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Data analyses using compressive sensing for internet of things (IoT) networks
Data analyses using compressive sensing for internet of things (IoT) networks
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机译:使用压缩感测技术进行物联网(IoT)网络的数据分析
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摘要
Internet of Things (IoT) devices (101A) continuously capture raw data over a regular interval of time. The captured raw data is transmitted to gateway devices (101B) deployed in an environment, for example, a warehouse. Continuous transmission of such data leads to data redundancy, continuous channel utilization and bandwidth usage, etc. To overcome this problem, present disclosure implements a Compressive Sensing based Data Prediction (CS-DP) model that predicts data at the gateway devices by learning the data pattern received from IoT devices, estimates and computes, using a Compressive Sensing based Data Estimation (CS-DE) model, optimal data instead of considering the overall data captured at the gateway devices and reconstructs, using a Compressive Sensing based Data Reconstruction (CS-DR) model, missing data and/or corrupted data using the partial information received at the gateway devices.
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