2025.
A. Ali, H. Shaukat, H. Elahi, S. Taimur, M. Q. Manan, W. A. Altabey, S. A. Kouritem, and M. Noori, “Advancements in energy harvesting techniques for sustainable IoT devices,” Results Eng., vol. 26, Jun. 2025.
A. Ojha and B. Gupta, “Evolving landscape of wireless sensor networks: A survey of trends, timelines, and future perspectives,” Discov. Appl. Sci., vol. 7, p. 825, 2025.
A. Khalifeh, A. Al-Qaisi, K. Aldahdouh, W. T. Al-Sit, A. Al Olaimat, S. Alouneh, and K. A. Darabkh, “Low power wide area network (LPWAN) protocols: Enablers for future wireless networks,” Results Eng., vol. 27, Sep. 2025.
K. Banti, I. Karampelia, T. Dimakis, A.-A. A. Boulogeorgos, T. Kyriakidis, and M. Louta, “LoRaWAN communication protocols: A comprehensive survey under an energy efficiency perspective,” Telecom, vol. 3, no. 2, pp. 322–357, 2022.
S. Heine, C. A. Hofmann, and A. Knopp, “Energy-aware protocol design and evaluation of the PHY layer in satellite IoT,” Int. J. Satell. Commun. Netw., vol. 43, no. 2, pp. 61–76, 2025.
S. De, H. M. Jalajamony, S. Adhinarayanan, S. Joshi, H. Upadhyay, and R. Fernandez, “Multimedia transmission over LoRa networks for IoT applications: A survey of strategies, deployments, and open challenges,” Sensors, vol. 25, no. 23, 2025.
D. Borsos, “Image transmission with LoRaWAN in agriculture,” in Critical Infrastructure Protection in the Light of the Armed Conflicts, T. A. Kovács, Z. Nyikes, T. Berek, N. Daruka, and L. Tóth, Eds., Cham, Switzerland: Springer Nature, 2024.
M. Q. Alazzawi, J.-C. Sánchez-Aarnoutse, A. S. Martínez-Sala, and M.-D. Cano, “Green IoT: Energy efficiency, renewable integration, and security implications,” IET Netw., vol. 14, no. 1, Art. no. e70003, 2025.
S. Anbazhagan and R. K. Mugelan, “Adaptive power-saving mode control in NB-IoT networks using soft actor-critic reinforcement learning for optimal power management,” Sci. Rep., vol. 15, p. 34618, 2025.
T. Zhang, B. Huang, X. Liu, J. Fan, J. Li, Z. Yue, and Y. Wang, “MCU intelligent upgrades: An overview of AI-enabled low-power technologies,” J. Low Power Electron. Appl., vol. 15, no. 4, 2025.
P. F. Karjou, S. K. Saryazdi, P. Stoffel, and D. Müller, “Practical design and implementation of IoT-based occupancy monitoring systems for office buildings: A case study,” Energy Build., vol. 323, Nov. 15, 2024.
K. Alahmadi, S. Alharbi, J. Chen et al., “Generalizing sentiment analysis: a review of progress, challenges, and emerging directions,” Soc. Netw. Anal. Min., vol. 15, p. 45, 2025.
S. Rabah, A. Zaier, J. Lloret, and H. Dahman, “Efficiency enhancement of a hybrid sustainable energy harvesting system using HHHOPSO-MPPT for IoT devices,” Sustainability, vol. 15, no. 13, 2023.
N. A.-H. S. Abd and A. S. Daghal, “An energy-efficient scheduling and routing protocol based on Q-learning for WSN,” Asian J. Comput. Eng. Technol., vol. 6, no. 1, pp. 48–62, 2025.
M. Shili, H. Chaoui, and K. Nouri, “Energy-aware sensor fusion architecture for autonomous channel robot navigation in constrained environments,” Sensors, vol. 25, no. 21, 2025.
G. G. Njema, R. B. O. Ouma, and J. K. Kibet, “A review on the recent advances in battery development and energy storage technologies,” J. Renew. Energy, vol. 2024.
