{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:31:59Z","timestamp":1774629119982,"version":"3.50.1"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Commun. Netw."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>In 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The 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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frcmn.2025.1587402","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T05:22:20Z","timestamp":1748928140000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Smart agriculture resource allocation and energy optimization using bidirectional long short-term memory with ant colony optimization (Bi-LSTM\u2013ACO)"],"prefix":"10.3389","volume":"6","author":[{"given":"M.","family":"Rathi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Gomathy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"11149","DOI":"10.1007\/s11227-023-05780-5","article-title":"Energy-efficient cluster head using modified fuzzy logic with WOA and path selection using enhanced CSO in IoT-enabled smart agriculture systems","volume":"80","author":"Chandrasekaran","year":"2024","journal-title":"J. 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