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.

翻译：无线传感器网络中的压缩数据聚合是任务特定的，且易受环境变化影响。然而，现有的压缩数据聚合框架（例如基于压缩感知的数据聚合、基于深度学习的数据聚合）不具备处理不同感知任务和环境变化所需的灵活性与适应性。此外，这些框架也未考虑后续物联网数据驱动型深度学习应用的表现。为解决这些不足，我们提出OrcoDCS——一种物联网-边缘协同的在线深度压缩感知框架，该框架对不同的物联网设备组及其感知任务具有高灵活性和适应性，并能提升后续应用的性能。本工作的创新之处在于，通过利用一种专门设计的不对称自编码器，在无线传感器网络上设计并部署了物联网-边缘协同的在线训练框架，该框架可大幅降低编码开销，并提升重构性能与鲁棒性。理论上和实证分析均表明，OrcoDCS在训练时间上优于最先进的DCDA方法，在面对不同重构任务时显著提升了灵活性与适应性，并为后续应用实现了更优的性能。