Optimized resource management system for precision farming using cluster based wireless sensor network

Abstract

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.