LEO satellite networks are emerging as a global-scale connectivity infrastructure for regions beyond the reach of terrestrial networks. Among them, satellite IoT targets low-power, low-cost IoT applications; however, our real-world measurements reveal that today's commercial satellite IoT still faces substantial challenges in supporting large-volume data transfer for real-world IoT applications. Our results show that a highly-compressed image of only tens of kilobytes typically takes 6–10 hours, extremely exceeding the application time requirements. We find that the bottleneck lies in the direct-to-satellite upload stage, where usable contacts are scarce and underutilized. Moreover, simply adding more nodes does not provide proportional gains, as beacon-triggered upload opportunities remain isolated and exhibit weak correlation. Based on this observation, we propose Co-DtS, a multi-interface upload system that promotes observed beacons into cross-interface coordination signals. Trace-driven evaluation with commercial devices shows that Co-DtS reduces image completion time from 7.62 hours to 0.9 hours.
Jinhong Liu, Ziyue Zhang, Xianjin Xia et al.· Conference on Applications,...· 0 citations
Direct-to-LEO Satellite (DtS) is widely touted as the path to global IoT connectivity, yet its real-world performance remains opaque. We present the first large-scale, in-the-wild measurement of DtS using one of the world's largest operational satellite IoT networks. Our findings overturn a popular belief: DtS capacity is not the pressing issue. Instead, DtS today is held back by low throughput, long-tail latency, and poor energy sustainability—problems that fundamentally limit practical adoption. We pinpoint the architectural and protocol-level causes behind these bottlenecks, revealing systemic inefficiencies across today's DtS designs. Guided by these insights, we redesign the DtS protocol with three drop-in enhancements: a NACK-driven reliability strategy that unlocks higher throughput, a flow-control mechanism that trims long-tail delays, and a fine-grained sleep management that cuts wasted energy. We validate the redesigned protocol through both testbed experiments and live production deployments, demonstrating 2.1× higher throughput, 52% fewer long-tail latencies, and 38% energy reduction.
Ziyue Zhang, Xianjin Xia, Ruonan Li et al.· Conference on Applications,...· 0 citations
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