<![CDATA[The deployment of wireless sensor networks (WSNs) in precision agriculture is essentially guarded by the energy limitations of sensor nodes, which can impede long-term, autonomous field monitoring. This paper introduces a hierarchical, cluster-based resource management system intended to prevail over these limitations. The central part of our approach is a dynamic clustering algorithm that intelligently groups sensor nodes to balance energy consumption and streamline data transmission across the network. Within each cluster, intra-cluster data aggregation is performed to fuse raw sensor data—encompassing critical parameters like soil temperature, humidity, and pH—thereby minimizing redundant packet transmissions. This aggregated data drives a predictive control model that automates decision-making for the precise actuation of irrigation and fertigation systems. Empirical validation demonstrates that our methodology achieves a dual objective: it significantly extends the network's operational lifespan by enhancing energy efficiency and throughput while reducing latency. Concurrently, this optimized resource allocation directly correlates with increased crop yield, presenting a robust and scalable framework for sustainable, high-efficiency agriculture, particularly in resource-intensive environments like urban and vertical farms.]]>
Copyrights © 2026
