OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

• URL: http://arxiv.org/abs/2308.05757v1
• Date: Sat, 5 Aug 2023 04:19:35 GMT
• Title: OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework
• Authors: Cheng-Wei Ching, Chirag Gupta, Zi Huang, Liting Hu
• Abstract summary: We propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework. OrcoDCS offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time.
• Score: 31.95604675656826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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