OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework
OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework
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DOI：
10.1109/icdcsw60045.2023.00007
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发表时间：
2023-07
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影响因子：
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通讯作者：
Cheng-Wei Ching;Chirag Gupta;Zisen Huang;Liting Hu
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作者：
Cheng-Wei Ching;Chirag Gupta;Zisen Huang;Liting Hu
无线传感器网络（WSN）上的压缩数据聚合（CDA）是特定于任务的，并且会受到环境变化的影响。但是，现有的压缩数据聚合（CDA）框架（例如，基于压缩感应的数据聚合，基于深度学习（基于DL）的数据聚合）不具有处理不同的感应任务和环境变化所需的灵活性和适应性。此外，他们不考虑后续物联网数据驱动的深度学习（DL）的应用程序的性能。为了解决这些缺点，我们建议ORCODC，这是一个IoT精心策划的在线深层压缩传感框架，可为不同的IoT设备组及其传感任务以及随访应用程序提供高灵活性和适应性。我们作品的新颖性是通过利用专门设计的不对称自动编码器来设计和部署WSN的IoT Edge精心策划的在线培训框架，这可以在很大程度上降低编码的开销并改善重建性能和鲁棒性。我们从分析和经验上表明，ORCODC在训练时间上的表现优于最先进的DCDA，当给出不同的重建任务时，可以显着提高灵活性和适应性，并在随访应用程序中实现更高的性能。
Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.