{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:25:55Z","timestamp":1773246355099,"version":"3.50.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"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>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This work presents an adaptive ant colony (AdCO) framework for dynamic task management in heterogeneous Non-Terrestrial Network\u2013Internet of Things (NTN-IoT) systems integrating Unmanned Aerial Vehicles (UAVs) and Low Earth Orbit (LEO) satellites. The framework addresses key challenges such as stochastic mobility, intermittent connectivity, and latency-sensitive operations common in large-scale IoT deployments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed approach employs adaptive pheromone learning, heuristic control, and multi-timescale scheduling. It follows a hierarchical co-optimization strategy, where UAV swarms perform edge-side task allocation while LEO satellites handle relay scheduling during orbital passes. Event-triggered pheromone resets and distributionally robust cost modeling are introduced to maintain stability and adaptability under dynamic network conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Simulation results demonstrate superior performance compared to classical Ant Colony Optimization (ACO) and recent meta-heuristic methods. The proposed model achieves higher task completion ratios, reduced end-to-end latency, and enhanced energy-normalized throughput across different orbital configurations, traffic patterns, and link failures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings confirm the efficiency and resilience of the proposed framework in NTN-IoT operations. Its adaptability makes it suitable for critical applications such as disaster response, precision agriculture, and maritime monitoring, where real-time coordination and reliability are essential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frcmn.2025.1691346","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:50:24Z","timestamp":1761720624000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive ant colony methods for UAV LEO coordination in non terrestrial IoT"],"prefix":"10.3389","volume":"6","author":[{"given":"Hitesh","family":"Mohapatra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"4840","DOI":"10.1038\/s41598-025-86803-4","article-title":"Performance analysis of hybrid optimization approach for uav path planning control using fopid-tid controller and haoaroa algorithm","volume":"15","author":"Basil","year":"2025","journal-title":"Sci. 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