Smart agriculture resource allocation and energy optimization using bidirectional long short-term memory with ant colony optimization (Bi-LSTM–ACO) - M. Rathi; C. Gomathy (Frontiers in Communications and Networks)
IntroductionIn precision agriculture, Wireless Sensor Networks (WSNs) are essential for real-time monitoring and informed decision-making. Nevertheless, increased node density, constrained energy supplies, and unstable environmental circumstances present barriers to resource allocation and communication efficiency.MethodsTo address these limitations, a hybrid system combining deep learning and metaheuristic optimization was developed, integrating Bidirectional Long Short-Term Memory (Bi-LSTM) with Ant Colony Optimization (ACO). Real-time multivariate data, encompassing temperature, humidity, soil moisture, and power usage, were gathered utilizing a customized embedded sensing technology used in an agricultural environment. Z-score normalization was utilized for preprocessing, followed by Principal Component Analysis (PCA) for feature extraction and Particle Swarm Optimization (PSO) for the selection of appropriate feature subsets. The Bi-LSTM model was optimized using ACO to improve temporal learning and energy-efficient scheduling among sensor nodes.ResultsThe assessment of the proposed Bi-LSTM-ACO system resulted in an accuracy of 98.61%, precision of 92.16%, recall of 98.06%, and an F1-score of 91.41%, outperforming baseline models including LSTM, GRU, and CNN-LSTM.DiscussionThe findings indicate that the proposed framework significantly decreases energy consumption, enhances resource usage, and guarantees low-latency actuation in Agri-IoT implementations. The proposed work provides a scalable and intelligent system for real-time, energy-efficient agricultural monitoring.