Energy-Efficient IoT Architectures for Smart Agriculture Applications

International Journal of Computer Technology and Electronics Communication 8 (1) (2025)
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Abstract

The Internet of Things (IoT) has emerged as a transformative technology in the field of smart agriculture, offering the potential to enhance productivity, sustainability, and efficiency. However, the widespread adoption of IoT in agriculture faces significant challenges, particularly in terms of energy consumption. IoT devices in agricultural environments, such as sensors, actuators, and communication modules, are often deployed in remote and large-scale areas, making energy efficiency a key concern. The energy consumption of these devices directly impacts the operational cost, scalability, and sustainability of IoT-based agricultural systems. This paper explores energy-efficient IoT architectures designed for smart agriculture applications. The focus is on IoT solutions that optimize energy usage while maintaining the performance of critical tasks like monitoring soil moisture, weather conditions, crop health, and irrigation. Various strategies for energy-efficient designs, including low-power communication protocols, energy harvesting techniques, and energy-aware routing algorithms, are discussed. Additionally, the paper evaluates state-of-the art architectures that incorporate energy-efficient components, such as low-power microcontrollers, battery management systems, and wireless sensor networks (WSNs).Furthermore, the paper provides an overview of existing smart agriculture applications, highlighting successful case studies where energy-efficient IoT systems have been implemented to enhance resource management in farming. The role of energy-efficient architectures in addressing challenges like scalability, maintenance, and long-term sustainability is also emphasized. Finally, the paper concludes with future directions for research in energy-efficient IoT systems for smart agriculture, including the integration of renewable energy sources and advancements in low-power machine learning algorithms for agricultural data analysis.

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