Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

Abstract

The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

Information Extraction and Data Planning of Smart City Based on Internet of Things

“…According to statistics in 2019, the total regional production The system displays the map of all streets and districts in the city, by dragging the mouse to browse the locationrelated content required by the user, and the black dots on the map are the monitoring distribution locations. If you need to check the monitoring situation of a certain location, you need to double-click the black dot at the location, that is, you can operate the monitoring equipment at this location, and you can also call the historical video of the monitoring of the location to realize the visual management of the smart city [24].…”

At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives

“…For efficient data generation and data privacy preservation for PM2.5 predictions, Putra et al in [ 21 ] proposed a federated compressed learning based on an edge computing framework for massive-scale WSNs. This approach used compressed sensing techniques at the sensor level to reduce network data traffic.…”

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“…Jiang et al [132] made an in-depth description on the feasibility of FL in smart city perception, and summarized the open problems and challenges in applications, so as to provide guidance for scholars on this topic. Putra et al [133] proposed federated compression learning, which ensures…”