{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:50:36Z","timestamp":1792252236198,"version":"4.4.0"},"reference-count":20,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize \u201cgood-put\u201d: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station\u2019s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems.<\/jats:p>","DOI":"10.3390\/s25247538","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T14:30:17Z","timestamp":1765463417000},"page":"7538","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Maximizing Nanosatellite Throughput via Dynamic Scheduling and Distributed Ground Stations"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1300-5236","authenticated-orcid":false,"given":"Rony","family":"Ronen","sequence":"first","affiliation":[{"name":"School of Computer Science, Ariel University, Ariel 4070000, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1580-5421","authenticated-orcid":false,"given":"Boaz","family":"Ben-Moshe","sequence":"additional","affiliation":[{"name":"School of Computer Science, Ariel University, Ariel 4070000, Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref_1","unstructured":"Croissant, K., White, D., \u00c1lvarez, X.C., Adamopoulos, V., Bassa, C., Boumghar, R., Brown, H., Damkalis, F., Daradimos, I., and Dohmen, P. 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