AI-Optimized Energy-Harvesting Wireless Sensor Networks for Climate-Adaptive Smart Agriculture

Abstract

: Smart agriculture is becoming more reliant on intelligent sensing systems that are able to adjust to the increasing climatic conditions and at the same time have long term reliability of operation. Conventional Wireless Sensor Networks (WSNs) however, have significant limitations in terms of constant sampling time, battery limitation and lack of adaptability of the system to changes in the environment. This paper will propose a solution to these challenges in the form of an AI-Optimized Energy-Harvesting Wireless Sensor Network (EH-WSN) to be used in climate-adaptive smart agricultural systems. The framework is based on the combination of hybrid energy-harvesting solutions, including solar, RF, and vibration energy origins, and AI-driven energy prediction to attain energy-neutrality functionality in different field conditions. A climate-reactive sensing model dynamically changes the sampling rates, according to changing temperature, moisture content, and the possibility of rainfall, so that essential farming information is obtained in time. Also, a routing protocol based on AI optimizes the formation of clusters, the choice of paths, and the scheduling of deliveries, minimizing the costs of communication, and enhancing network tolerance. The simulation analysis and experimental results indicate a significant enhancement in the network lifetime, sensing and data reliability over the traditional WSN architectures. These adaptive mechanisms are able to balance the power consumption and the requirement of the environment and make the monitoring possible even under the conditions of changes in energy access and weather conditions. In general, the suggested system can be described as a scaleable and sustainable solution to precision agriculture that will help farmers to make better decisions and manage resources in the most efficient and climate-sensitive way possible. Such an AI-EH system will provide a solid basis of the next-generation autonomous agricultural monitoring system that can be successfully used in various environmental and geographic conditions.