OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing
  Framework
Cheng-Wei Ching Chirag Gupta Zi Huang Liting Hu
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.