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- W4315705825 abstract "Pervasive applications over large-scale, distributed embedded devices and the Internet of Things (IoT) demand precise coordination with the network; for example, several such applications, like collaborative video streaming and live analysis, augmented reality, etc., need continuous monitoring of network throughput and adapt the application behavior accordingly. Although the idea of network throughput prediction is not new and quite dated, in this paper, we show that the existing approaches fail to correctly infer the throughput when the network operator or the device change, and thus, not generic enough for Internet-scale applications. We propose ourmethod, a novel approach that allows collaborative training across different client hardware by capturing throughput variations based on devices' sensitivity towards the corresponding network configurations. Rigorous evaluations show that ourmethod{} outperforms various standard baseline algorithms with more than $80%$ R2-score over different datasets. We also analyze the performance of ourmethod{} over a network-aware streaming media application and demonstrate its efficacy for various application scenarios." @default.
- W4315705825 created "2023-01-12" @default.
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- W4315705825 date "2023-01-09" @default.
- W4315705825 modified "2023-09-28" @default.
- W4315705825 title "Revisiting Cellular Throughput Prediction over the Edge: Collaborative Multi-device, Multi-network in-situ Learning" @default.
- W4315705825 doi "https://doi.org/10.48550/arxiv.2301.03722" @default.
- W4315705825 hasPublicationYear "2023" @default.
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