Artificial intelligence-based cyber-physical systems (CPS) applications have been spread across various fields such as smart cities, medical services, and industrial controls. When CPS devices are connected to a cloud server, big data streams generated by CPS devices impose enormous bandwidth pressure and exert excessive compute loads to the cloud server. Due to unpredictable environments and uncertainty in reality, these issues are mainly attributed to a large amount of high noise data captured and uploaded by CPS devices. To overcome these issues, this paper proposes a cyber-physical-cloud based framework for distributed deep neural networks (DDNNs) to prevent high noise data from being uploaded to the cloud. The proposed framework features a lightweight data filtering module enabled by depthwise separable convolutions to identify and filter out the high noise data that the cloud cannot recognize. Extensive experimental results demonstrate that the proposed data filtering module can achieve an accuracy of up to 83.72% in identifying high noise data and the proposed framework can effectively save bandwidth of up to 63.42% as compared to benchmarking methods. Note to Practitioners—This paper is motivated by the problems of enormous bandwidth pressure and excessive cloud compute loads in cyber-physical-cloud distributed computing paradigms. These problems are mainly caused by high noise data generated by CPS devices, because CPS devices often work in disturbing and unstable environments and there are uncontrollable uncertainties in reality. Especially for the emerging artificial intelligence-driven cyber-physical-cloud distributed paradigms, there is no existing research to solve the unnecessary transmission and cloud compute loads caused by high noise data. To tackle the challenge, this paper develops a novel cyber-physical-cloud distributed framework with data filtering capabilities to prevent high noise data from being uploaded. The proposed framework supports two popular loosely coupled and closely coupled distributed computing paradigms. Extensive experiments confirm that the proposed cyber-physical-cloud distributed framework can efficiently filter out high noise data and alleviate unnecessary transmission and needless cloud compute loads introduced by high noise data.

中文翻译：

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过滤掉分布式深度神经网络的高噪声数据

基于人工智能的信息物理系统（CPS）应用已遍及智慧城市、医疗服务和工业控制等各个领域。当CPS设备连接到云服务器时，CPS设备产生的大数据流会给云服务器带来巨大的带宽压力和过大的计算负载。由于不可预测的环境和现实的不确定性，这些问题主要归因于CPS设备捕获和上传的大量高噪声数据。为了克服这些问题，本文提出了一种基于网络物理云的分布式深度神经网络 (DDNN) 框架，以防止高噪声数据上传到云端。所提出的框架具有一个轻量级数据过滤模块，该模块由深度可分离卷积启用，以识别和过滤掉云无法识别的高噪声数据。大量实验结果表明，与基准方法相比，所提出的数据过滤模块在识别高噪声数据方面的准确率高达 83.72%，所提出的框架可有效节省高达 63.42% 的带宽。从业者须知——这篇论文的动机是网络-物理-云分布式计算范式中巨大的带宽压力和过多的云计算负载问题。这些问题主要是由CPS设备产生的高噪声数据引起的，因为CPS设备经常工作在干扰和不稳定的环境中，现实中存在不可控的不确定性。特别是对于新兴的人工智能驱动的信息-物理-云分布式范式，目前还没有研究解决高噪声数据带来的不必要的传输和云计算负载。为了应对这一挑战，本文开发了一种具有数据过滤功能的新型网络-物理-云分布式框架，以防止上传高噪声数据。所提出的框架支持两种流行的松散耦合和紧密耦合的分布式计算范例。大量实验证实，所提出的信息物理云分布式框架可以有效地过滤掉高噪声数据，减轻高噪声数据引入的不必要的传输和不必要的云计算负载。目前还没有研究来解决高噪声数据带来的不必要的传输和云计算负载。为了应对这一挑战，本文开发了一种具有数据过滤功能的新型网络-物理-云分布式框架，以防止上传高噪声数据。所提出的框架支持两种流行的松散耦合和紧密耦合的分布式计算范例。大量实验证实，所提出的信息物理云分布式框架可以有效地过滤掉高噪声数据，减轻高噪声数据引入的不必要的传输和不必要的云计算负载。目前还没有研究来解决高噪声数据带来的不必要的传输和云计算负载。为了应对这一挑战，本文开发了一种具有数据过滤功能的新型网络-物理-云分布式框架，以防止上传高噪声数据。所提出的框架支持两种流行的松散耦合和紧密耦合的分布式计算范例。大量实验证实，所提出的信息物理云分布式框架可以有效地过滤掉高噪声数据，减轻高噪声数据引入的不必要的传输和不必要的云计算负载。本文开发了一种具有数据过滤功能的新型网络物理云分布式框架，以防止上传高噪声数据。所提出的框架支持两种流行的松散耦合和紧密耦合的分布式计算范例。大量实验证实，所提出的信息物理云分布式框架可以有效地过滤掉高噪声数据，减轻高噪声数据引入的不必要的传输和不必要的云计算负载。本文开发了一种具有数据过滤功能的新型网络物理云分布式框架，以防止上传高噪声数据。所提出的框架支持两种流行的松散耦合和紧密耦合的分布式计算范例。大量实验证实，所提出的信息物理云分布式框架可以有效地过滤掉高噪声数据，减轻高噪声数据引入的不必要的传输和不必要的云计算负载。