HOSNA: Boosting Smart Agriculture Efficiency With The Hybrid Optimization-Based Sensor Node Activation Model

Abstract

Smart agriculture leverages Wireless Sensor Networks (WSNs) to monitor environmental parameters such as soil moisture, temperature, and humidity, enabling precision farming and efficient resource utilization. The Hybrid Optimization-Based Sensor Node Activation (HOSNA) model designed to enhance the efficiency and lifespan of Wireless Sensor Networks (WSN) in smart agriculture applications. HOSNA integrates clustering, energy-efficient activation, hybrid optimization algorithms, and machine learning to optimize sensor node operations while ensuring accurate and real-time environmental monitoring. The model employs Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to determine optimal sensor activation schedules, reducing energy consumption and prolonging network lifetime. Additionally, a Long Short-Term Memory (LSTM) neural network predicts environmental changes, allowing proactive sensor activation. Simulation results demonstrate that HOSNA achieves a 94.0% data accuracy after 1000 operational rounds, surpassing LEACH (90.0%), PEGASIS (86.0%), and Random Duty Cycling (RDC) (70.0%). Energy consumption reduced by 24% compared to LEACH, while network lifetime extended by 32% over PEGASIS. These results highlight HOSNA’s ability to provide reliable, energy-efficient, and scalable solutions for precision agriculture. Future improvements could involve adapting the model for heterogeneous sensor networks and integrating solar-powered nodes for sustainable energy.