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

TL;DR

OrcoDCS is a flexible, adaptive IoT-Edge framework for online deep compressed sensing in wireless sensor networks, enhancing data reconstruction and application performance amid environmental changes.

Contribution

It introduces a novel IoT-Edge orchestrated online training framework using an asymmetric autoencoder for improved efficiency and robustness in WSN data aggregation.

Findings

Outperforms state-of-the-art DCDA in training time

Enhances flexibility and adaptability for various sensing tasks

Achieves higher performance for downstream applications

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.